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He M, Zhu H, Wang X, Zhou L, Zhang J. Mapping PTSD-Related Brain Dysregulation With Connectome Gradient Analysis. J Magn Reson Imaging 2025. [PMID: 40411269 DOI: 10.1002/jmri.29829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2025] [Revised: 05/14/2025] [Accepted: 05/15/2025] [Indexed: 05/26/2025] Open
Abstract
BACKGROUND Hierarchical architecture is a fundamental organizational principle of the human brain. Previous studies have suggested that posttraumatic stress disorder (PTSD) may be characterized as disorders in the cerebral hierarchical organization. However, the specific abnormalities and underlying mechanisms are unclear. PURPOSE To investigate whether there are disorders of cerebral hierarchical organization in patients with PTSD and their underlying mechanisms of alteration. STUDY TYPE Prospective, case control. FILED STRENGTH/SEQUENCE 3.0T, gradient echo echo-planar imaging sequence. SUBJECTS Forty-nine patients with PTSD (11 males and 38 females; Clinician-Administered PTSD Scale (CAPS) score > 40) and 38 trauma-exposed controls (TEC) (13 males and 25 females; CAPS score < 40). ASSESSMENT Connectome gradient analysis was used to systematically examine disorders of cerebral hierarchical organization. Gradient metrics included range and variance of gradient scores. Graph theory analysis was also employed to explore underlying mechanisms of gradient abnormalities, and system segregation (quantifying the degree of separation between functional networks) and participation coefficients (PC) (quantifying the degree of connectivity that a given node has to other networks) were calculated. STATISTICAL TESTS Two-sample t-tests were used to compare differences in gradient and graph theory metrics between groups. The association between gradient scores and CAPS scores was assessed using partial correlation analysis. p < 0.05 was set as the statistical significance threshold, with false discovery rate (FDR) correction. RESULTS Compared with TEC, patients with PTSD showed significantly increased global gradient variance and altered gradient indicators in networks. At global and network levels, patients with PTSD exhibited significantly increased system segregation and significantly reduced PC, which were significantly associated with gradient variance (global system segregation: r = 0.84, global PC: r = 0.93, system segregation in SMN: r = 0.59, PC in DAN: r = -0.62 and PC in FPN: r = -0.53). Moreover, gradient scores in DAN (r = 0.319) and some regions of DMN (ANG.L: r = 0.294), SMN (PreCG.L: r = 0.319), and LIM (HIP.R: r = 0.319) were significantly correlated with CAPS score. DATA CONCLUSION This study, integrating connectome gradient analysis with graph theory, showed hierarchical disruptions across multilevel brain networks in PTSD, potentially explaining clinical symptoms such as hypervigilance and dissociation. EVIDENCE LEVEL 2. TECHNICAL EFFICACY Stage 1.
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Affiliation(s)
- Meirong He
- College of Electrical Engineering, Sichuan University, Chengdu, Sichuan, China
- College of Electrical Engineering, Northwest Minzu University, Lanzhou, Gansu, China
| | - Hongru Zhu
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiaoyan Wang
- College of Electrical Engineering, Sichuan University, Chengdu, Sichuan, China
- College of Electrical Engineering, Northwest Minzu University, Lanzhou, Gansu, China
| | - Lijun Zhou
- College of Electrical Engineering, Sichuan University, Chengdu, Sichuan, China
- College of Electrical Engineering, Northwest Minzu University, Lanzhou, Gansu, China
| | - Junran Zhang
- College of Electrical Engineering, Sichuan University, Chengdu, Sichuan, China
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Wang Y, Eichert N, Paquola C, Rodriguez-Cruces R, DeKraker J, Royer J, Cabalo DG, Auer H, Ngo A, Leppert IR, Tardif CL, Rudko DA, Leech R, Amunts K, Valk SL, Smallwood J, Evans AC, Bernhardt BC. Multimodal gradients unify local and global cortical organization. Nat Commun 2025; 16:3911. [PMID: 40280959 PMCID: PMC12032020 DOI: 10.1038/s41467-025-59177-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2024] [Accepted: 04/11/2025] [Indexed: 04/29/2025] Open
Abstract
Functional specialization of brain areas and subregions, as well as their integration into large-scale networks, are key principles in neuroscience. Consolidating both local and global perspectives on cortical organization, however, remains challenging. Here, we present an approach to integrate inter- and intra-areal similarities of microstructure, structural connectivity, and functional interactions. Using high-field in-vivo 7 tesla (7 T) Magnetic Resonance Imaging (MRI) data and a probabilistic post-mortem atlas of cortical cytoarchitecture, we derive multimodal gradients that capture cortex-wide organization. Inter-areal similarities follow a canonical sensory-fugal gradient, linking cortical integration with functional diversity across tasks. However, intra-areal heterogeneity does not follow this pattern, with greater variability in association cortices. Findings are replicated in an independent 7 T dataset and a 100-subject 3 tesla (3 T) cohort. These results highlight a robust coupling between local arealization and global cortical motifs, advancing our understanding of how specialization and integration shape human brain function.
