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Wan X, Lu X, Zhu L, Feng J. Relative prevalence of top-down versus bottom-up control in planktonic ecosystem under eutrophication and climate change: A comparative study of typical bay and estuary. Water Res 2024; 255:121487. [PMID: 38518414 DOI: 10.1016/j.watres.2024.121487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/26/2024] [Accepted: 03/18/2024] [Indexed: 03/24/2024]
Abstract
Eutrophication and climate change may affect the top-down versus bottom-up controls in aquatic ecosystems. However, the relative prevalence of the two controls in planktonic ecosystems along the eutrophication and climate gradients has rarely been addressed. Here, using the field surveys of 17 years in a typical bay and estuary, we test two opposite patterns of trophic control dominance and their response to regional temporal eutrophication and climate fluctuations. It was found that trophic control of planktonic ecosystems fluctuated between the dominance of top-down and bottom-up controls on time scales in both the bay and estuary studied. The relative prevalence of these two controls in both ecosystems was significantly driven directly by regional dissolved inorganic nitrogen but, for the estuary, also by the nonlinear effects of regional sea surface temperature. In terms of indirect pathways, community relationships (synchrony and grazing pressure) in the bay are driven by both regional dissolved inorganic nitrogen - soluble reactive phosphorus ratio and sea surface temperature, but this drive did not continue to be transmitted to the trophic control. Conversely, trophic control in estuary was directly related to grazing pressure and indirectly related to synchrony. These findings support the view that eutrophication and climate drive the relative prevalence of top-down versus bottom-up controls at ecosystem and temporal scales in planktonic ecosystems, which has important implications for predicting the potential impacts of anthropogenic and environmental perturbations on the structure and function of marine ecosystems.
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Affiliation(s)
- Xuhao Wan
- College of Environmental Science and Engineering, Nankai University, Tianjin, PR China
| | - Xueqiang Lu
- College of Environmental Science and Engineering, Nankai University, Tianjin, PR China
| | - Lin Zhu
- College of Environmental Science and Engineering, Nankai University, Tianjin, PR China
| | - Jianfeng Feng
- College of Environmental Science and Engineering, Nankai University, Tianjin, PR China.
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2
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Wang Q, Wang R, Wang X, Fu M, Gao Y, Feng J, Geng R, Yuan Z, Fan Q, Lu F. An NIR-II-emitting type-I photosensitizer for efficient hypoxic tumor phototheranostics. Chem Commun (Camb) 2024; 60:5322-5325. [PMID: 38666540 DOI: 10.1039/d4cc00431k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2024]
Abstract
A small molecule-based NIR-II type-I photosensitizer (IT-IC) with a strong push-pull effect and good planar π-conjugated structure was synthesized. The IT-IC NPs exhibited strong light absorption, outstanding NIR-II fluorescence emission, excellent photothermal conversion and efficient type-I/II ROS generation, showing encouraging therapeutic outcomes for hypoxic tumors.
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Affiliation(s)
- Qi Wang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing 210023, China.
| | - Ruoqing Wang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing 210023, China.
| | - Xiaoyuan Wang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing 210023, China.
| | - Mingxuan Fu
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing 210023, China.
| | - Yicong Gao
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing 210023, China.
| | - Jianfeng Feng
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing 210023, China.
| | - Renyong Geng
- School of Chemistry and Materials Engineering, Fuyang Normal University, Fuyang 236037, China.
| | - Zhen Yuan
- Faculty of Health Sciences and Center for Cognitive and Brain Sciences, University of Macau, Macau 999078, China
| | - Quli Fan
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing 210023, China.
| | - Feng Lu
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing 210023, China.
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3
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Lett TA, Vaidya N, Jia T, Polemiti E, Banaschewski T, Bokde ALW, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brüh R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Lemaitre H, Paus T, Poustka L, Stringaris A, Waller L, Zhang Z, Robinson L, Winterer J, Zhang Y, King S, Smolka MN, Whelan R, Schmidt U, Sinclair J, Walter H, Feng J, Robbins TW, Desrivières S, Marquand A, Schumann G. A framework for a brain-derived nosology of psychiatric disorders. medRxiv 2024:2024.05.07.24306980. [PMID: 38766134 PMCID: PMC11100856 DOI: 10.1101/2024.05.07.24306980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Current psychiatric diagnoses are not defined by neurobiological measures which hinders the development of therapies targeting mechanisms underlying mental illness 1,2 . Research confined to diagnostic boundaries yields heterogeneous biological results, whereas transdiagnostic studies often investigate individual symptoms in isolation. There is currently no paradigm available to comprehensively investigate the relationship between different clinical symptoms, individual disorders, and the underlying neurobiological mechanisms. Here, we propose a framework that groups clinical symptoms derived from ICD-10/DSM-V according to shared brain mechanisms defined by brain structure, function, and connectivity. The reassembly of existing ICD-10/DSM-5 symptoms reveal six cross-diagnostic psychopathology scores related to mania symptoms, depressive symptoms, anxiety symptoms, stress symptoms, eating pathology, and fear symptoms. They were consistently associated with multimodal neuroimaging components in the training sample of young adults aged 23, the independent test sample aged 23, participants aged 14 and 19 years, and in psychiatric patients. The identification of symptom groups of mental illness robustly defined by precisely characterized brain mechanisms enables the development of a psychiatric nosology based upon quantifiable neurobiological measures. As the identified symptom groups align well with existing diagnostic categories, our framework is directly applicable to clinical research and patient care.
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Feng J, Jirsa V, Lu W. Human brain computing and brain-inspired intelligence. Natl Sci Rev 2024; 11:nwae144. [PMID: 38742233 PMCID: PMC11089814 DOI: 10.1093/nsr/nwae144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 04/10/2024] [Indexed: 05/16/2024] Open
Affiliation(s)
- Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
- Shanghai Center for Mathematical Sciences, Fudan University, China
- Department of Computer Science, University of Warwick, UK
- Zhangjiang Fudan International Innovation Center, China
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Faculté de Medécine, Aix Marseille Université, France
| | - Wenlian Lu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
- Shanghai Center for Mathematical Sciences, Fudan University, China
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Zhou C, You J, Guan X, Guo T, Wu J, Wu H, Wu C, Chen J, Wen J, Tan S, Duanmu X, Qin J, Huang P, Zhang B, Cheng W, Feng J, Xu X, Wang L, Zhang M. Microstructural alterations of the hypothalamus in Parkinson's disease and probable REM sleep behavior disorder. Neurobiol Dis 2024; 194:106472. [PMID: 38479482 DOI: 10.1016/j.nbd.2024.106472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/24/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND Whether there is hypothalamic degeneration in Parkinson's disease (PD) and its association with clinical symptoms and pathophysiological changes remains controversial. OBJECTIVES We aimed to quantify microstructural changes in hypothalamus using a novel deep learning-based tool in patients with PD and those with probable rapid-eye-movement sleep behavior disorder (pRBD). We further assessed whether these microstructural changes associated with clinical symptoms and free thyroxine (FT4) levels. METHODS This study included 186 PD, 67 pRBD, and 179 healthy controls. Multi-shell diffusion MRI were scanned and mean kurtosis (MK) in hypothalamic subunits were calculated. Participants were assessed using Unified Parkinson's Disease Rating Scale (UPDRS), RBD Questionnaire-Hong Kong (RBDQ-HK), Hamilton Depression Rating Scale (HAMD), and Activity of Daily Living (ADL) Scale. Additionally, a subgroup of PD (n = 31) underwent assessment of FT4. RESULTS PD showed significant decreases of MK in anterior-superior (a-sHyp), anterior-inferior (a-iHyp), superior tubular (supTub), and inferior tubular hypothalamus when compared with healthy controls. Similarly, pRBD exhibited decreases of MK in a-iHyp and supTub. In PD group, MK in above four subunits were significantly correlated with UPDRS-I, HAMD, and ADL. Moreover, MK in a-iHyp and a-sHyp were significantly correlated with FT4 level. In pRBD group, correlations were observed between MK in a-iHyp and UPDRS-I. CONCLUSIONS Our study reveals that microstructural changes in the hypothalamus are already significant at the early neurodegenerative stage. These changes are associated with emotional alterations, daily activity levels, and thyroid hormone levels.
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Affiliation(s)
- Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Jia You
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433 Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433 Shanghai, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, 200433 Shanghai, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Haoting Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Chenqing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Jingwen Chen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Jiaqi Wen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Sijia Tan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Xiaojie Duanmu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Jianmei Qin
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433 Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433 Shanghai, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, 200433 Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433 Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433 Shanghai, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, 200433 Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China.
| | - Linbo Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433 Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433 Shanghai, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, 200433 Shanghai, China.
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China; Joint Laboratory of Clinical Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China.
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6
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Chen C, He W, Ni Z, Zhang X, Cui Y, Song X, Feng J. Bioaccumulation, trophic transfer and risk assessment of polycyclic musk in marine food webs of the Bohai Sea. Mar Pollut Bull 2024; 202:116353. [PMID: 38598929 DOI: 10.1016/j.marpolbul.2024.116353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/18/2024] [Accepted: 04/05/2024] [Indexed: 04/12/2024]
Abstract
Galaxolide (HHCB) and tonalide (AHTN) are dominant musks added to personal care products. However, the accumulate and trophic transfer of SMs through the marine food chain are unclear. In this study, organisms were collected from three bays in Bohai Sea to investigate the bioaccumulation, trophic transfer, and health risk of SMs. The HHCB and AHTN concentrations in the muscles range from 2.75 to 365.40 μg/g lw and 1.04-4.94 μg/g lw, respectively. The median HHCB concentrations in muscles were the highest in Bohai Bay, followed by Laizhou Bay and Liaodong Bay, consistent with the HHCB concentrations in sediments. The different fish tissues from Bohai Bay were analyzed, and the HHCB and AHTN concentrations followed the heart > liver > gill > muscles. The trophic magnification factors (TMF) were lower than 1 and the health risk assessment showed no adverse health effects. The results provide insights into the bioaccumulation and trophic transfer behavior of SMs in marine environments.
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Affiliation(s)
- Cuihong Chen
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Engineering Center of Environmental Diagnosis and Contamination Remediation, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Wanyu He
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Engineering Center of Environmental Diagnosis and Contamination Remediation, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Zhenyang Ni
- School of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou, China
| | - Xiaohui Zhang
- Engineering Research Center of Coal-based Ecological Carbon Sequestration Technology of the Ministry of Education, Shanxi Datong University, 037009, China
| | - Yuxiao Cui
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Engineering Center of Environmental Diagnosis and Contamination Remediation, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Xiaojing Song
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Engineering Center of Environmental Diagnosis and Contamination Remediation, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Jianfeng Feng
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Engineering Center of Environmental Diagnosis and Contamination Remediation, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
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7
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Duan H, Shi R, Kang J, Banaschewski T, Bokde ALW, Büchel C, Desrivières S, Flor H, Grigis A, Garavan H, Gowland PA, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Paus T, Poustka L, Hohmann S, Holz N, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Lin X, Feng J. Population clustering of structural brain aging and its association with brain development. medRxiv 2024:2024.01.09.24301030. [PMID: 38260410 PMCID: PMC10802651 DOI: 10.1101/2024.01.09.24301030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Structural brain aging has demonstrated strong inter-individual heterogeneity and mirroring patterns with brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, most of the existing research focused on the cross-sectional changes of brain aging. In this investigation, we present a data-driven approach that incorporate both cross-sectional changes and longitudinal trajectories of structural brain aging and identified two brain aging patterns among 37,013 healthy participants from UK Biobank. Participants with accelerated brain aging also demonstrated accelerated biological aging, cognitive decline and increased genetic susceptibilities to major neuropsychiatric disorders. Further, by integrating longitudinal neuroimaging studies from a multi-center adolescent cohort, we validated the "last in, first out" mirroring hypothesis and identified brain regions with manifested mirroring patterns between brain aging and brain development. Genomic analyses revealed risk loci and genes contributing to accelerated brain aging and delayed brain development, providing molecular basis for elucidating the biological mechanisms underlying brain aging and related disorders.
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Affiliation(s)
- Haojing Duan
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Runye Shi
- School of Data Science, Fudan University, Shanghai, China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Arun L. W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | | | - Sylvane Desrivières
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, Vermont, USA
| | - Penny A. Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, corporate Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 “Trajectoires développementales en psychiatrie”; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli; Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherce Médicale, INSERM U A10 “Trajectoires développementales & psychiatrie”, University Paris-Saclay, Ecole Normale Supérieure Paris-Saclay, CNRS; Centre Borelli, Gif-sur-Yvette, France; and AP-HP. Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 “Trajectoires développementalesen psychiatrie”; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette; and Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein Kiel University, Kiel, Germany
| | | | - Tomáš Paus
- Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Nathalie Holz
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Juliane H. Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N. Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Nilakshi Vaidya
- Department of Psychiatry and Neurosciences, Charité–Universitätsmedizin Berlin, corporate member of Freie Universität BerlinHumboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, corporate Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Gunter Schumann
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Psychiatry and Neurosciences, Charité–Universitätsmedizin Berlin, corporate member of Freie Universität BerlinHumboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Centre for Population Neuroscience and Stratified Medicine (PONS Centre), ISTBI, Fudan University, Shanghai, China
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Germany
| | - Xiaolei Lin
- School of Data Science, Fudan University, Shanghai, China
- Huashan Institute of Medicine, Huashan Hospital affiliated to Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
- School of Data Science, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
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8
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He XY, Wu BS, Yang L, Guo Y, Deng YT, Li ZY, Fei CJ, Liu WS, Ge YJ, Kang J, Feng J, Cheng W, Dong Q, Yu JT. Genetic associations of protein-coding variants in venous thromboembolism. Nat Commun 2024; 15:2819. [PMID: 38561338 PMCID: PMC10984941 DOI: 10.1038/s41467-024-47178-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
Previous genetic studies of venous thromboembolism (VTE) have been largely limited to common variants, leaving the genetic determinants relatively incomplete. We performed an exome-wide association study of VTE among 14,723 cases and 334,315 controls. Fourteen known and four novel genes (SRSF6, PHPT1, CGN, and MAP3K2) were identified through protein-coding variants, with broad replication in the FinnGen cohort. Most genes we discovered exhibited the potential to predict future VTE events in longitudinal analysis. Notably, we provide evidence for the additive contribution of rare coding variants to known genome-wide polygenic risk in shaping VTE risk. The identified genes were enriched in pathways affecting coagulation and platelet activation, along with liver-specific expression. The pleiotropic effects of these genes indicated the potential involvement of coagulation factors, blood cell traits, liver function, and immunometabolic processes in VTE pathogenesis. In conclusion, our study unveils the valuable contribution of protein-coding variants in VTE etiology and sheds new light on its risk stratification.
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Affiliation(s)
- Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Liu Yang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu Guo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ze-Yu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Chen-Jie Fei
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei-Shi Liu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, UK.
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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9
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Zeng L, Feng J, Lu W. A general description of criticality in neural network models. Heliyon 2024; 10:e27183. [PMID: 38562505 PMCID: PMC10982970 DOI: 10.1016/j.heliyon.2024.e27183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Recent experimental observations have supported the hypothesis that the cerebral cortex operates in a dynamical regime near criticality, where the neuronal network exhibits a mixture of ordered and disordered patterns. However, A comprehensive study of how criticality emerges and how to reproduce it is still lacking. In this study, we investigate coupled networks with conductance-based neurons and illustrate the co-existence of different spiking patterns, including asynchronous irregular (AI) firing and synchronous regular (SR) state, along with a scale-invariant neuronal avalanche phenomenon (criticality). We show that fast-acting synaptic coupling can evoke neuronal avalanches in the mean-dominated regime but has little effect in the fluctuation-dominated regime. In a narrow region of parameter space, the network exhibits avalanche dynamics with power-law avalanche size and duration distributions. We conclude that three stages which may be responsible for reproducing the synchronized bursting: mean-dominated subthreshold dynamics, fast-initiating a spike event, and time-delayed inhibitory cancellation. Remarkably, we illustrate the mechanisms underlying critical avalanches in the presence of noise, which can be explained as a stochastic crossing state around the Hopf bifurcation under the mean-dominated regime. Moreover, we apply the ensemble Kalman filter to determine and track effective connections for the neuronal network. The method is validated on noisy synthetic BOLD signals and could exactly reproduce the corresponding critical network activity. Our results provide a special perspective to understand and model the criticality, which can be useful for large-scale modeling and computation of brain dynamics.
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Affiliation(s)
- Longbin Zeng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and BrainInspired Intelligence (Fudan University), Ministry of Education, China
| | - Wenlian Lu
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
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10
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Chen D, Jia T, Cheng W, Desrivières S, Heinz A, Schumann G, Feng J. Evaluation of behavioral variance/covariance explained by the neuroimaging data through a pattern-based regression. Hum Brain Mapp 2024; 45:e26601. [PMID: 38488475 PMCID: PMC10941514 DOI: 10.1002/hbm.26601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 01/01/2024] [Accepted: 01/08/2024] [Indexed: 03/18/2024] Open
Abstract
Neuroimaging data have been widely used to understand the neural bases of human behaviors. However, most studies were either based on a few predefined regions of interest or only able to reveal limited vital regions, hence not providing an overarching description of the relationship between neuroimaging and behaviors. Here, we proposed a voxel-based pattern regression that not only could investigate the overall brain-associated variance (BAV) for a given behavioral measure but could also evaluate the shared neural bases between different behaviors across multiple neuroimaging data. The proposed method demonstrated consistently high reliability and accuracy through comprehensive simulations. We further implemented this approach on real data of adolescents (IMAGEN project, n = 2089) and adults (HCP project, n = 808) to investigate brain-based variances of multiple behavioral measures, for instance, cognitive behaviors, substance use, and psychiatric disorders. Notably, intelligence-related scores showed similar high BAVs with the gray matter volume across both datasets. Further, our approach allows us to reveal the latent brain-based correlation across multiple behavioral measures, which are challenging to obtain otherwise. For instance, we observed a shared brain architecture underlying depression and externalizing problems in adolescents, while the symptom comorbidity may only emerge later in adults. Overall, our approach will provide an important statistical tool for understanding human behaviors using neuroimaging data.
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Affiliation(s)
- Di Chen
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence (Fudan University)Ministry of EducationShanghaiChina
| | - Tianye Jia
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence (Fudan University)Ministry of EducationShanghaiChina
- Institute of Psychiatry, Psychology & NeuroscienceSGDP Centre, King's College LondonLondonUK
| | - Wei Cheng
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence (Fudan University)Ministry of EducationShanghaiChina
| | - Sylvane Desrivières
- Institute of Psychiatry, Psychology & NeuroscienceSGDP Centre, King's College LondonLondonUK
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCMCharité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt‐Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
| | - Gunter Schumann
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
- Department of Psychiatry and Psychotherapy CCMCharité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt‐Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
| | - Jianfeng Feng
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence (Fudan University)Ministry of EducationShanghaiChina
- Department of Computer ScienceUniversity of WarwickCoventryUK
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11
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Zhang W, Chen B, Feng J, Lu W. On a framework of data assimilation for hyperparameter estimation of spiking neuronal networks. Neural Netw 2024; 171:293-307. [PMID: 37973499 DOI: 10.1016/j.neunet.2023.11.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/20/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023]
Abstract
When handling real-world data modeled by a complex network dynamical system, the number of the parameters is often much more than the size of the data. Therefore, in many cases, it is impossible to estimate these parameters and the exact value of each parameter is frequently less interesting than the distribution of the parameters. In this paper, we aim to estimate the distribution of the parameters in the mesoscopic neuronal network model from the macroscopic experimental data, for example, the BOLD (blood oxygen level dependent) signal. Herein, we assume that the parameters of the neurons and synapses are inhomogeneous but independently and identically distributed from certain distributions with unknown hyperparameters. Thus, we estimate these hyperparameters of the distributions of the parameters, instead of estimating the parameters themselves. We formulate this problem under the framework of data assimilation and hierarchical Bayesian method and present an efficient method named Hierarchical Data Assimilation (HDA) to conduct the statistical inference on the neuronal network model with the BOLD signal data simulated by the hemodynamic model. We consider the Leaky Integral-Fire (LIF) neuronal networks with four synapses and show that the proposed algorithm can estimate the BOLD signals and the hyperparameters with high preciseness. In addition, we discuss the influence on the performance of the algorithm configuration and the LIF network model setup.
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Affiliation(s)
- Wenyong Zhang
- School of Mathematical Sciences, Fudan University, No. 220 Handan Road, Shanghai, 200433, Shanghai, China
| | - Boyu Chen
- School of Mathematical Sciences, Fudan University, No. 220 Handan Road, Shanghai, 200433, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, No. 220 Handan Road, Shanghai, 200433, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, No. 220 Handan Road, Shanghai, 200433, Shanghai, China
| | - Wenlian Lu
- School of Mathematical Sciences, Fudan University, No. 220 Handan Road, Shanghai, 200433, Shanghai, China; Shanghai Center for Mathematical Sciences, No. 220 Handan Road, Shanghai, 200433, Shanghai, China; Shanghai Key Laboratory for Contemporary Applied Mathematics, No. 220 Handan Road, Shanghai, 200433, Shanghai, China; Key Laboratory of Mathematics for Nonlinear Science, No. 220 Handan Road, Shanghai, 200433, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, No. 220 Handan Road, Shanghai, 200433, Shanghai, China.
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12
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Yang L, Zeng J, Gao N, Zhu L, Feng J. Predicting the Metal Mixture Toxicity with a Toxicokinetic-Toxicodynamic Model Considering the Time-Dependent Adverse Outcome Pathways. Environ Sci Technol 2024; 58:3714-3725. [PMID: 38350648 DOI: 10.1021/acs.est.3c09857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Chemicals mainly exist in ecosystems as mixtures, and understanding and predicting their effects are major challenges in ecotoxicology. While the adverse outcome pathway (AOP) and toxicokinetic-toxicodynamic (TK-TD) models show promise as mechanistic approaches in chemical risk assessment, there is still a lack of methodology to incorporate the AOP into a TK-TD model. Here, we describe a novel approach that integrates the AOP and TK-TD models to predict mixture toxicity using metal mixtures (specifically Cd-Cu) as a case study. We preliminarily constructed an AOP of the metal mixture through temporal transcriptome analysis together with confirmatory bioassays. The AOP revealed that prolonged exposure time activated more key events and adverse outcomes, indicating different modes of action over time. We selected a potential key event as a proxy for damage and used it as a measurable parameter to replace the theoretical parameter (scaled damage) in the TK-TD model. This refined model, which connects molecular responses to organism outcomes, effectively predicts Cd-Cu mixture toxicity over time and can be extended to other metal mixtures and even multicomponent mixtures. Overall, our results contribute to a better understanding of metal mixture toxicity and provide insights for integrating the AOP and TK-TD models to improve risk assessment for chemical mixtures.
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Affiliation(s)
- Lanpeng Yang
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, P. R. China
- School of Energy and Environment and State Key Laboratory of Marine Pollution, City University of Hong Kong, Kowloon, Hong Kong 999077, P. R. China
| | - Jing Zeng
- School of Life Sciences, Lanzhou University, Lanzhou 730000, P. R. China
| | - Ning Gao
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, P. R. China
| | - Lin Zhu
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, P. R. China
| | - Jianfeng Feng
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, P. R. China
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13
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Monteagudo B, Marqués FM, Gibelin J, Orr NA, Corsi A, Kubota Y, Casal J, Gómez-Camacho J, Authelet G, Baba H, Caesar C, Calvet D, Delbart A, Dozono M, Feng J, Flavigny F, Gheller JM, Giganon A, Gillibert A, Hasegawa K, Isobe T, Kanaya Y, Kawakami S, Kim D, Kiyokawa Y, Kobayashi M, Kobayashi N, Kobayashi T, Kondo Y, Korkulu Z, Koyama S, Lapoux V, Maeda Y, Motobayashi T, Miyazaki T, Nakamura T, Nakatsuka N, Nishio Y, Obertelli A, Ohkura A, Ota S, Otsu H, Ozaki T, Panin V, Paschalis S, Pollacco EC, Reichert S, Rousse JY, Saito AT, Sakaguchi S, Sako M, Santamaria C, Sasano M, Sato H, Shikata M, Shimizu Y, Shindo Y, Stuhl L, Sumikama T, Sun YL, Tabata M, Togano Y, Tsubota J, Uesaka T, Yang ZH, Yasuda J, Yoneda K, Zenihiro J. Mass, Spectroscopy, and Two-Neutron Decay of ^{16}Be. Phys Rev Lett 2024; 132:082501. [PMID: 38457706 DOI: 10.1103/physrevlett.132.082501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/18/2023] [Accepted: 01/29/2024] [Indexed: 03/10/2024]
Abstract
The structure and decay of the most neutron-rich beryllium isotope, ^{16}Be, has been investigated following proton knockout from a high-energy ^{17}B beam. Two relatively narrow resonances were observed for the first time, with energies of 0.84(3) and 2.15(5) MeV above the two-neutron decay threshold and widths of 0.32(8) and 0.95(15) MeV, respectively. These were assigned to be the ground (J^{π}=0^{+}) and first excited (2^{+}) state, with E_{x}=1.31(6) MeV. The mass excess of ^{16}Be was thus deduced to be 56.93(13) MeV, some 0.5 MeV more bound than the only previous measurement. Both states were observed to decay by direct two-neutron emission. Calculations incorporating the evolution of the wave function during the decay as a genuine three-body process reproduced the principal characteristics of the neutron-neutron energy spectra for both levels, indicating that the ground state exhibits a strong spatially compact dineutron component, while the 2^{+} level presents a far more diffuse neutron-neutron distribution.
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Affiliation(s)
- B Monteagudo
- LPC Caen, ENSICAEN, CNRS/IN2P3, Université de Caen, Normandie Université, 14050 Caen, France
- FRIB, Michigan State University, East Lansing, Michigan 48824, USA
| | - F M Marqués
- LPC Caen, ENSICAEN, CNRS/IN2P3, Université de Caen, Normandie Université, 14050 Caen, France
| | - J Gibelin
- LPC Caen, ENSICAEN, CNRS/IN2P3, Université de Caen, Normandie Université, 14050 Caen, France
| | - N A Orr
- LPC Caen, ENSICAEN, CNRS/IN2P3, Université de Caen, Normandie Université, 14050 Caen, France
| | - A Corsi
- Département de Physique Nucléaire, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Y Kubota
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
- Center for Nuclear Study, University of Tokyo, Hongo 7-3-1, Bunkyo, Tokyo 113-0033, Japan
- Department of Physics, Institut für Kernphysik, Technische Universität Darmstadt, D-64289 Darmstadt, Germany
| | - J Casal
- Dipartimento di Fisica e Astronomia "G. Galilei" and INFN-Sezione di Padova, Via Marzolo 8, 35131 Padova, Italy
- Departamento de Física Atómica, Molecular y Nuclear, Facultad de Física, Universidad de Sevilla, Apartado 1065, E-41080 Sevilla, Spain
| | - J Gómez-Camacho
- Departamento de Física Atómica, Molecular y Nuclear, Facultad de Física, Universidad de Sevilla, Apartado 1065, E-41080 Sevilla, Spain
| | - G Authelet
- Département des Accélérateurs, de Cryogénie et de Magnétisme, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - H Baba
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - C Caesar
- Department of Physics, Institut für Kernphysik, Technische Universität Darmstadt, D-64289 Darmstadt, Germany
| | - D Calvet
- Département d'électronique des Détecteurs et d'Informatique pour la Physique, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - A Delbart
- Département d'électronique des Détecteurs et d'Informatique pour la Physique, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - M Dozono
- Center for Nuclear Study, University of Tokyo, Hongo 7-3-1, Bunkyo, Tokyo 113-0033, Japan
| | - J Feng
- School of Physics, Peking University, Beijing 100871, China
| | - F Flavigny
- Institut de Physique Nucléaire, IN2P3-CNRS, Université Paris-Sud, Université Paris-Saclay, 91406 Orsay Cedex, France
| | - J-M Gheller
- Département des Accélérateurs, de Cryogénie et de Magnétisme, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - A Giganon
- Département d'électronique des Détecteurs et d'Informatique pour la Physique, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - A Gillibert
- Département de Physique Nucléaire, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - K Hasegawa
- Department of Physics, Tohoku University, Miyagi 980-8578, Japan
| | - T Isobe
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - Y Kanaya
- Department of Applied Physics, University of Miyazaki, Gakuen-Kibanadai-Nishi 1-1, Miyazaki 889-2192, Japan
| | - S Kawakami
- Department of Applied Physics, University of Miyazaki, Gakuen-Kibanadai-Nishi 1-1, Miyazaki 889-2192, Japan
| | - D Kim
- Department of Physics, Ewha Womans University, Republic of Korea
| | - Y Kiyokawa
- Center for Nuclear Study, University of Tokyo, Hongo 7-3-1, Bunkyo, Tokyo 113-0033, Japan
| | - M Kobayashi
- Center for Nuclear Study, University of Tokyo, Hongo 7-3-1, Bunkyo, Tokyo 113-0033, Japan
| | - N Kobayashi
- Department of Physics, Tokyo Institute of Technology, 2-12-1 O-Okayama, Meguro, Tokyo 152-8551, Japan
| | - T Kobayashi
- Department of Physics, Tohoku University, Miyagi 980-8578, Japan
| | - Y Kondo
- Department of Physics, Tokyo Institute of Technology, 2-12-1 O-Okayama, Meguro, Tokyo 152-8551, Japan
| | - Z Korkulu
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - S Koyama
- Department of Physics, University of Tokyo, Hongo 7-3-1, Bunkyo, Tokyo 113-0033, Japan
| | - V Lapoux
- Département de Physique Nucléaire, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Y Maeda
- Department of Applied Physics, University of Miyazaki, Gakuen-Kibanadai-Nishi 1-1, Miyazaki 889-2192, Japan
| | - T Motobayashi
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - T Miyazaki
- Department of Physics, University of Tokyo, Hongo 7-3-1, Bunkyo, Tokyo 113-0033, Japan
| | - T Nakamura
- Department of Physics, Tokyo Institute of Technology, 2-12-1 O-Okayama, Meguro, Tokyo 152-8551, Japan
| | - N Nakatsuka
- Department of Physics, Kyoto University, Kyoto 606-8502, Japan
| | - Y Nishio
- Department of Physics, Kyushu University, Nishi, Fukuoka 819-0367, Japan
| | - A Obertelli
- Département de Physique Nucléaire, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
- Department of Physics, Institut für Kernphysik, Technische Universität Darmstadt, D-64289 Darmstadt, Germany
| | - A Ohkura
- Department of Physics, Kyushu University, Nishi, Fukuoka 819-0367, Japan
| | - S Ota
- Center for Nuclear Study, University of Tokyo, Hongo 7-3-1, Bunkyo, Tokyo 113-0033, Japan
| | - H Otsu
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - T Ozaki
- Department of Physics, Tokyo Institute of Technology, 2-12-1 O-Okayama, Meguro, Tokyo 152-8551, Japan
| | - V Panin
- Département de Physique Nucléaire, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - S Paschalis
- Department of Physics, Institut für Kernphysik, Technische Universität Darmstadt, D-64289 Darmstadt, Germany
| | - E C Pollacco
- Département de Physique Nucléaire, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - S Reichert
- Department of Physics, Technische Universität Munchen, 85748 Garching bei München, Germany
| | - J-Y Rousse
- Département d'Ingénierie des Systèmes, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - A T Saito
- Department of Physics, Tokyo Institute of Technology, 2-12-1 O-Okayama, Meguro, Tokyo 152-8551, Japan
| | - S Sakaguchi
- Department of Physics, Kyushu University, Nishi, Fukuoka 819-0367, Japan
| | - M Sako
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - C Santamaria
- Département de Physique Nucléaire, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - M Sasano
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - H Sato
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - M Shikata
- Department of Physics, Tokyo Institute of Technology, 2-12-1 O-Okayama, Meguro, Tokyo 152-8551, Japan
| | - Y Shimizu
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - Y Shindo
- Department of Physics, Kyushu University, Nishi, Fukuoka 819-0367, Japan
| | - L Stuhl
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - T Sumikama
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - Y L Sun
- Département de Physique Nucléaire, IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
- Department of Physics, Institut für Kernphysik, Technische Universität Darmstadt, D-64289 Darmstadt, Germany
| | - M Tabata
- Department of Physics, Kyushu University, Nishi, Fukuoka 819-0367, Japan
| | - Y Togano
- Department of Physics, Tokyo Institute of Technology, 2-12-1 O-Okayama, Meguro, Tokyo 152-8551, Japan
| | - J Tsubota
- Department of Physics, Tokyo Institute of Technology, 2-12-1 O-Okayama, Meguro, Tokyo 152-8551, Japan
| | - T Uesaka
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - Z H Yang
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - J Yasuda
- Department of Physics, Kyushu University, Nishi, Fukuoka 819-0367, Japan
| | - K Yoneda
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - J Zenihiro
- RIKEN Nishina Center, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
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14
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Liu X, Jiao G, Zhou F, Kendrick KM, Yao D, Gong Q, Xiang S, Jia T, Zhang XY, Zhang J, Feng J, Becker B. A neural signature for the subjective experience of threat anticipation under uncertainty. Nat Commun 2024; 15:1544. [PMID: 38378947 PMCID: PMC10879105 DOI: 10.1038/s41467-024-45433-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/22/2024] [Indexed: 02/22/2024] Open
Abstract
Uncertainty about potential future threats and the associated anxious anticipation represents a key feature of anxiety. However, the neural systems that underlie the subjective experience of threat anticipation under uncertainty remain unclear. Combining an uncertainty-variation threat anticipation paradigm that allows precise modulation of the level of momentary anxious arousal during functional magnetic resonance imaging (fMRI) with multivariate predictive modeling, we train a brain model that accurately predicts subjective anxious arousal intensity during anticipation and test it across 9 samples (total n = 572, both gender). Using publicly available datasets, we demonstrate that the whole-brain signature specifically predicts anxious anticipation and is not sensitive in predicting pain, general anticipation or unspecific emotional and autonomic arousal. The signature is also functionally and spatially distinguishable from representations of subjective fear or negative affect. We develop a sensitive, generalizable, and specific neuroimaging marker for the subjective experience of uncertain threat anticipation that can facilitate model development.
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Affiliation(s)
- Xiqin Liu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Guojuan Jiao
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Feng Zhou
- Faculty of Psychology, Southwest University, Chongqing, China
- MOE Key Laboratory of Cognition and Personality, Chongqing, China
| | - Keith M Kendrick
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Dezhong Yao
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
- The Centre for Population Neuroscience and Stratified Medicine (PONS), ISTBI, Fudan University, Shanghai, China
- SGDP Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Xiao-Yong Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Benjamin Becker
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China.
- Department of Psychology, The University of Hong Kong, Hong Kong, China.
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15
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Li XP, Li Y, Liu L, Yuan ZT, Wang YC, Dong YC, Zhang DS, Feng J, Chen YN, Wang SB. [Clinical study of the efficacies of ruxolitinib plus low-dose PTCY for acute GVHD prevention after haploidentical transplantation in malignant hematological diseases]. Zhonghua Xue Ye Xue Za Zhi 2024; 45:128-133. [PMID: 38604788 DOI: 10.3760/cma.j.cn121090-20230929-00153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Objective: To investigate and verify a novel acute graft versus host disease (aGVHD) prevention protocol in the context of haploidentical hematopoietic stem cell transplantation (haplo-HSCT) . Methods: Patients who underwent haplo-HSCT in our center between January 2022 and December 2022 were included. All patients received reduced doses of cyclophosphamide, Rabbit anti-human tymoglobulin, ruxolitinib, methotrexate, cyclosporine, and MMF to prevent aGVHD. The transplantation outcomes, complications, and survival rate of all patients were investigated. Results: A total of 52 patients with haplo-HSCT were enrolled, 29 (55.8%) male and 23 (44.2%) female, with a median age of 28 (5-59) years. There were 25 cases of acute myeloid leukemia, 17 cases of acute lymphocyte leukemia, 6 cases of myelodysplastic syndrome, 2 cases of chronic myeloid leukemia and 2 cases of myeloproliferative neoplasms. 98.1% of patients had successful engraftment. The incidence of Ⅱ-Ⅳ aGVHD and Ⅲ-Ⅳ aGVHD was 19.2% (95% CI 8.2% -30.3% ) and 7.7% (95% CI 0.2% -15.2% ), respectively. No patients experienced severe gastrointestinal mucositis. The Epstein-Barr virus and CMV reactivation rates were 40.4% and 21.3%, respectively. 9.6% of patients relapsed during followup, with 1-year overall survival, progression-free survival, and non-relapse mortality rates of 86.5% (95% CI 76.9% -96.1% ), 78.8% (95% CI 67.4% -90.3% ) and 11.5% (95% CI 2.6% -20.5% ), respectively. Conclusion: Ruxolitinib combined with a low dose of PTCY is a safe and effective first-line aGVHD prevention strategy.