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Affiliation(s)
- Yezhou Wang
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - Nicole Eichert
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Casey Paquola
- Institute of Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, Jülich, Germany
| | - Raul Rodriguez-Cruces
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Jordan DeKraker
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Donna Gift Cabalo
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Hans Auer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Alexander Ngo
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Ilana R Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Christine L Tardif
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - David A Rudko
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Robert Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
- C. and O. Vogt Institute of Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Sofie L Valk
- Institute of Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, Jülich, Germany
- Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
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3
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Zhang Q, Zhang A, Zhao Z, Li Q, Hu Y, Huang X, Kuang W, Zhao Y, Gong Q. Cognition-related connectome gradient dysfunctions of thalamus and basal ganglia in drug-naïve first-episode major depressive disorder. J Affect Disord 2025; 370:249-259. [PMID: 39500466 DOI: 10.1016/j.jad.2024.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 10/29/2024] [Accepted: 11/01/2024] [Indexed: 11/14/2024]
Abstract
BACKGROUND Subcortical functional abnormalities are believed to contribute to clinical symptoms and cognitive impairments in major depressive disorder (MDD). By introducing functional gradient mapping, the present study evaluated subcortical gradients in MDD patients and their association with cognitive features. METHODS Organization patterns and between-group differences in the principal subcortical gradient were investigated in 145 never-treated first-episode MDD patients and 145 healthy controls (HCs) across limbic, thalamic, and basal ganglia (BG) systems and their structural and functional subregions. We also assessed the associations between significant gradient alterations and clinical characteristics and neuropsychological functioning. RESULTS Overall, MDD patients showed a relatively compressed and disturbed gradient organization than HCs, with limbic and BG regions located at the two extreme ends of the principal gradient. Specifically, MDD patients had lower principal gradient values in thalamus and limbic system but higher values in BG than HCs. These gradient alterations, associated with intrinsic Euclidian distance and functional connectivity patterns, manifested as spatial rearrangements of gradient values within each respective subregion. Lower gradient values in thalamic subregion projecting to default mode network were associated with higher principal gradient values in BG subregion projecting to ventral attention network, and these gradient alterations were correlated with poorer episodic memory performance in MDD patients. LIMITATIONS The specific neuropathological mechanisms driving the gradient alterations still require further investigation. CONCLUSIONS Opposing gradient alterations in the thalamic and BG regions synergistically impact episodic memory performance in MDD, revealing an internally differentiated and cognition related pattern of subcortical gradient dysfunction in MDD.
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Affiliation(s)
- Qian Zhang
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Aoxiang Zhang
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Ziyuan Zhao
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qian Li
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Yongbo Hu
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Xiaoqi Huang
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, China
| | - Youjin Zhao
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China.
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China; Xiamen Key Laboratory of Psychoradiology and Neuromodulation, Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
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4
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Patel D, Siegelmann HT. Navigating the unknown: Leveraging self-information and diversity in partially observable environments. Biochem Biophys Res Commun 2024; 741:150923. [PMID: 39579529 DOI: 10.1016/j.bbrc.2024.150923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 07/17/2024] [Accepted: 10/28/2024] [Indexed: 11/25/2024]
Abstract
Reinforcement learning algorithms often struggle to learn in partially observable environments, where different states of the environment may appear identical. However, not all partially observable environments pose the same level of difficulty for learning. This work introduces the concept of dissonance distance, a metric that can estimate the difficulty of learning in such environments. We demonstrate that self-information, such as internal oscillations or memory of previous actions, can increase the dissonance distance and make learning easier in partially observable environments. Additionally, sensory occlusion may occur after learning was completed, leading to a lack of sufficient information and catastrophic failure. To address this, we propose a spatially layered architecture (SLA) inspired by the brain, which trains multiple policies in parallel for the same task. SLA can change the amount of external information processed at each timestep, providing an adaptive approach to handle the changing information in the environment state-space. We evaluate the effectiveness of our SLA method showing learnability and robustness against realistic noise and occlusion in sensory inputs for the partially observable Continuous Mountain Car environment. We hypothesize that multi-policy approaches like SLA might explain the complex dopamine dynamics in the brain that cannot be explained with the state of the art scalar Temporal Difference error.