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Affiliation(s)
- X P Li
- Department of Hematology, 920th Hospital of Joint Logistics Support Force, Kunming 650000, China
| | - Y Li
- Department of Hematology, 920th Hospital of Joint Logistics Support Force, Kunming 650000, China
| | - L Liu
- Department of Hematology, 920th Hospital of Joint Logistics Support Force, Kunming 650000, China
| | - Z T Yuan
- Department of Hematology, 920th Hospital of Joint Logistics Support Force, Kunming 650000, China
| | - Y C Wang
- Department of Hematology, 920th Hospital of Joint Logistics Support Force, Kunming 650000, China
| | - Y C Dong
- Department of Hematology, 920th Hospital of Joint Logistics Support Force, Kunming 650000, China
| | - D S Zhang
- Department of Hematology, 920th Hospital of Joint Logistics Support Force, Kunming 650000, China
| | - J Feng
- Department of Hematology, 920th Hospital of Joint Logistics Support Force, Kunming 650000, China
| | - Y N Chen
- Department of Hematology, 920th Hospital of Joint Logistics Support Force, Kunming 650000, China
| | - S B Wang
- Department of Hematology, 920th Hospital of Joint Logistics Support Force, Kunming 650000, China
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16
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Gao N, Yang L, Lu X, Zhu L, Feng J. Non-negligible vector effect of micro(nano)plastics on tris(1,3-dichloro-2-propyl) phosphate in zebrafish quantified by toxicokinetic model. J Hazard Mater 2024; 463:132928. [PMID: 37944229 DOI: 10.1016/j.jhazmat.2023.132928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 10/23/2023] [Accepted: 11/02/2023] [Indexed: 11/12/2023]
Abstract
Micro(nano)plastics (MNPs) inevitably interact with coexisting contaminants and can act as vectors to affect their fate in organisms. However, the quantitative contribution of MNPs in the in vivo bioaccumulation and distribution of their coexisting contaminants remains unclear. Here, by selecting tris(1,3-dichloro-2-propyl) phosphate (TDCIPP) as the typical coexisting contaminant, we quantified the contribution of MNPs to bioaccumulation and distribution of TDCIPP with toxicokinetic models. Results indicated that MNPs differentially facilitated TDCIPP bioaccumulation and distribution, and NPs slowed down TDCIPP depuration more significantly than MPs. Model analysis further revealed increasing contributions of MNPs to whole-fish TDCIPP bioaccumulation over time, with NPs (33-42%) contributing more than MPs (12-32%) at 48 h exposure. NPs contributed more than MPs to TDCIPP distribution in the liver (13-19% for MPs; 36-52% for NPs) and carcass (24-45% for MPs; 57-71% for NPs). The size-dependent vector effect might be attributed to the fact that MNPs promote contaminant transfer by damaging biofilm structure and increasing tissue membrane permeability, with NPs exerting stronger effects. This work demonstrated the effectiveness of using modeling tools to understand the relative importance of MNPs as contaminant vectors in the TK process and highlighted the higher contaminant transfer potential of NPs under combined exposure scenarios.
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Affiliation(s)
- Ning Gao
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Lanpeng Yang
- School of Energy and Environment and State Key Laboratory of Marine Pollution, City University of Hong Kong, Kowloon 999077, Hong Kong SAR China
| | - Xueqiang Lu
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Lin Zhu
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Jianfeng Feng
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.
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17
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Wan X, Fang Y, Jiang Y, Lu X, Zhu L, Feng J. Temperature and nutrients alter the relative importance of stochastic and deterministic processes in the coastal macroinvertebrates biodiversity assembly on long-time scales. Ecol Evol 2024; 14:e11062. [PMID: 38389996 PMCID: PMC10883258 DOI: 10.1002/ece3.11062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/01/2024] [Accepted: 02/07/2024] [Indexed: 02/24/2024] Open
Abstract
Macroinvertebrates play a vital role in coastal ecosystems and are an important indicator of ecosystem quality. Both anthropogenic activity and environmental changes may lead to significant changes in the marine macroinvertebrate community. However, the assembly process of benthic biodiversity and its mechanism driven by environmental factors at large scales remains unclear. Here, using the benthic field survey data of 15 years at large spatial and temporal scales from the Yellow Sea Large Marine Ecosystem, we investigated the relative importance of environmental selection, dispersal processes, random-deterministic processes of macroinvertebrates community diversity assembly, and the responses of this relative importance driven by temperature and nutrients. Results showed that the macroinvertebrates community diversity is mainly affected by dispersal. Nitrogen and phosphorus are the most important negative factors among environmental variables, while geographical distance is the main limiting factor of β diversity. Within the range of 0.35-0.70 mg/L of nutrients, increasing nutrient concentration can significantly facilitate the contribution of the decay effect to β diversity. Within the temperature range studied (15.0-18.0°C), both warming and cooling can lead to a greater tendency for species diversity assembly processes to be dominated by deterministic processes. The analysis contributes to a better understanding of the assembly process of the diversity of coastal marine macroinvertebrates communities and how they adapt to global biogeochemical processes.
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Affiliation(s)
- Xuhao Wan
- College of Environmental Science and Engineering Nankai University Tianjin China
| | - Yuan Fang
- College of Environmental Science and Engineering Nankai University Tianjin China
| | - Yueming Jiang
- College of Environmental Science and Engineering Nankai University Tianjin China
| | - Xueqiang Lu
- College of Environmental Science and Engineering Nankai University Tianjin China
| | - Lin Zhu
- College of Environmental Science and Engineering Nankai University Tianjin China
| | - Jianfeng Feng
- College of Environmental Science and Engineering Nankai University Tianjin China
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18
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Ding J, He W, Sha W, Shan G, Zhu L, Zhu L, Feng J. Physiologically based toxicokinetic modelling of Tri(2-chloroethyl) phosphate (TCEP) in mice accounting for multiple exposure routes. Ecotoxicol Environ Saf 2024; 271:115976. [PMID: 38232524 DOI: 10.1016/j.ecoenv.2024.115976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 12/24/2023] [Accepted: 01/11/2024] [Indexed: 01/19/2024]
Abstract
Exposure routes are important for health risk assessment of chemical risks. The application of physiologically based toxicokinetic (PBTK) models to predict concentrations in vivo can determine the effects of harmful substances and tissue accumulation on the premise of saving experimental costs. In this study, Tri(2-chloroethyl) phosphate (TCEP), an organophosphate ester (OPE), was used as an example to study the PBTK model of mice exposed to different exposure doses by multiple routes. Different routes of exposure (gavage and intradermal injection) can cause differences in the concentration of chemicals in the organs. TCEP that enters the body through the mouth is mainly concentrated in the gastrointestinal tract and liver. However, the concentrations of chemicals that enter the skin into the mice are higher in skin, rest of body, and blood. In addition, TCEP was absorbed and accumulated very rapidly in mice, within half an hour after a single exposure. We have successfully established a mouse PBTK model of the TCEP accounting for multiple exposure Routes and obtained a series of kinetic parameters. The model includes blood, liver, kidney, stomach, intestine, skin, and rest of body compartments. Oral and dermal exposure route was considered for PBTK model. The PBTK model established in this study has a good predictive ability. More than 70% of the predicted values deviated from the measured values by less than 5-fold. In addition, we extrapolated the model to humans. A human PBTK model is built. We performed a health risk assessment for world populations based on human PBTK model. The risk of TCEP in dust is greater through mouth than through skin. The risk of TCEP in food of Chinese population is greater than dust.
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Affiliation(s)
- Jiaqi Ding
- Key laboratory of Pollution process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Wanyu He
- Key laboratory of Pollution process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Wanxiao Sha
- Key laboratory of Pollution process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Guoqiang Shan
- Key laboratory of Pollution process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Lingyan Zhu
- Key laboratory of Pollution process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Lin Zhu
- Key laboratory of Pollution process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Jianfeng Feng
- Key laboratory of Pollution process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.
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19
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Feng J, Liu WB, Li Y, Ding CG. [Determination of Perchloroethylene in blood by headspace gas chromatography-mass spectrometry]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi 2024; 42:42-46. [PMID: 38311948 DOI: 10.3760/cma.j.cn121094-20221026-00514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Objective: To establish a method for determination of Perchloroethylene (PCE) in blood by headspace gas chromatography-mass spectrometry (HS/GC-MS) . Methods: From Dctober to December 2021, A total of 3 mL blood samples were taken into a 10 mL headspace bottle, after heated at 60 ℃ for 30 mins, PCE in the top air was separated by VF-WAXms capillary column and detected by GC-MS. The retention time and external standard method were used for qualitative and quantitative analysis of PCE in samples, respectively. Results: There was good linear relationship in the range of 5.09-200.17 μg/L. The linear correlation coefficient was 0.9993.The detection limit was 0.21 μg/L and the lower limit of quantitation was 0.70 μg/L. The recovery rates of samples with different concentrations were 95.3%-103.8%. The intra-batch relative standard deviations (RSD) were 3.2%-4.6%, and inter-batch RSD was 4.0%-6.1%. The samples can be stored at 4 ℃ for three days and at -20 ℃ for seven days. Conclusion: This method is proved to be simple, practical and highly sensitive, which is suitable for the determination of PCE in blood.
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Affiliation(s)
- J Feng
- National Center for Occupational Safety and Health, NHC (National Center for Occupational Medicine of Coal Industry, NHC), Beijing 102308, China
| | - W B Liu
- National Center for Occupational Safety and Health, NHC (National Center for Occupational Medicine of Coal Industry, NHC), Beijing 102308, China
| | - Y Li
- National Center for Occupational Safety and Health, NHC (National Center for Occupational Medicine of Coal Industry, NHC), Beijing 102308, China
| | - C G Ding
- National Center for Occupational Safety and Health, NHC (National Center for Occupational Medicine of Coal Industry, NHC), Beijing 102308, China
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20
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Zhou C, Wang L, Cheng W, Lv J, Guan X, Guo T, Wu J, Zhang W, Gao T, Liu X, Bai X, Wu H, Cao Z, Gu L, Chen J, Wen J, Huang P, Xu X, Zhang B, Feng J, Zhang M. Author Correction: Two distinct trajectories of clinical and neurodegeneration events in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:22. [PMID: 38228691 DOI: 10.1038/s41531-024-00635-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2024] Open
Affiliation(s)
- Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Linbo Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom.
| | - JinChao Lv
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Ting Gao
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Xueqin Bai
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Haoting Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Zhengye Cao
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Luyan Gu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Jingwen Chen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Jiaqi Wen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom.
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China.
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21
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Rolls ET, Deco G, Huang CC, Feng J. The connectivity of the human frontal pole cortex, and a theory of its involvement in exploit versus explore. Cereb Cortex 2024; 34:bhad416. [PMID: 37991264 DOI: 10.1093/cercor/bhad416] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/23/2023] Open
Abstract
The frontal pole is implicated in humans in whether to exploit resources versus explore alternatives. Effective connectivity, functional connectivity, and tractography were measured between six human frontal pole regions and for comparison 13 dorsolateral and dorsal prefrontal cortex regions, and the 360 cortical regions in the Human Connectome Project Multi-modal-parcellation atlas in 171 HCP participants. The frontal pole regions have effective connectivity with Dorsolateral Prefrontal Cortex regions, the Dorsal Prefrontal Cortex, both implicated in working memory; and with the orbitofrontal and anterior cingulate cortex reward/non-reward system. There is also connectivity with temporal lobe, inferior parietal, and posterior cingulate regions. Given this new connectivity evidence, and evidence from activations and damage, it is proposed that the frontal pole cortex contains autoassociation attractor networks that are normally stable in a short-term memory state, and maintain stability in the other prefrontal networks during stable exploitation of goals and strategies. However, if an input from the orbitofrontal or anterior cingulate cortex that expected reward, non-reward, or punishment is received, this destabilizes the frontal pole and thereby other prefrontal networks to enable exploration of competing alternative goals and strategies. The frontal pole connectivity with reward systems may be key in exploit versus explore.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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Zhang B, Rolls ET, Wang X, Xie C, Cheng W, Feng J. Roles of the medial and lateral orbitofrontal cortex in major depression and its treatment. Mol Psychiatry 2024:10.1038/s41380-023-02380-w. [PMID: 38212376 DOI: 10.1038/s41380-023-02380-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 12/06/2023] [Accepted: 12/13/2023] [Indexed: 01/13/2024]
Abstract
We describe evidence for dissociable roles of the medial and lateral orbitofrontal cortex (OFC) in major depressive disorder (MDD) from structure, functional activation, functional connectivity, metabolism, and neurochemical systems. The reward-related medial orbitofrontal cortex has lower connectivity and less reward sensitivity in MDD associated with anhedonia symptoms; and the non-reward related lateral OFC has higher functional connectivity and more sensitivity to non-reward/aversive stimuli in MDD associated with negative bias symptoms. Importantly, we propose that conventional antidepressants act to normalize the hyperactive lateral (but not medial) OFC to reduce negative bias in MDD; while other treatments are needed to operate on the medial OFC to reduce anhedonia, with emerging evidence suggesting that ketamine may act in this way. The orbitofrontal cortex is the key cortical region in emotion and reward, and the current review presents much new evidence about the different ways that the medial and lateral OFC are involved in MDD.
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Affiliation(s)
- Bei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, PR China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China.
- Oxford Centre for Computational Neuroscience, Oxford, UK.
- Department of Computer Science, University of Warwick, Coventry, UK.
| | - Xiang Wang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, PR China
- Medical Psychological Institute, Central South University, Changsha, PR China
- China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, PR China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, PR China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, PR China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, PR China.
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, PR China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, PR China.
- Department of Computer Science, University of Warwick, Coventry, UK.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, PR China.
- Zhangjiang Fudan International Innovation Center, Shanghai, PR China.
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Li Z, Ma Q, Deng Y, Rolls ET, Shen C, Li Y, Zhang W, Xiang S, Langley C, Sahakian BJ, Robbins TW, Yu JT, Feng J, Cheng W. Irritable Bowel Syndrome Is Associated With Brain Health by Neuroimaging, Behavioral, Biochemical, and Genetic Analyses. Biol Psychiatry 2024:S0006-3223(24)00027-1. [PMID: 38199582 DOI: 10.1016/j.biopsych.2023.12.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/14/2023] [Accepted: 12/13/2023] [Indexed: 01/12/2024]
Abstract
BACKGROUND Irritable bowel syndrome (IBS) interacts with psychopathology in a complex way; however, little is known about the underlying brain, biochemical, and genetic mechanisms. METHODS To clarify the phenotypic and genetic associations between IBS and brain health, we performed a comprehensive retrospective cohort study on a large population. Our study included 171,104 participants from the UK Biobank who underwent a thorough assessment of IBS, with the majority also providing neuroimaging, behavioral, biochemical, and genetic information. Multistage linked analyses were conducted, including phenome-wide association analysis, polygenic risk score calculation, and 2-sample Mendelian randomization analysis. RESULTS The phenome-wide association analysis showed that IBS was linked to brain health problems, including anxiety and depression, and poor cognitive performance. Significantly lower brain volumes associated with more severe IBS were found in key areas related to emotional regulation and higher-order cognition, including the medial orbitofrontal cortex/ventromedial prefrontal cortex, anterior insula, anterior and mid-cingulate cortices, dorsolateral prefrontal cortex, and hippocampus. Higher triglycerides, lower high-intensity lipoprotein, and lower platelets were also related (p < 1 × 10-10) to more severe IBS. Finally, Mendelian randomization analyses demonstrated potential causal relationships between IBS and brain health and indicated possible mediating effects of dyslipidemia and inflammation. CONCLUSIONS For the first time, this study provides a comprehensive understanding of the relationship between IBS and brain health phenotypes, integrating perspectives from neuroimaging, behavioral performance, biochemical factors, and genetics, which is of great significance for clinical applications to potentially address brain health impairments in patients with IBS.
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Affiliation(s)
- Zeyu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Qing Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yueting Deng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, United Kingdom; Oxford Centre for Computational Neuroscience, Oxford, United Kingdom.
| | - Chun Shen
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yuzhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Christelle Langley
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Barbara J Sahakian
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Trevor W Robbins
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Jin-Tai Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, United Kingdom; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
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Zhang R, Rolls ET, Cheng W, Feng J. Different cortical connectivities in human females and males relate to differences in strength and body composition, reward and emotional systems, and memory. Brain Struct Funct 2024; 229:47-61. [PMID: 37861743 PMCID: PMC10827883 DOI: 10.1007/s00429-023-02720-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023]
Abstract
Sex differences in human brain structure and function are important, partly because they are likely to be relevant to the male-female differences in behavior and in mental health. To analyse sex differences in cortical function, functional connectivity was measured in 36,531 participants (53% female) in the UK Biobank (mean age 69) using the Human Connectome Project multimodal parcellation atlas with 360 well-specified cortical regions. Most of the functional connectivities were lower in females (Bonferroni corrected), with the mean Cohen's d = - 0.18. Removing these as covariates reduced the difference of functional connectivities for females-males from d = - 0.18 to - 0.06. The lower functional connectivities in females were especially of somatosensory/premotor regions including the insula, opercular cortex, paracentral lobule and mid-cingulate cortex, and were correlated with lower maximum workload (r = 0.17), and with higher whole body fat mass (r = - 0.17). But some functional connectivities were higher in females, involving especially the ventromedial prefrontal cortex and posterior cingulate cortex, and these were correlated with higher liking for some rewards such as sweet foods, higher happiness/subjective well-being, and with better memory-related functions. The main findings were replicated in 1000 individuals (532 females, mean age 29) from the Human Connectome Project. This investigation shows the cortical systems with different functional connectivity between females and males, and also provides for the first time a foundation for understanding the implications for behavior of these differences between females and males.
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Affiliation(s)
- Ruohan Zhang
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK
| | - Edmund T Rolls
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK.
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, 200403, China.
- Oxford Centre for Computational Neuroscience, Oxford, UK.
| | - Wei Cheng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, 200403, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, 200403, China
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25
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Sun YJ, Sahakian BJ, Langley C, Yang A, Jiang Y, Kang J, Zhao X, Li C, Cheng W, Feng J. Early-initiated childhood reading for pleasure: associations with better cognitive performance, mental well-being and brain structure in young adolescence. Psychol Med 2024; 54:359-373. [PMID: 37376848 DOI: 10.1017/s0033291723001381] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
BACKGROUND Childhood is a crucial neurodevelopmental period. We investigated whether childhood reading for pleasure (RfP) was related to young adolescent assessments of cognition, mental health, and brain structure. METHODS We conducted a cross-sectional and longitudinal study in a large-scale US national cohort (10 000 + young adolescents), using the well-established linear mixed model and structural equation methods for twin study, longitudinal and mediation analyses. A 2-sample Mendelian randomization (MR) analysis for potential causal inference was also performed. Important factors including socio-economic status were controlled. RESULTS Early-initiated long-standing childhood RfP (early RfP) was highly positively correlated with performance on cognitive tests and significantly negatively correlated with mental health problem scores of young adolescents. These participants with higher early RfP scores exhibited moderately larger total brain cortical areas and volumes, with increased regions including the temporal, frontal, insula, supramarginal; left angular, para-hippocampal; right middle-occipital, anterior-cingulate, orbital areas; and subcortical ventral-diencephalon and thalamus. These brain structures were significantly related to their cognitive and mental health scores, and displayed significant mediation effects. Early RfP was longitudinally associated with higher crystallized cognition and lower attention symptoms at follow-up. Approximately 12 h/week of youth regular RfP was cognitively optimal. We further observed a moderately significant heritability of early RfP, with considerable contribution from environments. MR analysis revealed beneficial causal associations of early RfP with adult cognitive performance and left superior temporal structure. CONCLUSIONS These findings, for the first time, revealed the important relationships of early RfP with subsequent brain and cognitive development and mental well-being.
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Affiliation(s)
- Yun-Jun Sun
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Barbara J Sahakian
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Christelle Langley
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Anyi Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yuchao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Xingming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Chunhe Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
- Zhangjiang Fudan International Innovation Center, Shanghai, China
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He W, Ding J, Gao N, Zhu L, Zhu L, Feng J. Elucidating the toxicity mechanisms of organophosphate esters by adverse outcome pathway network. Arch Toxicol 2024; 98:233-250. [PMID: 37864630 DOI: 10.1007/s00204-023-03624-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 10/05/2023] [Indexed: 10/23/2023]
Abstract
With the widespread use of organophosphate esters (OPEs), the accumulation and toxicity effect of OPEs in biota are attracting more and more concern. In order to clarify the mechanism of toxicity of OPEs to organisms, this study reviewed the OPEs toxicity and systematically identified the mechanism of OPEs toxicity under the framework of adverse outcome pathway (AOP). OPEs were divided into three groups (alkyl-OPEs, aryl-OPEs, and halogenated-OPEs) and biota was divided into aquatic organism and mammals. The results showed that tris(1,3-dichloro-2-propyl) phosphate (TDCIPP) and triphenyl phosphate (TPHP) mainly caused neurotoxicity, reproductive, and hepatotoxicity in different mechanisms. According to the constructed AOP network, the toxicity mechanism of OPEs on aquatic organisms and mammals is different, which is mainly attributed to the different biological metabolic systems of aquatic organisms and mammals. Interestingly, our results indicate that the toxicity effect of the three kinds of OPEs on aquatic organisms is different, while there was no obvious difference in the mechanism of toxicity of OPEs on mammals. This study provides a theoretical basis for OPEs risk assessment in the future.
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Affiliation(s)
- Wanyu He
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China
| | - Jiaqi Ding
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China
| | - Ning Gao
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China
| | - Lingyan Zhu
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China
| | - Lin Zhu
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China
| | - Jianfeng Feng
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China.
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27
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Chen Y, Zhang Q, Sun L, Liu H, Feng J, Li J, Wang Z. Ginsenoside Rg1 attenuates dextran sodium sulfate-induced ulcerative colitis in mice. Physiol Res 2023; 72:783-792. [PMID: 38215064 PMCID: PMC10805260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 08/30/2023] [Indexed: 01/14/2024] Open
Abstract
Ulceration colitis (UC) is a chronic and recurrent inflammatory disorder in the gastro-intestinal tract. The purpose of our study is to explore the potential mechanisms of ginsenoside Rg1 (GS Rg1) on dextran sulfate sodium (DSS)-induced colitis in mice and lipopolysaccharide (LPS)-induced RAW 264.7 cells. Acute colitis was induced in male C57BL/6 mice. In vitro model of LPS-induced RAW 264.7 cells to simulate enteritis model. The disease activity index (DAI), colon length, body weight and histopathological analysis were performed in vivo. Pro-inflammatory cytokines and markers for oxidative and anti-oxidative stress, MPO level were measured in vivo and in vitro. Nuclear erythroid 2-related factor 2 (Nrf2) and NF-?B p65 protein levels were analyzed using western blotting. Our results indicated that the UC models were established successfully by drinking DSS water. GS Rg1 significantly attenuated UC-related symptoms, including preventing weight loss, decreasing DAI scores, and increasing colon length. GS Rg1 ameliorated the DSS-induced oxidative stress. IL-1beta, IL-6, and TNF-alpha levels were significantly increased in serum and cell supernatant effectively, while treatment with the GS Rg1 significantly reduced these factors. GS Rg1 reduced MPO content in the colon. GS Rg1 treatment increased SOD and decreased MDA levels in the serum, colon, and cell supernatant. GS Rg1 restored the Nrf-2/HO-1/NF-?B pathway in RAW 264.7 cells and UC mice, and these changes were blocked by Nrf-2 siRNA. Overall, GS Rg1 ameliorated inflammation and oxidative stress in colitis via Nrf-2/HO-1/NF-kappaB pathway. Thus, GS Rg1 could serve as a potential therapeutic agent for the treatment of UC.
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Affiliation(s)
- Y Chen
- Department of Pediatrics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
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28
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Xiang S, Jia T, Xie C, Zhu Z, Cheng W, Schumann G, Robbins TW, Feng J. Fractionation of neural reward processing into independent components by novel decoding principle. Neuroimage 2023; 284:120463. [PMID: 37989457 DOI: 10.1016/j.neuroimage.2023.120463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 11/12/2023] [Accepted: 11/17/2023] [Indexed: 11/23/2023] Open
Abstract
How to retrieve latent neurobehavioural processes from complex neurobiological signals is an important yet unresolved challenge. Here, we develop a novel approach, orthogonal-Decoding multi-Cognitive Processes (DeCoP), to reveal underlying latent neurobehavioural processing and show that its performance is superior to traditional non-orthogonal decoding in terms of both false inference and robustness. Processing value and salience information are two fundamental but mutually confounded pathways of reward reinforcement essential for decision making. During reward/punishment anticipation, we applied DeCoP to decode brain-wide responses into spatially overlapping, yet functionally independent, evaluation and readiness processes, which are modulated differentially by meso‑limbic vs nigro-striatal dopamine systems. Using DeCoP, we further demonstrated that most brain regions only encoded abstract information but not the exact input, except for dorsal anterior cingulate cortex and insula. Furthermore, we anticipate our novel analytical principle to be applied generally in decoding multiple latent neurobehavioral processes and thus advance both the design and hypothesis testing for cognitive tasks.
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Affiliation(s)
- Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China; Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, SE5 8AF, United Kingdom; Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China
| | - Zhichao Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Department of Psychiatry and Psychotherapy, Centre for Population Neuroscience and Precision Medicine (PONS), CCM, Charite Universitaetsmedizin, Berlin, Germany
| | - Trevor W Robbins
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China; Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China; Department of Computer Science, University of Warwick, Coventry, United Kingdom; School of Mathematical Sciences and Centre for Computational Systems Biology, Fudan University, Shanghai, China.
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29
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Zhong X, Qiang Y, Wang L, Zhang Y, Li J, Feng J, Cheng W, Tan L, Yu J. Peripheral immunity and risk of incident brain disorders: a prospective cohort study of 161,968 participants. Transl Psychiatry 2023; 13:382. [PMID: 38071240 PMCID: PMC10710500 DOI: 10.1038/s41398-023-02683-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/23/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
Whether peripheral immunity prospectively influences brain health remains controversial. This study aims to investigate the longitudinal associations between peripheral immunity markers with incident brain disorders. A total of 161,968 eligible participants from the UK Biobank were included. We investigated the linear and non-linear effects of peripheral immunity markers including differential leukocytes counts, their derived ratios and C-reactive protein (CRP) on the risk of dementia, Parkinson's disease (PD), stroke, schizophrenia, bipolar affective disorder (BPAD), major depressive disorder (MDD) and anxiety, using Cox proportional hazard models and restricted cubic spline models. Linear regression models were used to explore potential mechanisms driven by brain structures. During a median follow-up of 9.66 years, 16,241 participants developed brain disorders. Individuals with elevated innate immunity markers including neutrophils, monocytes, platelets, neutrophil-to-lymphocyte ratio (NLR), and systemic immune-inflammation index (SII) had an increased risk of brain disorders. Among these markers, neutrophils exhibited the most significant correlation with risk of dementia (hazard ratio 1.08, 95% confidence interval 1.04-1.12), stroke (HR 1.06, 95% CI 1.03-1.09), MDD (HR 1.13, 95% CI 1.10-1.16) and anxiety (HR 1.07, 95% CI 1.04-1.10). Subgroup analysis revealed age-specific and sex-specific associations between innate immunity markers with risk of dementia and MDD. Neuroimaging analysis highlighted the associations between peripheral immunity markers and alterations in multiple cortical, subcortical regions and white matter tracts, typically implicated in dementia and psychiatric disorders. These findings support the hypothesis that neuroinflammation is important to the etiology of various brain disorders, offering new insights into their potential therapeutic approaches.
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Affiliation(s)
- Xiaoling Zhong
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
- Department of Neurology, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao Cancer Hospital, Qingdao, China
| | - Yixuan Qiang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological diseases, Shanghai, China
| | - Ling Wang
- Department of Neurology, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao Cancer Hospital, Qingdao, China
| | - Yaru Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological diseases, Shanghai, China
| | - Jieqiong Li
- Department of Neurology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jianfeng Feng
- The Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Wei Cheng
- The Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China.
| | - Jintai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological diseases, Shanghai, China.
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30
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Fan H, Liu Z, Wu X, Yu G, Gu X, Kuang N, Zhang K, Liu Y, Jia T, Sahakian BJ, Robbins TW, Schumann G, Cheng W, Feng J, Becker B, Zhang J. Decoding anxiety-impulsivity subtypes in preadolescent internalising disorders: findings from the Adolescent Brain Cognitive Development study. Br J Psychiatry 2023; 223:542-554. [PMID: 37730654 DOI: 10.1192/bjp.2023.107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
BACKGROUND Internalising disorders are highly prevalent emotional dysregulations during preadolescence but clinical decision-making is hampered by high heterogeneity. During this period impulsivity represents a major risk factor for psychopathological trajectories and may act on this heterogeneity given the controversial anxiety-impulsivity relationships. However, how impulsivity contributes to the heterogeneous symptomatology, neurobiology, neurocognition and clinical trajectories in preadolescent internalising disorders remains unclear. AIMS The aim was to determine impulsivity-dependent subtypes in preadolescent internalising disorders that demonstrate distinct anxiety-impulsivity relationships, neurobiological, genetic, cognitive and clinical trajectory signatures. METHOD We applied a data-driven strategy to determine impulsivity-related subtypes in 2430 preadolescents with internalising disorders from the Adolescent Brain Cognitive Development study. Cross-sectional and longitudinal analyses were employed to examine subtype-specific signatures of the anxiety-impulsivity relationship, brain morphology, cognition and clinical trajectory from age 10 to 12 years. RESULTS We identified two distinct subtypes of patients who internalise with comparably high anxiety yet distinguishable levels of impulsivity, i.e. enhanced (subtype 1) or decreased (subtype 2) compared with control participants. The two subtypes exhibited opposing anxiety-impulsivity relationships: higher anxiety at baseline was associated with higher lack of perseverance in subtype 1 but lower sensation seeking in subtype 2 at baseline/follow-up. Subtype 1 demonstrated thicker prefrontal and temporal cortices, and genes enriched in immune-related diseases and glutamatergic and GABAergic neurons. Subtype 1 exhibited cognitive deficits and a detrimental trajectory characterised by increasing emotional/behavioural dysregulations and suicide risks during follow-up. CONCLUSIONS Our results indicate impulsivity-dependent subtypes in preadolescent internalising disorders and unify past controversies about the anxiety-impulsivity interaction. Clinically, individuals with a high-impulsivity subtype exhibit a detrimental trajectory, thus early interventions are warranted.
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Affiliation(s)
- Huaxin Fan
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Zhaowen Liu
- School of Computer Science, Northwestern Polytechnical University, China
| | - Xinran Wu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Gechang Yu
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Xinrui Gu
- Sino-European School of Technology, Shanghai University, China
| | - Nanyu Kuang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Kai Zhang
- School of Computer Science and Technology, East China Normal University, Shanghai, China
| | - Yu Liu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Barbara J Sahakian
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, UK and Department of Psychiatry, University of Cambridge School of Clinical Medicine, UK
| | - Trevor W Robbins
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China; Behavioural and Clinical Neuroscience Institute, University of Cambridge, UK and Department of Psychology, University of Cambridge, UK
| | - Gunter Schumann
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China and PONS-Center, Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité Universitätsmedizin Berlin, Germany
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, China and Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Benjamin Becker
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China; Department of Psychology, The University of Hong Kong, Hong Kong, China
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
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Zhu Y, Pan X, Jia Y, Yang X, Song X, Ding J, Zhong W, Feng J, Zhu L. Exploring Route-Specific Pharmacokinetics of PFAS in Mice by Coupling in Vivo Tests and Physiologically Based Toxicokinetic Models. Environ Health Perspect 2023; 131:127012. [PMID: 38088889 PMCID: PMC10718298 DOI: 10.1289/ehp11969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/08/2023] [Accepted: 11/08/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND Oral ingestion, inhalation, and skin contact are important exposure routes for humans to uptake per- and polyfluoroalkyl substances (PFAS). However, nasal and dermal exposure to PFAS remains unclear, and accurately predicting internal body burden of PFAS in humans via multiple exposure pathways is urgently required. OBJECTIVES We aimed to develop multiple physiologically based toxicokinetic (PBTK) models to unveil the route-specific pharmacokinetics and bioavailability of PFAS via respective oral, nasal, and dermal exposure pathways using a mouse model and sought to predict the internal concentrations in various tissues through multiple exposure routes and extrapolate it to humans. METHODS Mice were administered the mixed solution of perfluorohexane sulfonate, perfluorooctane sulfonate, and perfluorooctanoic acid through oral, nasal, and dermal exposure separately or jointly. The time-dependent concentrations of PFAS in plasma and tissues were determined to calibrate and validate the individual and combined PBTK models, which were applied in single- and repeated-dose scenarios. RESULTS The developed route-specific PBTK models successfully simulated the tissue concentrations of PFAS in mice following single or joint exposure routes as well as long-term repeated dose scenarios. The time to peak concentration of PFAS in plasma via dermal exposure was much longer (34.1-83.0 h) than that via nasal exposure (0.960 h). The bioavailability of PFAS via oral exposure was the highest (73.2%-98.0%), followed by nasal (33.9%-66.8%) and dermal exposure (4.59%-7.80%). This model was extrapolated to predict internal levels in human under real environment. DISCUSSION Based on these data, we predict the following: PFAS were absorbed quickly via nasal exposure, whereas a distinct hysteresis effect was observed for dermal exposure. Almost all the PFAS to which mice were exposed via gastrointestinal route were absorbed into plasma, which exhibited the highest bioavailability. Exhalation clearance greatly depressed the bioavailability of PFAS via nasal exposure, whereas the lowest bioavailability in dermal exposure was because of the interception of PFAS within the skin layers. https://doi.org/10.1289/EHP11969.
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Affiliation(s)
- Yumin Zhu
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| | - Xiaoyu Pan
- Beijing Sankuai Online Technology Co., Ltd., Beijing, P. R. China
| | - Yibo Jia
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| | - Xin Yang
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| | - Xiaohua Song
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| | - Jiaqi Ding
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| | - Wenjue Zhong
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| | - Jianfeng Feng
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
| | - Lingyan Zhu
- Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, P. R. China
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32
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Yang Z, Zhao W, Linli Z, Guo S, Feng J. Associations between polygenic risk scores and accelerated brain ageing in smokers. Psychol Med 2023; 53:7785-7794. [PMID: 37555321 PMCID: PMC10755245 DOI: 10.1017/s0033291723001812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 08/10/2023]
Abstract
BACKGROUND Smoking contributes to a variety of neurodegenerative diseases and neurobiological abnormalities, suggesting that smoking is associated with accelerated brain aging. However, the neurobiological mechanisms affected by smoking, and whether they are genetically influenced, remain to be investigated. METHODS Using structural magnetic resonance imaging data from the UK Biobank (n = 33 293), a brain age predictor was trained on non-smoking healthy groups and tested on smokers to obtain the BrainAge Gap (BAG). The cumulative effect of multiple common genetic variants associated with smoking was then calculated to acquire a polygenic risk score (PRS). The relationship between PRS, BAG, total gray matter volume (tGMV), and smoking parameters was explored and further genes included in the PRS were annotated to identify potential molecular mechanisms affected by smoking. RESULTS The BrainAge in smokers was predicted with very high accuracy (r = 0.725, MAE = 4.16). Smokers had a greater BAG (Cohen's d = 0.074, p < 0.0001) and higher PRS (Cohen's d = 0.63, p < 0.0001) than non-smokers. A higher PRS was associated with increased amount of smoking, mediated by BAG and tGMV. Several neurotransmitters and ion channel pathways were enriched in the group of smoking-related genes involved in addiction, brain synaptic plasticity, and some neurological disorders. CONCLUSION By using a simplified single indicator of the entire brain (BAG) in combination with the PRS, this study highlights the greater BAG in smokers and its linkage with genes and smoking behavior, providing insight into the neurobiological underpinnings and potential features of smoking-related aging.