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Affiliation(s)
- Devdhar Patel
- Manning College of Information and Computer Science, University of Massachusetts, Amherst, MA, 01003, USA.
| | - Hava T Siegelmann
- Manning College of Information and Computer Science, University of Massachusetts, Amherst, MA, 01003, USA
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5
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Wang Y, Tang L, Wang J, Li W, Wang M, Chen Q, Yang Z, Li Z, Wang Z, Wu G, Zhang P. Disruption of network hierarchy pattern in bulimia nervosa reveals brain information integration disorder. Appetite 2024; 203:107694. [PMID: 39341080 DOI: 10.1016/j.appet.2024.107694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 09/22/2024] [Accepted: 09/25/2024] [Indexed: 09/30/2024]
Abstract
The human brain works as a hierarchical organization that is a continuous axis spanning sensorimotor cortex to transmodal cortex (referring to cortex that integrates multimodal sensory information and participates in complex cognitive functions). Previous studies have demonstrated abnormalities in several specific networks that may account for their multiple behavioral deficits in patients with bulimia nervosa (BN), but whether and how the network hierarchical organization changes in BN remain unknown. This study aimed to investigate alterations of the hierarchy organization in BN network and their clinical relevance. Connectome gradient analyses were applied to depict the network hierarchy patterns of fifty-nine patients with BN and thirty-nine healthy controls (HCs). Then, we evaluated the network- and voxel-level gradient alterations of BN by comparing gradient values in each network and each voxel between patients with BN and HCs. Finally, the association between altered gradient values and clinical variables was explored. In the principal gradient, patients with BN exhibited reduced gradient values in dorsal attention network and increased gradient values in subcortical regions compared to HCs. In the secondary gradient, patients with BN showed decreased gradient values in ventral attention network and increased gradient values in limbic network. Regionally, the areas with altered principal or secondary gradient values in BN group were mainly located in transmodal networks, i.e., the default-mode and frontoparietal network. In BN group, the principal gradient values of right inferior frontal gyrus were negatively associated with external eating behavior. This study revealed the disordered network hierarchy patterns in patients with BN, which suggested a disturbance of brain information integration from attention network and subcortical regions to transmodal networks in these patients. These findings may provide insight into the neurobiological underpinnings of BN.
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Affiliation(s)
- Yiling Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No.95 Yongan Road, Xicheng District, Beijing, 100050, China
| | - Lirong Tang
- Beijing Anding Hospital Capital Medical University, No.5 Ankang Lane, Dewai Avenue, Xicheng District, Beijing, 100088, China; The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, No.5 Ankang Lane, Dewai Avenue, Xicheng District, Beijing, 100088, China
| | - Jiani Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No.95 Yongan Road, Xicheng District, Beijing, 100050, China
| | - Weihua Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No.95 Yongan Road, Xicheng District, Beijing, 100050, China
| | - Miao Wang
- Peking University, No.5 Summer Palace Road, Haidian District, Beijing, 100871, China
| | - Qian Chen
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No.95 Yongan Road, Xicheng District, Beijing, 100050, China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No.95 Yongan Road, Xicheng District, Beijing, 100050, China
| | - Zhanjiang Li
- Beijing Anding Hospital Capital Medical University, No.5 Ankang Lane, Dewai Avenue, Xicheng District, Beijing, 100088, China; The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, No.5 Ankang Lane, Dewai Avenue, Xicheng District, Beijing, 100088, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No.95 Yongan Road, Xicheng District, Beijing, 100050, China.
| | - Guowei Wu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, No.16 Lincui Road, Chaoyang District, Beijing, 100020, China.
| | - Peng Zhang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No.95 Yongan Road, Xicheng District, Beijing, 100050, China.