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Affiliation(s)
- Zeyu Yang
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Zeqiang Linli
- School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou, 510006, P.R.China
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Jianfeng Feng
- Centre for Computational Systems Biology, Fudan University, Shanghai 200433, P.R.China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, England
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33
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Du X, Yan Y, Yu J, Zhu T, Huang CC, Zhang L, Shan X, Li R, Dai Y, Lv H, Zhang XY, Feng J, Li WG, Luo Q, Li F. SH2B1 Tunes Hippocampal ERK Signaling to Influence Fluid Intelligence in Humans and Mice. Research (Wash D C) 2023; 6:0269. [PMID: 38434247 PMCID: PMC10907025 DOI: 10.34133/research.0269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 10/19/2023] [Indexed: 03/05/2024]
Abstract
Fluid intelligence is a cognitive domain that encompasses general reasoning, pattern recognition, and problem-solving abilities independent of task-specific experience. Understanding its genetic and neural underpinnings is critical yet challenging for predicting human development, lifelong health, and well-being. One approach to address this challenge is to map the network of correlations between intelligence and other constructs. In the current study, we performed a genome-wide association study using fluid intelligence quotient scores from the UK Biobank to explore the genetic architecture of the associations between obesity risk and fluid intelligence. Our results revealed novel common genetic loci (SH2B1, TUFM, ATP2A1, and FOXO3) underlying the association between fluid intelligence and body metabolism. Surprisingly, we demonstrated that SH2B1 variation influenced fluid intelligence independently of its effects on metabolism but partially mediated its association with bilateral hippocampal volume. Consistently, selective genetic ablation of Sh2b1 in the mouse hippocampus, particularly in inhibitory neurons, but not in excitatory neurons, significantly impaired working memory, short-term novel object recognition memory, and behavioral flexibility, but not spatial learning and memory, mirroring the human intellectual performance. Single-cell genetic profiling of Sh2B1-regulated molecular pathways revealed that Sh2b1 deletion resulted in aberrantly enhanced extracellular signal-regulated kinase (ERK) signaling, whereas pharmacological inhibition of ERK signaling reversed the associated behavioral impairment. Our cross-species study thus provides unprecedented insight into the role of SH2B1 in fluid intelligence and has implications for understanding the genetic and neural underpinnings of lifelong mental health and well-being.
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Affiliation(s)
- Xiujuan Du
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence,
Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenom Institute,
Fudan University, Shanghai 200032, China
| | - Yuhua Yan
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education),
School of Life Sciences, East China Normal University, Shanghai 200062, China
- Department of Rehabilitation Medicine, Huashan Hospital, Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science,
Fudan University, Shanghai 200032, China
| | - Juehua Yu
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
- Center for Experimental Studies and Research,
The First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - Tailin Zhu
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education),
School of Life Sciences, East China Normal University, Shanghai 200062, China
- Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China
| | - Chu-Chung Huang
- Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science,
East China Normal University, Shanghai 200062, China
| | - Lingli Zhang
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
| | - Xingyue Shan
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education),
School of Life Sciences, East China Normal University, Shanghai 200062, China
| | - Ren Li
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence,
Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenom Institute,
Fudan University, Shanghai 200032, China
| | - Yuan Dai
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
| | - Hui Lv
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
| | - Xiao-Yong Zhang
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence,
Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenom Institute,
Fudan University, Shanghai 200032, China
| | - Jianfeng Feng
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence,
Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenom Institute,
Fudan University, Shanghai 200032, China
| | - Wei-Guang Li
- Department of Rehabilitation Medicine, Huashan Hospital, Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science,
Fudan University, Shanghai 200032, China
| | - Qiang Luo
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence,
Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenom Institute,
Fudan University, Shanghai 200032, China
| | - Fei Li
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
- Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China
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Qin Y, Zhang XY, Liu Y, Ma Z, Tao S, Li Y, Peng R, Wang F, Wang J, Feng J, Qiu Z, Jin L, Wang H, Gong X. Downregulation of mGluR1-mediated signaling underlying autistic-like core symptoms in Shank1 P1812L-knock-in mice. Transl Psychiatry 2023; 13:329. [PMID: 37880287 PMCID: PMC10600164 DOI: 10.1038/s41398-023-02626-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/16/2023] [Accepted: 10/06/2023] [Indexed: 10/27/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by core symptoms that consist of social deficits and repetitive behaviors. Unfortunately, no effective medication is available thus far to target the core symptoms of ASD, since the pathogenesis remains largely unknown. To investigate the pathogenesis of the core symptoms in ASD, we constructed Shank1 P1812L-knock-in (KI) mice corresponding to a recurrent ASD-related mutation, SHANK1 P1806L, to achieve construct validity and face validity. Shank1 P1812L-KI heterozygous (HET) mice presented with social deficits and repetitive behaviors without the presence of confounding comorbidities. HET mice also exhibited downregulation of metabotropic glutamate receptor (mGluR1) and associated signals, along with structural abnormalities in the dendritic spines and postsynaptic densities. Combined with findings from Shank1 R882H-KI mice, our study confirms that mGluR1-mediated signaling dysfunction is a pivotal mechanism underlying the core symptoms of ASD. Interestingly, Shank1 P1812L-KI homozygous (HOM) mice manifested behavioral signs of impaired long-term memory rather than autistic-like core traits; thus, their phenotype was markedly different from that of Shank1 P1812L-KI HET mice. Correspondingly, at the molecular level, Shank1 P1812L-KI HOM displayed upregulation of AMPA receptor (GluA2)-related signals. The different patterns of protein changes in HOM and HET mice may explain the differences in behaviors. Our study emphasizes the universality of mGluR1-signaling hypofunction in the pathogenesis of the core symptoms in ASD, providing a potential target for therapeutic drugs. The precise correspondence between genotype and phenotype, as shown in HOM and HET mice, indicates the importance of reproducing disease-related genotypes in mouse models.
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Affiliation(s)
- Yue Qin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Xiao-Yong Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yanyan Liu
- School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei, China
- Institute of Integrated Chinese and Western Medicine, Anhui Academy of Chinese Medicine, Hefei, China
| | - Zehan Ma
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Shuo Tao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Ying Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Rui Peng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Fei Wang
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Jianfeng Feng
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei, China
| | - Zilong Qiu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Hongyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
| | - Xiaohong Gong
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
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35
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Aguilar M, Ambrosi G, Anderson H, Arruda L, Attig N, Bagwell C, Barao F, Barbanera M, Barrin L, Bartoloni A, Battiston R, Belyaev N, Berdugo J, Bertucci B, Bindi V, Bollweg K, Bolster J, Borchiellini M, Borgia B, Boschini MJ, Bourquin M, Burger J, Burger WJ, Cai XD, Capell M, Casaus J, Castellini G, Cervelli F, Chang YH, Chen GM, Chen GR, Chen H, Chen HS, Chen Y, Cheng L, Chou HY, Chouridou S, Choutko V, Chung CH, Clark C, Coignet G, Consolandi C, Contin A, Corti C, Cui Z, Dadzie K, D'Angelo F, Dass A, Delgado C, Della Torre S, Demirköz MB, Derome L, Di Falco S, Di Felice V, Díaz C, Dimiccoli F, von Doetinchem P, Dong F, Donnini F, Duranti M, Egorov A, Eline A, Faldi F, Feng J, Fiandrini E, Fisher P, Formato V, Gámez C, García-López RJ, Gargiulo C, Gast H, Gervasi M, Giovacchini F, Gómez-Coral DM, Gong J, Goy C, Grandi D, Graziani M, Guracho AN, Haino S, Han KC, Hashmani RK, He ZH, Heber B, Hsieh TH, Hu JY, Huang BW, Ionica M, Incagli M, Jia Y, Jinchi H, Karagöz G, Khan S, Khiali B, Kirn T, Klipfel AP, Kounina O, Kounine A, Koutsenko V, Krasnopevtsev D, Kuhlman A, Kulemzin A, La Vacca G, Laudi E, Laurenti G, LaVecchia G, Lazzizzera I, Lee HT, Lee SC, Li HL, Li JQ, Li M, Li M, Li Q, Li Q, Li QY, Li S, Li SL, Li JH, Li ZH, Liang J, Liang MJ, Lin CH, Lippert T, Liu JH, Lu SQ, Lu YS, Luebelsmeyer K, Luo JZ, Luo SD, Luo X, Mañá C, Marín J, Marquardt J, Martin T, Martínez G, Masi N, Maurin D, Medvedeva T, Menchaca-Rocha A, Meng Q, Molero M, Mott P, Mussolin L, Jozani YN, Negrete J, Nicolaidis R, Nikonov N, Nozzoli F, Ocampo-Peleteiro J, Oliva A, Orcinha M, Ottupara MA, Palermo M, Palmonari F, Paniccia M, Pashnin A, Pauluzzi M, Pensotti S, Plyaskin V, Poluianov S, Qin X, Qu ZY, Quadrani L, Rancoita PG, Rapin D, Conde AR, Robyn E, Rodríguez-García I, Romaneehsen L, Rossi F, Rozhkov A, Rozza D, Sagdeev R, Savin E, Schael S, von Dratzig AS, Schwering G, Seo ES, Shan BS, Siedenburg T, Silvestre G, Song JW, Song XJ, Sonnabend R, Strigari L, Su T, Sun Q, Sun ZT, Tacconi M, Tang XW, Tang ZC, Tian J, Tian Y, Ting SCC, Ting SM, Tomassetti N, Torsti J, Urban T, Usoskin I, Vagelli V, Vainio R, Valencia-Otero M, Valente E, Valtonen E, Vázquez Acosta M, Vecchi M, Velasco M, Vialle JP, Wang CX, Wang L, Wang LQ, Wang NH, Wang QL, Wang S, Wang X, Wang Y, Wang ZM, Wei J, Weng ZL, Wu H, Wu Y, Xiao JN, Xiong RQ, Xiong XZ, Xu W, Yan Q, Yang HT, Yang Y, Yelland A, Yi H, You YH, Yu YM, Yu ZQ, Zhang C, Zhang F, Zhang FZ, Zhang J, Zhang JH, Zhang Z, Zhao F, Zheng C, Zheng ZM, Zhuang HL, Zhukov V, Zichichi A, Zuccon P. Temporal Structures in Positron Spectra and Charge-Sign Effects in Galactic Cosmic Rays. Phys Rev Lett 2023; 131:151002. [PMID: 37897756 DOI: 10.1103/physrevlett.131.151002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 08/26/2023] [Accepted: 09/01/2023] [Indexed: 10/30/2023]
Abstract
We present the precision measurements of 11 years of daily cosmic positron fluxes in the rigidity range from 1.00 to 41.9 GV based on 3.4×10^{6} positrons collected with the Alpha Magnetic Spectrometer (AMS) aboard the International Space Station. The positron fluxes show distinctly different time variations from the electron fluxes at short and long timescales. A hysteresis between the electron fluxes and the positron fluxes is observed with a significance greater than 5σ at rigidities below 8.5 GV. On the contrary, the positron fluxes and the proton fluxes show similar time variation. Remarkably, we found that positron fluxes are modulated more than proton fluxes with a significance greater than 5σ for rigidities below 7 GV. These continuous daily positron fluxes, together with AMS daily electron, proton, and helium fluxes over an 11-year solar cycle, provide unique input to the understanding of both the charge-sign and mass dependencies of cosmic rays in the heliosphere.
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Affiliation(s)
- M Aguilar
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
| | - G Ambrosi
- INFN Sezione di Perugia, 06100 Perugia, Italy
| | - H Anderson
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - L Arruda
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), 1649-003 Lisboa, Portugal
| | - N Attig
- Jülich Supercomputing Centre and JARA-FAME, Research Centre Jülich, 52425 Jülich, Germany
| | - C Bagwell
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - F Barao
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), 1649-003 Lisboa, Portugal
| | - M Barbanera
- INFN Sezione di Perugia, 06100 Perugia, Italy
| | - L Barrin
- European Organization for Nuclear Research (CERN), 1211 Geneva 23, Switzerland
| | | | - R Battiston
- INFN TIFPA, 38123 Trento, Italy
- Università di Trento, 38123 Trento, Italy
| | - N Belyaev
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - J Berdugo
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
| | - B Bertucci
- INFN Sezione di Perugia, 06100 Perugia, Italy
- Università di Perugia, 06100 Perugia, Italy
| | - V Bindi
- Physics and Astronomy Department, University of Hawaii, Honolulu, Hawaii 96822, USA
| | - K Bollweg
- National Aeronautics and Space Administration Johnson Space Center (JSC), Houston, Texas 77058, USA
| | - J Bolster
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - M Borchiellini
- Kapteyn Astronomical Institute, University of Groningen, P.O. Box 800, 9700 AV Groningen, Netherlands
| | - B Borgia
- INFN Sezione di Roma 1, 00185 Roma, Italy
- Università di Roma La Sapienza, 00185 Roma, Italy
| | - M J Boschini
- INFN Sezione di Milano-Bicocca, 20126 Milano, Italy
| | - M Bourquin
- DPNC, Université de Genève, 1211 Genève 4, Switzerland
| | - J Burger
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | | | - X D Cai
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - M Capell
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - J Casaus
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
| | | | | | - Y H Chang
- Institute of Physics, Academia Sinica, Nankang, Taipei 11529, Taiwan
| | - G M Chen
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
| | - G R Chen
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - H Chen
- Zhejiang University (ZJU), Hangzhou 310058, China
| | - H S Chen
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
| | - Y Chen
- DPNC, Université de Genève, 1211 Genève 4, Switzerland
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - L Cheng
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - H Y Chou
- Institute of Physics, Academia Sinica, Nankang, Taipei 11529, Taiwan
| | - S Chouridou
- I. Physics Institute and JARA-FAME, RWTH Aachen University, 52056 Aachen, Germany
| | - V Choutko
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - C H Chung
- I. Physics Institute and JARA-FAME, RWTH Aachen University, 52056 Aachen, Germany
| | - C Clark
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
- National Aeronautics and Space Administration Johnson Space Center (JSC), Houston, Texas 77058, USA
| | - G Coignet
- Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LAPP-IN2P3, 74000 Annecy, France
| | - C Consolandi
- Physics and Astronomy Department, University of Hawaii, Honolulu, Hawaii 96822, USA
| | - A Contin
- INFN Sezione di Bologna, 40126 Bologna, Italy
- Università di Bologna, 40126 Bologna, Italy
| | - C Corti
- Physics and Astronomy Department, University of Hawaii, Honolulu, Hawaii 96822, USA
| | - Z Cui
- Shandong University (SDU), Jinan, Shandong 250100, China
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - K Dadzie
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - F D'Angelo
- INFN Sezione di Bologna, 40126 Bologna, Italy
- Università di Bologna, 40126 Bologna, Italy
| | - A Dass
- INFN TIFPA, 38123 Trento, Italy
- Università di Trento, 38123 Trento, Italy
| | - C Delgado
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
| | | | - M B Demirköz
- Department of Physics, Middle East Technical University (METU), 06800 Ankara, Türkiye
| | - L Derome
- Université Grenoble Alpes, CNRS, Grenoble INP, LPSC-IN2P3, 38000 Grenoble, France
| | | | - V Di Felice
- INFN Sezione di Roma Tor Vergata, 00133 Roma, Italy
| | - C Díaz
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
| | | | - P von Doetinchem
- Physics and Astronomy Department, University of Hawaii, Honolulu, Hawaii 96822, USA
| | - F Dong
- Southeast University (SEU), Nanjing 210096, China
| | - F Donnini
- INFN Sezione di Perugia, 06100 Perugia, Italy
| | - M Duranti
- INFN Sezione di Perugia, 06100 Perugia, Italy
| | - A Egorov
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - A Eline
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - F Faldi
- INFN Sezione di Perugia, 06100 Perugia, Italy
- Università di Perugia, 06100 Perugia, Italy
| | - J Feng
- Sun Yat-Sen University (SYSU), Guangzhou 510275, China
| | - E Fiandrini
- INFN Sezione di Perugia, 06100 Perugia, Italy
- Università di Perugia, 06100 Perugia, Italy
| | - P Fisher
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - V Formato
- INFN Sezione di Roma Tor Vergata, 00133 Roma, Italy
| | - C Gámez
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
| | - R J García-López
- Instituto de Astrofísica de Canarias (IAC), 38205 La Laguna, and Departamento de Astrofísica, Universidad de La Laguna, 38206 La Laguna, Tenerife, Spain
| | - C Gargiulo
- European Organization for Nuclear Research (CERN), 1211 Geneva 23, Switzerland
| | - H Gast
- I. Physics Institute and JARA-FAME, RWTH Aachen University, 52056 Aachen, Germany
| | - M Gervasi
- INFN Sezione di Milano-Bicocca, 20126 Milano, Italy
- Università di Milano-Bicocca, 20126 Milano, Italy
| | - F Giovacchini
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
| | - D M Gómez-Coral
- Instituto de Física, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, 01000 Mexico
| | - J Gong
- Southeast University (SEU), Nanjing 210096, China
| | - C Goy
- Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LAPP-IN2P3, 74000 Annecy, France
| | - D Grandi
- INFN Sezione di Milano-Bicocca, 20126 Milano, Italy