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6
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Xiong S, Tan Y, Wang G, Yan P, Xiang X. Learning feature relationships in CNN model via relational embedding convolution layer. Neural Netw 2024; 179:106510. [PMID: 39024707 DOI: 10.1016/j.neunet.2024.106510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 06/28/2024] [Accepted: 07/03/2024] [Indexed: 07/20/2024]
Abstract
Establishing the relationships among hierarchical visual attributes of objects in the visual world is crucial for human cognition. The classic convolution neural network (CNN) can successfully extract hierarchical features but ignore the relationships among features, resulting in shortcomings compared to humans in areas like interpretability and domain generalization. Recently, algorithms have introduced feature relationships by external prior knowledge and special auxiliary modules, which have been proven to bring multiple improvements in many computer vision tasks. However, prior knowledge is often difficult to obtain, and auxiliary modules bring additional consumption of computing and storage resources, which limits the flexibility and practicality of the algorithm. In this paper, we aim to drive the CNN model to learn the relationships among hierarchical deep features without prior knowledge and consumption increasing, while enhancing the fundamental performance of some aspects. Firstly, the task of learning the relationships among hierarchical features in CNN is defined and three key problems related to this task are pointed out, including the quantitative metric of connection intensity, the threshold of useless connections, and the updating strategy of relation graph. Secondly, Relational Embedding Convolution (RE-Conv) layer is proposed for the representation of feature relationships in convolution layer, followed by a scheme called use & disuse strategy which aims to address the three problems of feature relation learning. Finally, the improvements brought by the proposed feature relation learning scheme have been demonstrated through numerous experiments, including interpretability, domain generalization, noise robustness, and inference efficiency. In particular, the proposed scheme outperforms many state-of-the-art methods in the domain generalization community and can be seamlessly integrated with existing methods for further improvement. Meanwhile, it maintains comparable precision to the original CNN model while reducing floating point operations (FLOPs) by approximately 50%.
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Affiliation(s)
- Shengzhou Xiong
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China; National Key Laboratory of Multispectral Information Intelligent Processing Technology, Wuhan, 430074, China.
| | - Yihua Tan
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China; National Key Laboratory of Multispectral Information Intelligent Processing Technology, Wuhan, 430074, China.
| | - Guoyou Wang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China; National Key Laboratory of Multispectral Information Intelligent Processing Technology, Wuhan, 430074, China.
| | - Pei Yan
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China; National Key Laboratory of Multispectral Information Intelligent Processing Technology, Wuhan, 430074, China.
| | - Xuanyu Xiang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China; National Key Laboratory of Multispectral Information Intelligent Processing Technology, Wuhan, 430074, China.
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7
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Wang D, Li Z, Zhao K, Chen P, Yang F, Yao H, Zhou B, Wei Y, Lu J, Chen Y, Zhang X, Han Y, Wang P, Liu Y. Macroscale Gradient Dysfunction in Alzheimer's Disease: Patterns With Cognition Terms and Gene Expression Profiles. Hum Brain Mapp 2024; 45:e70046. [PMID: 39449114 PMCID: PMC11502409 DOI: 10.1002/hbm.70046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Revised: 09/27/2024] [Accepted: 10/02/2024] [Indexed: 10/26/2024] Open
Abstract
Macroscale functional gradient techniques provide a continuous coordinate system that extends from unimodal regions to transmodal higher-order networks. However, the alterations of these functional gradients in AD and their correlations with cognitive terms and gene expression profiles remain to be established. In the present study, we directly studied the functional gradients with functional MRI data from seven scanners. We adopted data-driven meta-analytic techniques to unveil AD-associated changes in the functional gradients. The principal primary-to-transmodal gradient was suppressed in AD. Compared to NCs, AD patients exhibited global connectome gradient alterations, including reduced gradient range and gradient variation, increased gradient scores in the somatomotor, ventral attention, and frontoparietal regions, and decreased in the default mode network. More importantly, the Gene Ontology terms of biological processes were significantly enriched in the potassium ion transport and protein-containing complex remodeling. Our compelling evidence provides a new perspective in understanding the connectome alterations in AD.
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Affiliation(s)
- Dawei Wang
- Department of RadiologyQilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong UniversityJinanChina
- Research Institute of Shandong UniversityMagnetic Field‐Free Medicine & Functional ImagingJinanChina
- Shandong Key Laboratory: Magnetic Field‐Free Medicine & Functional Imaging (MF)JinanChina
| | - Zhuangzhuang Li
- Queen Mary School HainanBeijing University of Posts and TelecommunicationsHainanChina
| | - Kun Zhao
- Queen Mary School HainanBeijing University of Posts and TelecommunicationsHainanChina
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
| | - Pindong Chen
- School of Artificial IntelligenceUniversity of Chinese Academy of Sciences, & Institute of Automation, Chinese Academy of SciencesBeijingChina
| | - Fan Yang
- CAS Key Laboratory of Molecular ImagingInstitute of AutomationBeijingChina
| | - Hongxiang Yao
- Department of Radiology, the Second Medical CentreNational Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Bo Zhou
- Department of Neurology, the Second Medical CentreNational Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Yongbin Wei
- Queen Mary School HainanBeijing University of Posts and TelecommunicationsHainanChina
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
| | - Jie Lu
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Yuqi Chen
- Affiliated HospitalBeijing University of Posts and TelecommunicationsBeijingChina
| | - Xi Zhang
- Department of Neurology, the Second Medical CentreNational Clinical Research Centre for Geriatric Diseases, Chinese PLA General HospitalBeijingChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- School of Biomedical EngineeringHainan UniversityHaikouChina
- Center of Alzheimer's DiseaseBeijing Institute for Brain DisordersBeijingChina
| | - Pan Wang
- Department of NeurologyTianjin Huanhu HospitalTianjinChina
| | - Yong Liu
- Queen Mary School HainanBeijing University of Posts and TelecommunicationsHainanChina
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8
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McGovern HT, Grimmer HJ, Doss MK, Hutchinson BT, Timmermann C, Lyon A, Corlett PR, Laukkonen RE. An Integrated theory of false insights and beliefs under psychedelics. COMMUNICATIONS PSYCHOLOGY 2024; 2:69. [PMID: 39242747 PMCID: PMC11332244 DOI: 10.1038/s44271-024-00120-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 07/23/2024] [Indexed: 09/09/2024]
Abstract
Psychedelics are recognised for their potential to re-orient beliefs. We propose a model of how psychedelics can, in some cases, lead to false insights and thus false beliefs. We first review experimental work on laboratory-based false insights and false memories. We then connect this to insights and belief formation under psychedelics using the active inference framework. We propose that subjective and brain-based alterations caused by psychedelics increases the quantity and subjective intensity of insights and thence beliefs, including false ones. We offer directions for future research in minimising the risk of false and potentially harmful beliefs arising from psychedelics. Ultimately, knowing how psychedelics may facilitate false insights and beliefs is crucial if we are to optimally leverage their therapeutic potential.
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Affiliation(s)
- H T McGovern
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia.
- The Cairnmillar Institute, Melbourne, VIC, Australia.
| | - H J Grimmer
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
| | - M K Doss
- Department of Psychiatry and Behavioral Sciences, Center for Psychedelic Research & Therapy, The University of Texas at Austin Dell Medical School, Austin, TX, USA
| | - B T Hutchinson
- Faculty of Behavioural and Movement Sciences, Cognitive Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - C Timmermann
- Division of Psychiatry, Department of Brain Sciences, Centre for Psychedelic Research, Imperial College London, London, UK
| | - A Lyon
- Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - P R Corlett
- Department of Psychiatry, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - R E Laukkonen
- Faculty of Health, Southern Cross University, Gold Coast, QLD, Australia
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9
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Yang D, Tan Y, Zhou Z, Ke Z, Huang L, Mo Y, Tang L, Mao C, Hu Z, Cheng Y, Shao P, Zhang B, Zhu X, Xu Y. Connectome gradient dysfunction contributes to white matter hyperintensity-related cognitive decline. CNS Neurosci Ther 2024; 30:e14843. [PMID: 38997814 PMCID: PMC11245402 DOI: 10.1111/cns.14843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 05/29/2024] [Accepted: 06/20/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Although white matter hyperintensity (WMH) is closely associated with cognitive decline, the precise neurobiological mechanisms underlying this relationship are not fully elucidated. Connectome studies have identified a primary-to-transmodal gradient in functional brain networks that support the spectrum from sensation to cognition. However, whether connectome gradient structure is altered as WMH progresses and how this alteration is associated with WMH-related cognitive decline remain unknown. METHODS A total of 758 WMH individuals completed cognitive assessment and resting-state functional MRI (rs-fMRI). The functional connectome gradient was reconstructed based on rs-fMRI by using a gradient decomposition framework. Interrelations among the spatial distribution of WMH, functional gradient measures, and specific cognitive domains were explored. RESULTS As the WMH volume increased, the executive function (r = -0.135, p = 0.001) and information-processing speed (r = -0.224, p = 0.001) became poorer, the gradient range (r = -0.099, p = 0.006), and variance (r = -0.121, p < 0.001) of the primary-to-transmodal gradient reduced. A narrower gradient range (r = 0.131, p = 0.001) and a smaller gradient variance (r = 0.136, p = 0.001) corresponded to a poorer executive function. In particular, the relationship between the frontal/occipital WMH and executive function was partly mediated by gradient range/variance of the primary-to-transmodal gradient. CONCLUSIONS These findings indicated that WMH volume, the primary-to-transmodal gradient, and cognition were interrelated. The detrimental effect of the frontal/occipital WMH on executive function was partly mediated by the decreased differentiation of the connectivity pattern between the primary and transmodal areas.