- Università di Milano-Bicocca, 20126 Milano, Italy
| | - M Graziani
- INFN Sezione di Perugia, 06100 Perugia, Italy
- Università di Perugia, 06100 Perugia, Italy
| | | | - S Haino
- Institute of Physics, Academia Sinica, Nankang, Taipei 11529, Taiwan
| | - K C Han
- National Chung-Shan Institute of Science and Technology (NCSIST), Longtan, Tao Yuan 32546, Taiwan
| | - R K Hashmani
- Department of Physics, Middle East Technical University (METU), 06800 Ankara, Türkiye
| | - Z H He
- Sun Yat-Sen University (SYSU), Guangzhou 510275, China
| | - B Heber
- Institut für Experimentelle und Angewandte Physik, Christian-Alberts-Universität zu Kiel, 24118 Kiel, Germany
| | - T H Hsieh
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - J Y Hu
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
| | - B W Huang
- Zhejiang University (ZJU), Hangzhou 310058, China
| | - M Ionica
- INFN Sezione di Perugia, 06100 Perugia, Italy
| | - M Incagli
- INFN Sezione di Pisa, 56100 Pisa, Italy
| | - Yi Jia
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - H Jinchi
- National Chung-Shan Institute of Science and Technology (NCSIST), Longtan, Tao Yuan 32546, Taiwan
| | - G Karagöz
- Department of Physics, Middle East Technical University (METU), 06800 Ankara, Türkiye
| | - S Khan
- DPNC, Université de Genève, 1211 Genève 4, Switzerland
| | - B Khiali
- INFN Sezione di Roma Tor Vergata, 00133 Roma, Italy
| | - Th Kirn
- I. Physics Institute and JARA-FAME, RWTH Aachen University, 52056 Aachen, Germany
| | - A P Klipfel
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - O Kounina
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - A Kounine
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - V Koutsenko
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - D Krasnopevtsev
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - A Kuhlman
- Physics and Astronomy Department, University of Hawaii, Honolulu, Hawaii 96822, USA
| | - A Kulemzin
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - G La Vacca
- INFN Sezione di Milano-Bicocca, 20126 Milano, Italy
- Università di Milano-Bicocca, 20126 Milano, Italy
| | - E Laudi
- European Organization for Nuclear Research (CERN), 1211 Geneva 23, Switzerland
| | - G Laurenti
- INFN Sezione di Bologna, 40126 Bologna, Italy
| | - G LaVecchia
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - I Lazzizzera
- INFN TIFPA, 38123 Trento, Italy
- Università di Trento, 38123 Trento, Italy
| | - H T Lee
- Academia Sinica Grid Center (ASGC), Nankang, Taipei 11529, Taiwan
| | - S C Lee
- Institute of Physics, Academia Sinica, Nankang, Taipei 11529, Taiwan
| | - H L Li
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - J Q Li
- Southeast University (SEU), Nanjing 210096, China
| | - M Li
- DPNC, Université de Genève, 1211 Genève 4, Switzerland
| | - M Li
- Shandong University (SDU), Jinan, Shandong 250100, China
| | - Q Li
- Southeast University (SEU), Nanjing 210096, China
| | - Q Li
- Shandong University (SDU), Jinan, Shandong 250100, China
| | - Q Y Li
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - S Li
- I. Physics Institute and JARA-FAME, RWTH Aachen University, 52056 Aachen, Germany
| | - S L Li
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
| | - J H Li
- Shandong University (SDU), Jinan, Shandong 250100, China
| | - Z H Li
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
| | - J Liang
- Shandong University (SDU), Jinan, Shandong 250100, China
| | - M J Liang
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
| | - C H Lin
- Institute of Physics, Academia Sinica, Nankang, Taipei 11529, Taiwan
| | - T Lippert
- Jülich Supercomputing Centre and JARA-FAME, Research Centre Jülich, 52425 Jülich, Germany
| | - J H Liu
- Institute of Electrical Engineering (IEE), Chinese Academy of Sciences, Beijing 100190, China
| | - S Q Lu
- Institute of Physics, Academia Sinica, Nankang, Taipei 11529, Taiwan
| | - Y S Lu
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
| | - K Luebelsmeyer
- I. Physics Institute and JARA-FAME, RWTH Aachen University, 52056 Aachen, Germany
| | - J Z Luo
- Southeast University (SEU), Nanjing 210096, China
| | - S D Luo
- Zhejiang University (ZJU), Hangzhou 310058, China
| | - Xi Luo
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - C Mañá
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
| | - J Marín
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
| | - J Marquardt
- Institut für Experimentelle und Angewandte Physik, Christian-Alberts-Universität zu Kiel, 24118 Kiel, Germany
| | - T Martin
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
- National Aeronautics and Space Administration Johnson Space Center (JSC), Houston, Texas 77058, USA
| | - G Martínez
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
| | - N Masi
- INFN Sezione di Bologna, 40126 Bologna, Italy
| | - D Maurin
- Université Grenoble Alpes, CNRS, Grenoble INP, LPSC-IN2P3, 38000 Grenoble, France
| | - T Medvedeva
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - A Menchaca-Rocha
- Instituto de Física, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, 01000 Mexico
| | - Q Meng
- Southeast University (SEU), Nanjing 210096, China
| | - M Molero
- Instituto de Astrofísica de Canarias (IAC), 38205 La Laguna, and Departamento de Astrofísica, Universidad de La Laguna, 38206 La Laguna, Tenerife, Spain
| | - P Mott
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
- National Aeronautics and Space Administration Johnson Space Center (JSC), Houston, Texas 77058, USA
| | - L Mussolin
- INFN Sezione di Perugia, 06100 Perugia, Italy
- Università di Perugia, 06100 Perugia, Italy
| | - Y Najafi Jozani
- I. Physics Institute and JARA-FAME, RWTH Aachen University, 52056 Aachen, Germany
| | - J Negrete
- Physics and Astronomy Department, University of Hawaii, Honolulu, Hawaii 96822, USA
| | - R Nicolaidis
- INFN TIFPA, 38123 Trento, Italy
- Università di Trento, 38123 Trento, Italy
| | - N Nikonov
- Physics and Astronomy Department, University of Hawaii, Honolulu, Hawaii 96822, USA
| | | | - J Ocampo-Peleteiro
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
| | - A Oliva
- INFN Sezione di Bologna, 40126 Bologna, Italy
| | - M Orcinha
- Laboratório de Instrumentação e Física Experimental de Partículas (LIP), 1649-003 Lisboa, Portugal
| | - M A Ottupara
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - M Palermo
- Physics and Astronomy Department, University of Hawaii, Honolulu, Hawaii 96822, USA
| | - F Palmonari
- INFN Sezione di Bologna, 40126 Bologna, Italy
- Università di Bologna, 40126 Bologna, Italy
| | - M Paniccia
- DPNC, Université de Genève, 1211 Genève 4, Switzerland
| | - A Pashnin
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - M Pauluzzi
- INFN Sezione di Perugia, 06100 Perugia, Italy
- Università di Perugia, 06100 Perugia, Italy
| | - S Pensotti
- INFN Sezione di Milano-Bicocca, 20126 Milano, Italy
- Università di Milano-Bicocca, 20126 Milano, Italy
| | - V Plyaskin
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - S Poluianov
- Sodankylä Geophysical Observatory and Space Physics and Astronomy Research Unit, University of Oulu, 90014 Oulu, Finland
| | - X Qin
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - Z Y Qu
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - L Quadrani
- INFN Sezione di Bologna, 40126 Bologna, Italy
- Università di Bologna, 40126 Bologna, Italy
| | - P G Rancoita
- INFN Sezione di Milano-Bicocca, 20126 Milano, Italy
| | - D Rapin
- DPNC, Université de Genève, 1211 Genève 4, Switzerland
| | | | - E Robyn
- DPNC, Université de Genève, 1211 Genève 4, Switzerland
| | - I Rodríguez-García
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
| | - L Romaneehsen
- Institut für Experimentelle und Angewandte Physik, Christian-Alberts-Universität zu Kiel, 24118 Kiel, Germany
| | - F Rossi
- INFN TIFPA, 38123 Trento, Italy
- Università di Trento, 38123 Trento, Italy
| | - A Rozhkov
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - D Rozza
- INFN Sezione di Milano-Bicocca, 20126 Milano, Italy
| | - R Sagdeev
- East-West Center for Space Science, University of Maryland, College Park, Maryland 20742, USA
| | - E Savin
- INFN Sezione di Bologna, 40126 Bologna, Italy
- Università di Bologna, 40126 Bologna, Italy
| | - S Schael
- I. Physics Institute and JARA-FAME, RWTH Aachen University, 52056 Aachen, Germany
| | | | - G Schwering
- I. Physics Institute and JARA-FAME, RWTH Aachen University, 52056 Aachen, Germany
| | - E S Seo
- IPST, University of Maryland, College Park, Maryland 20742, USA
| | - B S Shan
- Beihang University (BUAA), Beijing 100191, China
| | - T Siedenburg
- I. Physics Institute and JARA-FAME, RWTH Aachen University, 52056 Aachen, Germany
| | - G Silvestre
- INFN Sezione di Perugia, 06100 Perugia, Italy
| | - J W Song
- Shandong University (SDU), Jinan, Shandong 250100, China
| | - X J Song
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - R Sonnabend
- I. Physics Institute and JARA-FAME, RWTH Aachen University, 52056 Aachen, Germany
| | - L Strigari
- INFN Sezione di Roma 1, 00185 Roma, Italy
| | - T Su
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - Q Sun
- Shandong University (SDU), Jinan, Shandong 250100, China
| | - Z T Sun
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
| | - M Tacconi
- INFN Sezione di Milano-Bicocca, 20126 Milano, Italy
- Università di Milano-Bicocca, 20126 Milano, Italy
| | - X W Tang
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
| | - Z C Tang
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
| | - J Tian
- INFN Sezione di Roma Tor Vergata, 00133 Roma, Italy
| | - Y Tian
- Zhejiang University (ZJU), Hangzhou 310058, China
| | - Samuel C C Ting
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
- European Organization for Nuclear Research (CERN), 1211 Geneva 23, Switzerland
| | - S M Ting
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - N Tomassetti
- INFN Sezione di Perugia, 06100 Perugia, Italy
- Università di Perugia, 06100 Perugia, Italy
| | - J Torsti
- Space Research Laboratory, Department of Physics and Astronomy, University of Turku, 20014 Turku, Finland
| | - T Urban
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
- National Aeronautics and Space Administration Johnson Space Center (JSC), Houston, Texas 77058, USA
| | - I Usoskin
- Sodankylä Geophysical Observatory and Space Physics and Astronomy Research Unit, University of Oulu, 90014 Oulu, Finland
| | - V Vagelli
- INFN Sezione di Perugia, 06100 Perugia, Italy
- Agenzia Spaziale Italiana (ASI), 00133 Roma, Italy
| | - R Vainio
- Space Research Laboratory, Department of Physics and Astronomy, University of Turku, 20014 Turku, Finland
| | - M Valencia-Otero
- Physics Department and Center for High Energy and High Field Physics, National Central University (NCU), Tao Yuan 32054, Taiwan
| | - E Valente
- INFN Sezione di Roma 1, 00185 Roma, Italy
- Università di Roma La Sapienza, 00185 Roma, Italy
| | - E Valtonen
- Space Research Laboratory, Department of Physics and Astronomy, University of Turku, 20014 Turku, Finland
| | - M Vázquez Acosta
- Instituto de Astrofísica de Canarias (IAC), 38205 La Laguna, and Departamento de Astrofísica, Universidad de La Laguna, 38206 La Laguna, Tenerife, Spain
| | - M Vecchi
- Kapteyn Astronomical Institute, University of Groningen, P.O. Box 800, 9700 AV Groningen, Netherlands
| | - M Velasco
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
| | - J P Vialle
- Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, LAPP-IN2P3, 74000 Annecy, France
| | - C X Wang
- Shandong University (SDU), Jinan, Shandong 250100, China
| | - L Wang
- Institute of Electrical Engineering (IEE), Chinese Academy of Sciences, Beijing 100190, China
| | - L Q Wang
- Shandong University (SDU), Jinan, Shandong 250100, China
| | - N H Wang
- Shandong University (SDU), Jinan, Shandong 250100, China
| | - Q L Wang
- Institute of Electrical Engineering (IEE), Chinese Academy of Sciences, Beijing 100190, China
| | - S Wang
- Physics and Astronomy Department, University of Hawaii, Honolulu, Hawaii 96822, USA
| | - X Wang
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - Yu Wang
- Shandong University (SDU), Jinan, Shandong 250100, China
| | - Z M Wang
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - J Wei
- DPNC, Université de Genève, 1211 Genève 4, Switzerland
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - Z L Weng
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - H Wu
- Southeast University (SEU), Nanjing 210096, China
| | - Y Wu
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - J N Xiao
- Zhejiang University (ZJU), Hangzhou 310058, China
| | - R Q Xiong
- Southeast University (SEU), Nanjing 210096, China
| | - X Z Xiong
- Zhejiang University (ZJU), Hangzhou 310058, China
| | - W Xu
- Shandong University (SDU), Jinan, Shandong 250100, China
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - Q Yan
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - H T Yang
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
| | - Y Yang
- National Cheng Kung University, Tainan 70101, Taiwan
| | - A Yelland
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - H Yi
- Southeast University (SEU), Nanjing 210096, China
| | - Y H You
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
| | - Y M Yu
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - Z Q Yu
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
| | - C Zhang
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
| | - F Zhang
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
| | - F Z Zhang
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
| | - J Zhang
- Shandong University (SDU), Jinan, Shandong 250100, China
| | - J H Zhang
- Southeast University (SEU), Nanjing 210096, China
| | - Z Zhang
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - F Zhao
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
| | - C Zheng
- Shandong Institute of Advanced Technology (SDIAT), Jinan, Shandong 250100, China
| | - Z M Zheng
- Beihang University (BUAA), Beijing 100191, China
| | - H L Zhuang
- Institute of High Energy Physics (IHEP), Chinese Academy of Sciences, Beijing 100049, China
| | - V Zhukov
- I. Physics Institute and JARA-FAME, RWTH Aachen University, 52056 Aachen, Germany
| | - A Zichichi
- INFN Sezione di Bologna, 40126 Bologna, Italy
- Università di Bologna, 40126 Bologna, Italy
| | - P Zuccon
- INFN TIFPA, 38123 Trento, Italy
- Università di Trento, 38123 Trento, Italy
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Abe K, Hayato Y, Hiraide K, Ieki K, Ikeda M, Kameda J, Kanemura Y, Kaneshima R, Kashiwagi Y, Kataoka Y, Miki S, Mine S, Miura M, Moriyama S, Nakano Y, Nakahata M, Nakayama S, Noguchi Y, Okamoto K, Sato K, Sekiya H, Shiba H, Shimizu K, Shiozawa M, Sonoda Y, Suzuki Y, Takeda A, Takemoto Y, Takenaka A, Tanaka H, Watanabe S, Yano T, Han S, Kajita T, Okumura K, Tashiro T, Tomiya T, Wang X, Xia J, Yoshida S, Megias GD, Fernandez P, Labarga L, Ospina N, Zaldivar B, Pointon BW, Kearns E, Raaf JL, Wan L, Wester T, Bian J, Griskevich NJ, Kropp WR, Locke S, Smy MB, Sobel HW, Takhistov V, Yankelevich A, Hill J, Park RG, Bodur B, Scholberg K, Walter CW, Bernard L, Coffani A, Drapier O, El Hedri S, Giampaolo A, Mueller TA, Santos AD, Paganini P, Quilain B, Ishizuka T, Nakamura T, Jang JS, Learned JG, Choi K, Cao S, Anthony LHV, Martin D, Scott M, Sztuc AA, Uchida Y, Berardi V, Catanesi MG, Radicioni E, Calabria NF, Machado LN, De Rosa G, Collazuol G, Iacob F, Lamoureux M, Mattiazzi M, Ludovici L, Gonin M, Pronost G, Fujisawa C, Maekawa Y, Nishimura Y, Friend M, Hasegawa T, Ishida T, Kobayashi T, Jakkapu M, Matsubara T, Nakadaira T, Nakamura K, Oyama Y, Sakashita K, Sekiguchi T, Tsukamoto T, Boschi T, Di Lodovico F, Gao J, Goldsack A, Katori T, Migenda J, Taani M, Zsoldos S, Kotsar Y, Ozaki H, Suzuki AT, Takeuchi Y, Bronner C, Feng J, Kikawa T, Mori M, Nakaya T, Wendell RA, Yasutome K, Jenkins SJ, McCauley N, Mehta P, Tsui KM, Fukuda Y, Itow Y, Menjo H, Ninomiya K, Lagoda J, Lakshmi SM, Mandal M, Mijakowski P, Prabhu YS, Zalipska J, Jia M, Jiang J, Jung CK, Wilking MJ, Yanagisawa C, Harada M, Ishino H, Ito S, Kitagawa H, Koshio Y, Nakanishi F, Sakai S, Barr G, Barrow D, Cook L, Samani S, Wark D, Nova F, Yang JY, Malek M, McElwee JM, Stone O, Thiesse MD, Thompson LF, Okazawa H, Kim SB, Seo JW, Yu I, Ichikawa AK, Nakamura KD, Tairafune S, Nishijima K, Iwamoto K, Nakagiri K, Nakajima Y, Taniuchi N, Yokoyama M, Martens K, de Perio P, Vagins MR, Kuze M, Izumiyama S, Inomoto M, Ishitsuka M, Ito H, Kinoshita T, Matsumoto R, Ommura Y, Shigeta N, Shinoki M, Suganuma T, Yamauchi K, Martin JF, Tanaka HA, Towstego T, Akutsu R, Gousy-Leblanc V, Hartz M, Konaka A, Prouse NW, Chen S, Xu BD, Zhang B, Posiadala-Zezula M, Hadley D, Nicholson M, O'Flaherty M, Richards B, Ali A, Jamieson B, Marti L, Minamino A, Pintaudi G, Sano S, Suzuki S, Wada K. Erratum: Search for Cosmic-Ray Boosted Sub-GeV Dark Matter Using Recoil Protons at Super-Kamiokande [Phys. Rev. Lett. 130, 031802 (2023)]. Phys Rev Lett 2023; 131:159903. [PMID: 37897794 DOI: 10.1103/physrevlett.131.159903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Indexed: 10/30/2023]
Abstract
This corrects the article DOI: 10.1103/PhysRevLett.130.031802.
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37
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Jiang Y, Luo C, Wang J, Palaniyappan L, Chang X, Xiang S, Zhang J, Duan M, Huang H, Gaser C, Nemoto K, Miura K, Hashimoto R, Westlye LT, Richard G, Fernandez-Cabello S, Parker N, Andreassen OA, Kircher T, Nenadić I, Stein F, Thomas-Odenthal F, Teutenberg L, Usemann P, Dannlowski U, Hahn T, Grotegerd D, Meinert S, Lencer R, Tang Y, Zhang T, Li C, Yue W, Zhang Y, Yu X, Zhou E, Lin CP, Tsai SJ, Rodrigue AL, Glahn D, Pearlson G, Blangero J, Karuk A, Pomarol-Clotet E, Salvador R, Fuentes-Claramonte P, Garcia-León MÁ, Spalletta G, Piras F, Vecchio D, Banaj N, Cheng J, Liu Z, Yang J, Gonul AS, Uslu O, Burhanoglu BB, Demir AU, Rootes-Murdy K, Calhoun VD, Sim K, Green M, Quidé Y, Chung YC, Kim WS, Sponheim SR, Demro C, Ramsay IS, Iasevoli F, de Bartolomeis A, Barone A, Ciccarelli M, Brunetti A, Cocozza S, Pontillo G, Tranfa M, Park MTM, Kirschner M, Georgiadis F, Kaiser S, Rheenen TEV, Rossell SL, Hughes M, Woods W, Carruthers SP, Sumner P, Ringin E, Spaniel F, Skoch A, Tomecek D, Homan P, Homan S, Omlor W, Cecere G, Nguyen DD, Preda A, Thomopoulos S, Jahanshad N, Cui LB, Yao D, Thompson PM, Turner JA, van Erp TG, Cheng W, Feng J. Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia. medRxiv 2023:2023.10.11.23296862. [PMID: 37873296 PMCID: PMC10593004 DOI: 10.1101/2023.10.11.23296862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.