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Affiliation(s)
- Dan Yang
- Department of Neurology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yi Tan
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - ZhiXin Zhou
- Department of Neurology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhihong Ke
- Department of Neurology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Lili Huang
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yuting Mo
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Limoran Tang
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - ChengLu Mao
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zheqi Hu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yue Cheng
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Pengfei Shao
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xiaolei Zhu
- Department of Neurology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Department of Neurology, Nanjing Drum Tower Hospital, State Key Laboratory of Pharmaceutical Biotechnology and Institute of Translational Medicine for Brain Critical Diseases, Nanjing University, Nanjing, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Department of Neurology, Nanjing Drum Tower Hospital, State Key Laboratory of Pharmaceutical Biotechnology and Institute of Translational Medicine for Brain Critical Diseases, Nanjing University, Nanjing, China
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10
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He R, Al-Tamimi J, Sánchez-Benavides G, Montaña-Valverde G, Domingo Gispert J, Grau-Rivera O, Suárez-Calvet M, Minguillon C, Fauria K, Navarro A, Hinzen W. Atypical cortical hierarchy in Aβ-positive older adults and its reflection in spontaneous speech. Brain Res 2024; 1830:148806. [PMID: 38365129 DOI: 10.1016/j.brainres.2024.148806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 02/07/2024] [Indexed: 02/18/2024]
Abstract
Abnormal deposition of Aβ amyloid is an early neuropathological marker of Alzheimer's disease (AD), arising long ahead of clinical symptoms. Non-invasive measures of associated early neurofunctional changes, together with easily accessible behavioral readouts of these changes, could be of great clinical benefit. We pursued this aim by investigating large-scale cortical gradients of functional connectivity with functional MRI, which capture the hierarchical integration of cortical functions, together with acoustic-prosodic features from spontaneous speech, in cognitively unimpaired older adults with and without Aβ positivity (total N = 188). We predicted distortions of the cortical hierarchy associated with prosodic changes in the Aβ + group. Results confirmed substantially altered cortical hierarchies and less variability in these in the Aβ + group, together with an increase in quantitative prosodic measures, which correlated with gradient variability as well as digit span test scores. Overall, these findings confirm that long before the clinical stage and objective cognitive impairment, increased risk of cognitive decline as indexed by Aβ accumulation is marked by neurofunctional changes in the cortical hierarchy, which are related to automatically extractable speech patterns and alterations in working memory functions.
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Affiliation(s)
- Rui He
- Department of Translation & Language Sciences, Universitat Pompeu Fabra, 08018 Barcelona, Spain.