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Affiliation(s)
- Yuchao Jiang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, Canada
| | - Xiao Chang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Jie Zhang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Department of Neurology, Jena University Hospital, Jena, Germany
- German Center for Mental Health (DZPG), Site Jena-Magdeburg-Halle, Germany
| | - Kiyotaka Nemoto
- Department of Psychiatry, Division of Clinical Medicine, Institute of Medicine, University of Tsukuba, Tsukuba, 305-8575, Japan
| | - Kenichiro Miura
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 187-8553, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 187-8553, Japan
| | - Lars T. Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Genevieve Richard
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Sara Fernandez-Cabello
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Nadine Parker
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Lea Teutenberg
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry and Psychotherapie and Center for Brain, Behavior and Metabolism, Lübeck University, Lübeck, Germany
- Institute for Transnational Psychiatry and Otto Creutzfeldt Center for Behavioral and Cognitive Neuroscience, University of Münster, Münster, Germany
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weihua Yue
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, PR China
- Chinese Institute for Brain Research, Beijing, PR China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, PR China
| | - Yuyanan Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, PR China
| | - Xin Yu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, PR China
| | - Enpeng Zhou
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, PR China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Amanda L. Rodrigue
- Department of Psychiatry, Boston Children’s Hospital, Harvard Medical School, Boston MA, USA
| | - David Glahn
- Department of Psychiatry, Boston Children’s Hospital, Harvard Medical School, Boston MA, USA
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas of the Rio Grande Valley, Brownsville, TX, USA
| | - Andriana Karuk
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona 08035, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona 08035, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Spain
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona 08035, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Spain
| | - Paola Fuentes-Claramonte
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona 08035, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Spain
| | - María Ángeles Garcia-León
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona 08035, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Spain
| | - Gianfranco Spalletta
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Fabrizio Piras
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Daniela Vecchio
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Nerisa Banaj
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhening Liu
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China
| | - Jie Yang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China
| | - Ali Saffet Gonul
- Ege University School of Medicine Department of Psychiatry, SoCAT Lab, Izmir, Turkey
| | - Ozgul Uslu
- Ege University Institute of Health Sciences Department of Neuroscience, Izmir, Turkey
| | | | - Aslihan Uyar Demir
- Ege University School of Medicine Department of Psychiatry, SoCAT Lab, Izmir, Turkey
| | - Kelly Rootes-Murdy
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Melissa Green
- School of Clinical Medicine, University of New South Wales, Sydney, Australia
| | - Yann Quidé
- School of Psychology, University of New South Wales, Sydney, Australia
| | - Young Chul Chung
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Woo-Sung Kim
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Scott R. Sponheim
- Minneapolis VA Medical Center, University of Minnesota, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Caroline Demro
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Ian S. Ramsay
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Felice Iasevoli
- Section of Psychiatry - Department of Neuroscience - University “Federico II”, Naples, Italy
| | - Andrea de Bartolomeis
- Section of Psychiatry - Department of Neuroscience - University “Federico II”, Naples, Italy
| | - Annarita Barone
- Section of Psychiatry - Department of Neuroscience - University “Federico II”, Naples, Italy
| | - Mariateresa Ciccarelli
- Section of Psychiatry - Department of Neuroscience - University “Federico II”, Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences - University “Federico II”, Naples, Italy
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences - University “Federico II”, Naples, Italy
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences - University “Federico II”, Naples, Italy
| | - Mario Tranfa
- Department of Advanced Biomedical Sciences - University “Federico II”, Naples, Italy
| | - Min Tae M. Park
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
| | - Matthias Kirschner
- Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Switzerland
| | - Foivos Georgiadis
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Switzerland
| | - Stefan Kaiser
- Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Switzerland
| | - Tamsyn E Van Rheenen
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, Australia
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, Melbourne, Australia
| | - Susan L Rossell
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, Melbourne, Australia
| | - Matthew Hughes
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, Melbourne, Australia
| | - William Woods
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, Melbourne, Australia
| | - Sean P Carruthers
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, Melbourne, Australia
| | - Philip Sumner
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, Melbourne, Australia
| | - Elysha Ringin
- National Institute of Mental Health, Klecany, Czech Republic
| | - Filip Spaniel
- National Institute of Mental Health, Klecany, Czech Republic
| | - Antonin Skoch
- National Institute of Mental Health, Klecany, Czech Republic
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - David Tomecek
- National Institute of Mental Health, Klecany, Czech Republic
- Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Philipp Homan
- Psychiatric Hospital, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich & Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Stephanie Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, Switzerland
- Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Switzerland
| | - Wolfgang Omlor
- Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Giacomo Cecere
- Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Dana D Nguyen
- Department of Pediatrics, University of California Irvine, Irvine, California, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Sophia Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Long-Biao Cui
- Department of Clinical Psychology, Fourth Military Medical University, Xi’an, PR China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, China
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jessica A. Turner
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, USA
| | - Theo G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine Hall, room 109, Irvine, CA, 92697-3950, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, 309 Qureshey Research Lab, Irvine, CA, 92697, USA
| | - Wei Cheng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
| | | | | | - Jianfeng Feng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
- School of Data Science, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
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Ma H, Qi Y, Gong P, Zhang J, Lu WL, Feng J. Self-Organization of Nonlinearly Coupled Neural Fluctuations Into Synergistic Population Codes. Neural Comput 2023; 35:1820-1849. [PMID: 37725705 DOI: 10.1162/neco_a_01612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/26/2023] [Indexed: 09/21/2023]
Abstract
Neural activity in the brain exhibits correlated fluctuations that may strongly influence the properties of neural population coding. However, how such correlated neural fluctuations may arise from the intrinsic neural circuit dynamics and subsequently affect the computational properties of neural population activity remains poorly understood. The main difficulty lies in resolving the nonlinear coupling between correlated fluctuations with the overall dynamics of the system. In this study, we investigate the emergence of synergistic neural population codes from the intrinsic dynamics of correlated neural fluctuations in a neural circuit model capturing realistic nonlinear noise coupling of spiking neurons. We show that a rich repertoire of spatial correlation patterns naturally emerges in a bump attractor network and further reveals the dynamical regime under which the interplay between differential and noise correlations leads to synergistic codes. Moreover, we find that negative correlations may induce stable bound states between two bumps, a phenomenon previously unobserved in firing rate models. These noise-induced effects of bump attractors lead to a number of computational advantages including enhanced working memory capacity and efficient spatiotemporal multiplexing and can account for a range of cognitive and behavioral phenomena related to working memory. This study offers a dynamical approach to investigating realistic correlated neural fluctuations and insights to their roles in cortical computations.
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Affiliation(s)
- Hengyuan Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Yang Qi
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, NSW 2006, Australia
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
| | - Wen-Lian Lu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, U.K.
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Rolls ET, Deco G, Zhang Y, Feng J. Hierarchical organization of the human ventral visual streams revealed with magnetoencephalography. Cereb Cortex 2023; 33:10686-10701. [PMID: 37689834 DOI: 10.1093/cercor/bhad318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/17/2023] [Accepted: 08/17/2023] [Indexed: 09/11/2023] Open
Abstract
The hierarchical organization between 25 ventral stream visual cortical regions and 180 cortical regions was measured with magnetoencephalography using the Human Connectome Project Multimodal Parcellation atlas in 83 Human Connectome Project participants performing a visual memory task. The aim was to reveal the hierarchical organization using a whole-brain model based on generative effective connectivity with this fast neuroimaging method. V1-V4 formed a first group of interconnected regions. Especially V4 had connectivity to a ventrolateral visual stream: V8, the fusiform face cortex, and posterior inferior temporal cortex PIT. These regions in turn had effectivity connectivity to inferior temporal cortex visual regions TE2p and TE1p. TE2p and TE1p then have connectivity to anterior temporal lobe regions TE1a, TE1m, TE2a, and TGv, which are multimodal. In a ventromedial visual stream, V1-V4 connect to ventromedial regions VMV1-3 and VVC. VMV1-3 and VVC connect to the medial parahippocampal gyrus PHA1-3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal PHA1-3 regions have connectivity to the hippocampal system regions the perirhinal cortex, entorhinal cortex, and hippocampus. These effective connectivities of two ventral visual cortical streams measured with magnetoencephalography provide support to the hierarchical organization of brain systems measured with fMRI, and new evidence on directionality.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Yi Zhang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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Duan ZJ, Feng J, Zhao HQ, Wang HD, Gui QP, Zhang XF, Ma Z, Hu ZJ, Xiang L, Qi XL. [Plurihormonal PIT1-lineage pituitary neuroendocrine tumors: a clinicopathological study]. Zhonghua Bing Li Xue Za Zhi 2023; 52:1017-1024. [PMID: 37805393 DOI: 10.3760/cma.j.cn112151-20230216-00137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 10/09/2023]
Abstract
Objective: To investigate the clinicopathological characteristics of plurihormonal PIT1-lineage pituitary neuroendocrine tumors. Methods: Forty-eight plurihormonal PIT1-lineage tumors were collected between January 2018 and April 2022 from the pathological database of Sanbo Brain Hospital, Capital Medical University. The related clinical and imaging data were retrieved. H&E, immunohistochemical and special stains were performed. Results: Out of the 48 plurihormonal PIT1-lineage tumors included, 13 cases were mature PIT1-lineage tumors and 35 cases were immature PIT1-lineage tumors. There were some obvious clinicopathological differences between the two groups. Clinically, the mature plurihormonal PIT1-lineage tumor mostly had endocrine symptoms due to increased hormone production, while a small number of immature PIT1-lineage tumors had endocrine symptoms accompanied by low-level increased serum pituitary hormone; patients with the immature PIT1-lineage tumors were younger than the mature PIT1-lineage tumors; the immature PIT1-lineage tumors were larger in size and more likely invasive in imaging. Histopathologically, the mature PIT1-lineage tumors were composed of large eosinophilic cells with high proportion of growth hormone expression, while the immature PIT1-lineage tumors consisted of chromophobe cells with a relatively higher expression of prolactin; the mature PIT1-lineage tumors had consistently diffuse cytoplasmic positive staining for keratin, while the immature PIT1-lineage tumors had various expression for keratin; the immature PIT1-lineage tumors showed more mitotic figures and higher Ki-67 proliferation index; in addition, 25.0% (12/48) of PIT1-positive plurihormonal tumors showed abnormal positive staining for gonadotropin hormones. There was no significant difference in the progression-free survival between the two groups (P=0.648) by Kaplan-Meier analysis. Conclusions: Plurihormonal PIT1-lineage tumor belongs to a rare type of PIT1-lineage pituitary neuroendocrine tumors, most of which are of immature lineage. Clinically increased symptoms owing to pituitary hormone secretion, histopathologically increased number of eosinophilic tumor cells with high proportion of growth hormone expression, diffusely cytoplasmic keratin staining and low proliferative activity can help differentiate the mature plurihormonal PIT1-lineage tumors from the immature PIT1-lineage tumors. The immature PIT1-lineage tumors have more complicated clinicopathological characteristics.
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Affiliation(s)
- Z J Duan
- Department of Pathology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - J Feng
- Department of Pathology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - H Q Zhao
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - H D Wang
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - Q P Gui
- Department of Pathology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - X F Zhang
- Department of Radiology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - Z Ma
- Department of Pathology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - Z J Hu
- Department of Pathology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - L Xiang
- Department of Pathology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - X L Qi
- Department of Pathology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
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Schick J, Lanciano RM, Feng J, Whitlark A, Pancholy P, Ji W, Hanlon A, Lozano A, Lamond J. High Risk Prognostic Factors Predictive of Outcome Following Stereotactic Body Radiation Therapy (SBRT) for Early-Stage Lung Cancer. Int J Radiat Oncol Biol Phys 2023; 117:e55-e56. [PMID: 37785691 DOI: 10.1016/j.ijrobp.2023.06.769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) High risk prognostic factors for outcomes following SBRT for early-stage lung cancer per SWOG/NRG 1914 include: tumor size > 2cm; moderately/poorly/undifferentiated histology; or high metabolic activity on PET/CT defined as SUV > 6.2. The purpose of this IRB approved retrospective study is to assess outcome for stage I lung cancer and to validate SWOG risk stratification in a single community-based radiation department. MATERIALS/METHODS A total of 132 patients with 139 tumors treated with SBRT between 2014 and 2019 were stratified by SWOG risk group (high vs. low). To assess differences by risk group in estimated overall survival (OS) at the patient level, as well as Freedom from Local Failure (FFLF), Freedom from Regional Failure (FFRF) and Freedom from Distant Failure (FFDF) at the tumor level, Kaplan-Meier methodology and Cox proportional hazards (PH) modeling for correlated data were used. Statistical significance was concluded at the 0.05 level. RESULTS Median follow-up for the entire group is 56 months. 77% of patients had high risk tumors. The median patient age was 75 years with 57% female. Patients with high-risk tumors were older (p = 0.023) compared to patients with low-risk tumors. At the tumor level, high risk tumors were more likely to have biopsies performed (91% vs 57% p = 0.002) and more likely to experience regional failure (28% vs 7% p = 0.005). High risk tumors were more likely to experience local failure (8% vs 0% p = NS) and distant failure (27% vs 14% p = NS). Median survival was 49 months for the whole group (46.0 months high risk, 65.1 months low risk). Actuarial OS at 5 years is 38% (35% high risk, 51% low risk). Actuarial FFLF at 5 years is 94% (93% high risk, 100% low risk). Actuarial FFRF at 5 years is 77% (72% high risk, 93% low risk). Actuarial FFDF at 5 years is 74% (70% high risk, 88% low risk). Cox PH models revealed no statistically significant differences in FFRF by risk group (p = 0.08). CONCLUSION Excellent local control but higher regional and distant failure was demonstrated for high-risk lung tumors as defined by SWOG/NRG 1914. Clinically important decrement in outcomes were consistently noted for high-risk tumors in this sample which did not demonstrate statistical significance due to lack of events and statistical power. However, our data supports the prognostic importance of tumor size/grade and SUV for identifying patients at high risk. Further validation with larger sample sizes would contribute to our knowledge regarding risk stratification for early-stage lung cancer treated with SBRT.
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Affiliation(s)
- J Schick
- Crozer Keystone Healthcare System, Philadelphia, PA
| | - R M Lanciano
- Crozer Keystone Healthcare System/ Philadelphia CyberKnife Center, Havertown, PA
| | - J Feng
- Philadelphia CyberKnife, Havertown, PA
| | - A Whitlark
- Crozer Chester Medical Center, Philadelphia, PA
| | - P Pancholy
- Crozer Chester Medical Center, Philadelphia, PA
| | - W Ji
- Virginia Tech, Roanoke, VA
| | | | | | - J Lamond
- Philadelphia CyberKnife, Philadelphia, PA
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Schumann G, Andreassen OA, Banaschewski T, Calhoun VD, Clinton N, Desrivieres S, Brandlistuen RE, Feng J, Hese S, Hitchen E, Hoffmann P, Jia T, Jirsa V, Marquand AF, Nees F, Nöthen MM, Novarino G, Polemiti E, Ralser M, Rapp M, Schepanski K, Schikowski T, Slater M, Sommer P, Stahl BC, Thompson PM, Twardziok S, van der Meer D, Walter H, Westlye L. Addressing Global Environmental Challenges to Mental Health Using Population Neuroscience: A Review. JAMA Psychiatry 2023; 80:1066-1074. [PMID: 37610741 DOI: 10.1001/jamapsychiatry.2023.2996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Importance Climate change, pollution, urbanization, socioeconomic inequality, and psychosocial effects of the COVID-19 pandemic have caused massive changes in environmental conditions that affect brain health during the life span, both on a population level as well as on the level of the individual. How these environmental factors influence the brain, behavior, and mental illness is not well known. Observations A research strategy enabling population neuroscience to contribute to identify brain mechanisms underlying environment-related mental illness by leveraging innovative enrichment tools for data federation, geospatial observation, climate and pollution measures, digital health, and novel data integration techniques is described. This strategy can inform innovative treatments that target causal cognitive and molecular mechanisms of mental illness related to the environment. An example is presented of the environMENTAL Project that is leveraging federated cohort data of over 1.5 million European citizens and patients enriched with deep phenotyping data from large-scale behavioral neuroimaging cohorts to identify brain mechanisms related to environmental adversity underlying symptoms of depression, anxiety, stress, and substance misuse. Conclusions and Relevance This research will lead to the development of objective biomarkers and evidence-based interventions that will significantly improve outcomes of environment-related mental illness.
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Affiliation(s)
- Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, Georgia
| | | | - Sylvane Desrivieres
- Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, United Kingdom
| | | | - Jianfeng Feng
- Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Soeren Hese
- Institute of Geography, Friedrich Schiller University Jena, Jena, Germany
| | - Esther Hitchen
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Per Hoffmann
- Institute of Human Genetics, University Hospital of Bonn, Bonn, Germany
| | - Tianye Jia
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Viktor Jirsa
- Institut National de la Santé et de la Recherche Médicale (Inserm), Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | | | - Frauke Nees
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University Hospital of Bonn, Bonn, Germany
| | - Gaia Novarino
- Institute of Science and Technology, Klosterneuburg, Austria
| | - Elli Polemiti
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Institute of Biochemistry Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Rapp
- Department for Social and Preventive Medicine, University of Potsdam, Potsdam, Germany
| | | | - Tamara Schikowski
- NAKO, Leibniz Institute for Environmental Medicine, Duesseldorf, Germany
| | - Mel Slater
- Campus de Mundet, ICREA-University of Barcelona, Barcelona, Spain
- Department of Computer Science, University College London, London, United Kingdom
| | | | - Bernd Carsten Stahl
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Los Angeles, California
| | - Sven Twardziok
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Henrik Walter
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lars Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
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Shi R, Xiang S, Jia T, Robbins TW, Kang J, Banaschewski T, Barker GJ, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Paus T, Poustka L, Hohmann S, Millenet S, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Lin X, Sahakian BJ, Feng J. Structural neurodevelopment at the individual level - a life-course investigation using ABCD, IMAGEN and UK Biobank data. medRxiv 2023:2023.09.20.23295841. [PMID: 37790416 PMCID: PMC10543061 DOI: 10.1101/2023.09.20.23295841] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages and the neurobiological basis underlying individual heterogeneity remains poorly understood. Using structural magnetic resonance imaging from the IMAGEN cohort (n=1,543), we show that adolescents can be clustered into three groups defined by distinct developmental patterns of whole-brain gray matter volume (GMV). Genetic and epigenetic determinants of group clustering and long-term impacts of neurodevelopment in mid-to-late adulthood were investigated using data from the ABCD, IMAGEN and UK Biobank cohorts. Group 1, characterized by continuously decreasing GMV, showed generally the best neurocognitive performances during adolescence. Compared to Group 1, Group 2 exhibited a slower rate of GMV decrease and worsened neurocognitive development, which was associated with epigenetic changes and greater environmental burden. Further, Group 3 showed increasing GMV and delayed neurocognitive development during adolescence due to a genetic variation, while these disadvantages were attenuated in mid-to-late adulthood. In summary, our study revealed novel clusters of adolescent structural neurodevelopment and suggested that genetically-predicted delayed neurodevelopment has limited long-term effects on mental well-being and socio-economic outcomes later in life. Our results could inform future research on policy interventions aimed at reducing the financial and emotional burden of mental illness.
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Li C, Xiao Z, Li Y, Chen Z, Ji X, Liu Y, Feng S, Zhang Z, Zhang K, Feng J, Robbins TW, Xiong S, Chen Y, Xiao X. Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys. Zool Res 2023; 44:967-980. [PMID: 37721106 PMCID: PMC10559098 DOI: 10.24272/j.issn.2095-8137.2022.449] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 09/14/2023] [Indexed: 09/19/2023] Open
Abstract
Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and prediction. However, action recognition currently used in non-human primate (NHP) research relies heavily on intense manual labor and lacks standardized assessment. In this work, we established two standard benchmark datasets of NHPs in the laboratory: MonkeyinLab (MiL), which includes 13 categories of actions and postures, and MiL2D, which includes sequences of two-dimensional (2D) skeleton features. Furthermore, based on recent methodological advances in deep learning and skeleton visualization, we introduced the MonkeyMonitorKit (MonKit) toolbox for automatic action recognition, posture estimation, and identification of fine motor activity in monkeys. Using the datasets and MonKit, we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome (RTT). MonKit was used to assess motor function, stereotyped behaviors, and depressive phenotypes, with the outcomes compared with human manual detection. MonKit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency, thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.
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Affiliation(s)
- Chuxi Li
- School of Information Science and Technology Micro Nano System Center, Fudan University, Shanghai 200433, China
| | - Zifan Xiao
- Department of Anesthesiology, Huashan Hospital
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education
- Behavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Yerong Li
- School of Information Science and Technology Micro Nano System Center, Fudan University, Shanghai 200433, China
| | - Zhinan Chen
- School of Information Science and Technology Micro Nano System Center, Fudan University, Shanghai 200433, China
| | - Xun Ji
- Kuang Yaming Honors School, Nanjing University, Nanjing, Jiangsu 210023, China
| | - Yiqun Liu
- Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China
| | - Shufei Feng
- State Key Laboratory of Primate Biomedical Research
- Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Zhen Zhang
- State Key Laboratory of Primate Biomedical Research
- Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Kaiming Zhang
- New Vision World LLC., Aliso Viejo, California 92656, USA
| | - Jianfeng Feng
- Department of Anesthesiology, Huashan Hospital
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education
- Behavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Trevor W Robbins
- Department of Anesthesiology, Huashan Hospital
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education
- Behavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 1TN, UK
| | - Shisheng Xiong
- School of Information Science and Technology Micro Nano System Center, Fudan University, Shanghai 200433, China. E-mail:
| | - Yongchang Chen
- State Key Laboratory of Primate Biomedical Research
- Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Xiao Xiao
- Department of Anesthesiology, Huashan Hospital
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education
- Behavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China. E-mail:
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He A, Yang L, Zhu L, Feng J. A refined toxicokinetic model for quantifying the interaction between Cd and Cu in zebrafish larvae. Ecotoxicol Environ Saf 2023; 263:115303. [PMID: 37515971 DOI: 10.1016/j.ecoenv.2023.115303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 07/18/2023] [Accepted: 07/22/2023] [Indexed: 07/31/2023]
Abstract
The interaction between metals is ubiquitous, but there is still a lack of quantitative models considering the interaction between metals, which leads to the deviations in predicting the joint toxicity of metals. The present study estimated the uptake rate constants (kin) and elimination rate constants (kout) and elucidated how the presence of one metal (Cu or Cd) affects the absorption and excretion of another metal (Cd or Cu) in zebrafish larvae. The results showed that Cd and Cu inhibited each other in the process of absorption and excretion by comparing separately kin and kout of Cd or Cu with the other metal Cu or Cd mixed concentrations increased, thereby affecting the Cd and Cu bioaccumulation in the zebrafish larvae. Then the interactions between Cd and Cu in the uptake and elimination processes were quantified to obtain a refined toxicokinetic model. Verification with independent experiment data showed that the refined toxicokinetic model could significantly improve the prediction of the Cd or Cu bioaccumulation in the zebrafish larvae. This study contributes to understand the toxicokinetic process of the Cd-Cu mixture in the zebrafish larvae, and the developed model could be used to predict the toxicity of the metal mixtures.
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Affiliation(s)
- An He
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Lanpeng Yang
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Lin Zhu
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Jianfeng Feng
- Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China.
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Yu G, Liu Z, Wu X, Becker B, Zhang K, Fan H, Peng S, Kuang N, Kang J, Dong G, Zhao XM, Schumann G, Feng J, Sahakian BJ, Robbins TW, Palaniyappan L, Zhang J. Common and disorder-specific cortical thickness alterations in internalizing, externalizing and thought disorders during early adolescence: an Adolescent Brain and Cognitive Development study. J Psychiatry Neurosci 2023; 48:E345-E356. [PMID: 37673436 PMCID: PMC10495167 DOI: 10.1503/jpn.220202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 02/13/2023] [Accepted: 05/17/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND A growing body of neuroimaging studies has reported common neural abnormalities among mental disorders in adults. However, it is unclear whether the distinct disorder-specific mechanisms operate during adolescence despite the overlap among disorders. METHODS We studied a large cohort of more than 11 000 preadolescent (age 9-10 yr) children from the Adolescent Brain and Cognitive Development cohort. We adopted a regrouping approach to compare cortical thickness (CT) alterations and longitudinal changes between healthy controls (n = 4041) and externalizing (n = 1182), internalizing (n = 1959) and thought disorder (n = 347) groups. Genome-wide association study (GWAS) was performed on regional CT across 4468 unrelated European youth. RESULTS Youth with externalizing or internalizing disorders exhibited increased regional CT compared with controls. Externalizing (p = 8 × 10-4, Cohen d = 0.10) and internalizing disorders (p = 2 × 10-3, Cohen d = 0.08) shared thicker CT in the left pars opercularis. The somatosensory and the primary auditory cortex were uniquely affected in externalizing disorders, whereas the primary motor cortex and higher-order visual association areas were uniquely affected in internalizing disorders. Only youth with externalizing disorders showed decelerated cortical thinning from age 10-12 years. The GWAS found 59 genome-wide significant associated genetic variants across these regions. Cortical thickness in common regions was associated with glutamatergic neurons, while internalizing-specific regional CT was associated with astrocytes, oligodendrocyte progenitor cells and GABAergic neurons. LIMITATIONS The sample size of the GWAS was relatively small. CONCLUSION Our study provides strong evidence for the presence of specificity in CT, developmental trajectories and underlying genetic underpinnings among externalizing and internalizing disorders during early adolescence. Our results support the neurobiological validity of the regrouping approach that could supplement the use of a dimensional approach in future clinical practice.
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Affiliation(s)
- Gechang Yu
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Zhaowen Liu
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Xinran Wu
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Benjamin Becker
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Kai Zhang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Huaxin Fan
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Songjun Peng
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Nanyu Kuang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Jujiao Kang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Guiying Dong
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Xing-Ming Zhao
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Gunter Schumann
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Jianfeng Feng
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Barbara J Sahakian
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Trevor W Robbins
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Lena Palaniyappan
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Jie Zhang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
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47
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Xie C, Xiang S, Shen C, Peng X, Kang J, Li Y, Cheng W, He S, Bobou M, Broulidakis MJ, van Noort BM, Zhang Z, Robinson L, Vaidya N, Winterer J, Zhang Y, King S, Banaschewski T, Barker GJ, Bokde ALW, Bromberg U, Büchel C, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Ittermann B, Lemaître H, Martinot JL, Martinot MLP, Nees F, Orfanos DP, Paus T, Poustka L, Fröhner JH, Schmidt U, Sinclair J, Smolka MN, Stringaris A, Walter H, Whelan R, Desrivières S, Sahakian BJ, Robbins TW, Schumann G, Jia T, Feng J. Author Correction: A shared neural basis underlying psychiatric comorbidity. Nat Med 2023; 29:2375. [PMID: 37558759 PMCID: PMC10504065 DOI: 10.1038/s41591-023-02512-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Affiliation(s)
- Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Chun Shen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Xuerui Peng
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Yuzhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Shiqi He
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- School of Health Sciences, The University of Manchester, Manchester, UK
| | - Marina Bobou
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - M John Broulidakis
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
- Department of Psychology, College of Science, Northeastern University, Boston, MA, USA
| | | | - Zuo Zhang
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Lauren Robinson
- Department of Psychological Medicine, Section for Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Jeanne Winterer
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Yuning Zhang
- Psychology Department, B44 University Rd, University of Southampton, Southampton, UK
| | - Sinead King
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- School of Medicine, Center for Neuroimaging, Cognition and Genomics, National University of Ireland (NUI), Galway, Ireland
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Uli Bromberg
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | | | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, C.E.A., Université Paris-Saclay, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Hervé Lemaître
- NeuroSpin, C.E.A., Université Paris-Saclay, Gif-sur-Yvette, France
- Institut National de la Santé et de la Recherche Médicale, UMR 992 INSERM, CEA, Faculté de médecine, Université Paris-Sud, Université Paris-Saclay, NeuroSpin, Gif-sur-Yvette, France
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 'Trajectoires développementales en psychiatrie', Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS UMR9010, Centre Borelli, Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 'Trajectoires développementales en psychiatrie', Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS UMR9010, Centre Borelli, Gif-sur-Yvette, France
- AP-HP, Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | | | - Tomáš Paus
- Department of Psychiatry and Neuroscience and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Quebec, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Ulrike Schmidt
- Department of Psychological Medicine, Section for Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Julia Sinclair
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Argyris Stringaris
- Division of Psychiatry and Department of Clinical, Educational & Health Psychology, University College London, London, UK
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Barbara J Sahakian
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Trevor W Robbins
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Gunter Schumann
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Department of Sports and Health Sciences, University of Potsdam, Potsdam, Germany
- PONS Centre, Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- School of Mathematical Sciences and Centre for Computational Systems Biology, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, UK
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China
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48
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Ye L, Feng J, Li C. Controlling brain dynamics: Landscape and transition path for working memory. PLoS Comput Biol 2023; 19:e1011446. [PMID: 37669311 PMCID: PMC10503743 DOI: 10.1371/journal.pcbi.1011446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/15/2023] [Accepted: 08/21/2023] [Indexed: 09/07/2023] Open
Abstract
Understanding the underlying dynamical mechanisms of the brain and controlling it is a crucial issue in brain science. The energy landscape and transition path approach provides a possible route to address these challenges. Here, taking working memory as an example, we quantified its landscape based on a large-scale macaque model. The working memory function is governed by the change of landscape and brain-wide state switching in response to the task demands. The kinetic transition path reveals that information flow follows the direction of hierarchical structure. Importantly, we propose a landscape control approach to manipulate brain state transition by modulating external stimulation or inter-areal connectivity, demonstrating the crucial roles of associative areas, especially prefrontal and parietal cortical areas in working memory performance. Our findings provide new insights into the dynamical mechanism of cognitive function, and the landscape control approach helps to develop therapeutic strategies for brain disorders.
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Affiliation(s)
- Leijun Ye
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Chunhe Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
- School of Mathematical Sciences and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
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49
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Xiang S, Jia T, Xie C, Cheng W, Chaarani B, Banaschewski T, Barker GJ, Bokde ALW, Büchel C, Desrivières S, Flor H, Grigis A, Gowland PA, Brühl R, Martinot JL, Martinot MLP, Nees F, Orfanos DP, Poustka L, Hohmann S, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Garavan H, Schumann G, Sahakian BJ, Robbins TW, Feng J. Association between vmPFC gray matter volume and smoking initiation in adolescents. Nat Commun 2023; 14:4684. [PMID: 37582920 PMCID: PMC10427673 DOI: 10.1038/s41467-023-40079-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 07/12/2023] [Indexed: 08/17/2023] Open
Abstract
Smoking of cigarettes among young adolescents is a pressing public health issue. However, the neural mechanisms underlying smoking initiation and sustenance during adolescence, especially the potential causal interactions between altered brain development and smoking behaviour, remain elusive. Here, using large longitudinal adolescence imaging genetic cohorts, we identify associations between left ventromedial prefrontal cortex (vmPFC) gray matter volume (GMV) and subsequent self-reported smoking initiation, and between right vmPFC GMV and the maintenance of smoking behaviour. Rule-breaking behaviour mediates the association between smaller left vmPFC GMV and smoking behaviour based on longitudinal cross-lagged analysis and Mendelian randomisation. In contrast, smoking behaviour associated longitudinal covariation of right vmPFC GMV and sensation seeking (especially hedonic experience) highlights a potential reward-based mechanism for sustaining addictive behaviour. Taken together, our findings reveal vmPFC GMV as a possible biomarker for the early stages of nicotine addiction, with implications for its prevention and treatment.
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Affiliation(s)
- Shitong Xiang
- Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Tianye Jia
- Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Centre for Population Neuroscience and Stratified Medicine (PONS Centre), ISTBI, Fudan University, Shanghai, China.
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Chao Xie
- Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Wei Cheng
- Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Bader Chaarani
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | | | - Sylvane Desrivières
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, C.E.A., Université Paris-Saclay, Gif-sur-Yvette, France
| | - Penny A Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 'Trajectoires développementales en psychiatrie', Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS UMR9010, Centre Borelli, Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 'Trajectoires développementales en psychiatrie', Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS UMR9010, Centre Borelli, Gif-sur-Yvette, France
- AP-HP, Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | | | - Luise Poustka
- Department of Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Nilakshi Vaidya
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität BerlinHumboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität BerlinHumboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA
| | - Gunter Schumann
- Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
- Centre for Population Neuroscience and Stratified Medicine (PONS Centre), ISTBI, Fudan University, Shanghai, China
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität BerlinHumboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Centre for Population Neuroscience and Stratified Medicine (PONS Centre), Charité University Medicine Berlin, Berlin, Germany
| | - Barbara J Sahakian
- Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
- Department of Psychiatry and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Trevor W Robbins
- Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China.
- Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK.
| | - Jianfeng Feng
- Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, United Kingdom.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
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Kuang N, Liu Z, Yu G, Wu X, Becker B, Fan H, Peng S, Zhang K, Zhao J, Kang J, Dong G, Zhao X, Sahakian BJ, Robbins TW, Cheng W, Feng J, Schumann G, Palaniyappan L, Zhang J. Neurodevelopmental risk and adaptation as a model for comorbidity among internalizing and externalizing disorders: genomics and cell-specific expression enriched morphometric study. BMC Med 2023; 21:291. [PMID: 37542243 PMCID: PMC10403847 DOI: 10.1186/s12916-023-02920-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 06/01/2023] [Indexed: 08/06/2023] Open
Abstract
BACKGROUND Comorbidity is the rule rather than the exception for childhood and adolescent onset mental disorders, but we cannot predict its occurrence and do not know the neural mechanisms underlying comorbidity. We investigate if the effects of comorbid internalizing and externalizing disorders on anatomical differences represent a simple aggregate of the effects on each disorder and if these comorbidity-associated cortical surface differences relate to a distinct genetic underpinning. METHODS We studied the cortical surface area (SA) and thickness (CT) of 11,878 preadolescents (9-10 years) from the Adolescent Brain and Cognitive Development Study. Linear mixed models were implemented in comparative and association analyses among internalizing (dysthymia, major depressive disorder, disruptive mood dysregulation disorder, agoraphobia, panic disorder, specific phobia, separation anxiety disorder, social anxiety disorder, generalized anxiety disorder, post-traumatic stress disorder), externalizing (attention-deficit/hyperactivity disorder, oppositional defiant disorder, conduct disorder) diagnostic groups, a group with comorbidity of the two and a healthy control group. Genome-wide association analysis (GWAS) and cell type specificity analysis were performed on 4468 unrelated European participants from this cohort. RESULTS Smaller cortical surface area but higher thickness was noted across patient groups when compared to controls. Children with comorbid internalizing and externalizing disorders had more pronounced areal reduction than those without comorbidity, indicating an additive burden. In contrast, cortical thickness had a non-linear effect with comorbidity: the comorbid group had no significant CT differences, while those patient groups without comorbidity had significantly higher thickness compare to healthy controls. Distinct biological pathways were implicated in regional SA and CT differences. Specifically, CT differences were associated with immune-related processes implicating astrocytes and oligodendrocytes, while SA-related differences related mainly to inhibitory neurons. CONCLUSION The emergence of comorbidity across distinct clusters of psychopathology is unlikely to be due to a simple additive neurobiological effect alone. Distinct developmental risk moderated by immune-related adaptation processes, with unique genetic and cell-specific factors, may contribute to underlying SA and CT differences. Children with the highest risk but lowest resilience, both captured in their developmental morphometry, may develop a comorbid illness pattern.
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Affiliation(s)
- Nanyu Kuang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Ministry of Education, Fudan University, Beijing, People's Republic of China
| | - Zhaowen Liu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shanxin, People's Republic of China
| | - Gechang Yu
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Ministry of Education, Fudan University, Beijing, People's Republic of China
| | - Xinran Wu
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Ministry of Education, Fudan University, Beijing, People's Republic of China
| | - Benjamin Becker
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Huaxin Fan
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Ministry of Education, Fudan University, Beijing, People's Republic of China
| | - Songjun Peng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Ministry of Education, Fudan University, Beijing, People's Republic of China
| | - Kai Zhang
- Institute of Computer Science and Technology, East China Normal University, Shanghai, People's Republic of China
| | - Jiajia Zhao
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Ministry of Education, Fudan University, Beijing, People's Republic of China
| | - Jujiao Kang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, People's Republic of China
| | - Guiying Dong
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People's Republic of China
| | - Xingming Zhao
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People's Republic of China
- Zhangjiang Fudan International Innovation Center, Shanghai, 200433, People's Republic of China
| | - Barbara J Sahakian
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Trevor W Robbins
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China
- Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Wei Cheng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Ministry of Education, Fudan University, Beijing, People's Republic of China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, 321004, China
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, 200032, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Ministry of Education, Fudan University, Beijing, People's Republic of China
- Shanghai Center for Mathematical Sciences, Shanghai, 200433, People's Republic of China
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK
- Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200433, People's Republic of China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, People's Republic of China
| | - Gunter Schumann
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China.
- PONS Research Group, Department of Psychiatry and 20 Psychotherapy, Humboldt University, Berlin and Leibniz Institute for Neurobiology, Campus Charite Mitte, Magdeburg, Germany.
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada.
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
- Robarts Research Institute, University of Western Ontario, London, ON, Canada.
- Department of Medical Biophysica, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
| | - Jie Zhang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, People's Republic of China.
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Ministry of Education, Fudan University, Beijing, People's Republic of China.
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