| | - Jalal Al-Tamimi
- Université Paris Cité, Laboratoire de Linguistique Formelle (LLF), CNRS, 75013 Paris, France
| | - Gonzalo Sánchez-Benavides
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | | | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Department of Medicine and Life Sciences, Universitat Pompeu Fabra, 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBERBBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Oriol Grau-Rivera
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain; Servei de Neurologia, Hospital del Mar, 08003 Barcelona, Spain
| | - Marc Suárez-Calvet
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain; Servei de Neurologia, Hospital del Mar, 08003 Barcelona, Spain
| | - Carolina Minguillon
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Karine Fauria
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Arcadi Navarro
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Catalan Institution of Research and Advanced Studies (ICREA), 08010 Barcelona, Spain; Department of Medicine and Life Sciences, Institute of Evolutionary Biology (UPF-CSIC), Universitat Pompeu Fabra, 08003 Barcelona, Spain; CRG, Centre for Genomic Regulation, Barcelona Institute of Science and Technology (BIST), 08003 Barcelona, Spain
| | - Wolfram Hinzen
- Department of Translation & Language Sciences, Universitat Pompeu Fabra, 08018 Barcelona, Spain; Catalan Institution of Research and Advanced Studies (ICREA), 08010 Barcelona, Spain
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11
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Zhou B, Zhao Y, Wu X. Differences of individual gray matter networks between MCI patients who converted to AD within 3 Years and nonconverters. Heliyon 2024; 10:e28874. [PMID: 38623255 PMCID: PMC11016615 DOI: 10.1016/j.heliyon.2024.e28874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 03/24/2024] [Accepted: 03/26/2024] [Indexed: 04/17/2024] Open
Abstract
Objective Here we aimed to explore the differences in individual gray matter (GM) networks at baseline in mild cognitive impairment patients who converted to Alzheimer's disease (AD) within 3 years (MCI-C) and nonconverters (MCI-NC). Materials and methods Data from 461 MCI patients (180 MCI-C and 281 MCI-NC) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject, a GM network was constructed using 3D-T1 imaging and the Kullback-Leibler divergence method. Gradient and topological analyses of individual GM networks were performed, and partial correlations were calculated to evaluate relationships among network properties, cognitive function, and apolipoprotein E (APOE) €4 alleles. Subsequently, a support vector machine (SVM) model was constructed to discriminate the MCI-C and MCI-NC patients at baseline. Results The gradient analysis revealed that the principal gradient score distribution was more compressed in the MCI-C group than in the MCI-NC group, with scores for the left lingual gyrus, right fusiform gyrus and left middle temporal gyrus being increased in the MCI-C group (p < 0.05, FDR corrected). The topological analysis showed significant differences in nodal efficiency in four nodes between the two groups. Furthermore, the regional gradient scores or nodal efficiency were found to be significantly related to the neuropsychological test scores, and the left middle temporal gyrus gradient scores were positively associated with the number of APOE €4 alleles (r = 0.192, p = 0.002). Ultimately, the SVM model achieved a balanced accuracy of 79.4% in classifying MCI-C and MCI-NC patients (p < 0.001). Conclusion The whole-brain GM network hierarchy in the MCI-C group was more compressed than that in the MCI-NC group, suggesting more serious cognitive impairments in the MCI-C group. The left middle temporal gyrus gradient scores were related to both cognitive function and APOE €4 alleles, thus serving as potential biomarkers distinguishing MCI-C from MCI-NC at baseline.
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Affiliation(s)
- Baiwan Zhou
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yueqi Zhao
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaojia Wu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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12
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Riddle J, Schooler JW. Hierarchical consciousness: the Nested Observer Windows model. Neurosci Conscious 2024; 2024:niae010. [PMID: 38504828 PMCID: PMC10949963 DOI: 10.1093/nc/niae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/31/2024] [Accepted: 02/26/2024] [Indexed: 03/21/2024] Open
Abstract
Foremost in our experience is the intuition that we possess a unified conscious experience. However, many observations run counter to this intuition: we experience paralyzing indecision when faced with two appealing behavioral choices, we simultaneously hold contradictory beliefs, and the content of our thought is often characterized by an internal debate. Here, we propose the Nested Observer Windows (NOW) Model, a framework for hierarchical consciousness wherein information processed across many spatiotemporal scales of the brain feeds into subjective experience. The model likens the mind to a hierarchy of nested mosaic tiles-where an image is composed of mosaic tiles, and each of these tiles is itself an image composed of mosaic tiles. Unitary consciousness exists at the apex of this nested hierarchy where perceptual constructs become fully integrated and complex behaviors are initiated via abstract commands. We define an observer window as a spatially and temporally constrained system within which information is integrated, e.g. in functional brain regions and neurons. Three principles from the signal analysis of electrical activity describe the nested hierarchy and generate testable predictions. First, nested observer windows disseminate information across spatiotemporal scales with cross-frequency coupling. Second, observer windows are characterized by a high degree of internal synchrony (with zero phase lag). Third, observer windows at the same spatiotemporal level share information with each other through coherence (with non-zero phase lag). The theoretical framework of the NOW Model accounts for a wide range of subjective experiences and a novel approach for integrating prominent theories of consciousness.
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Affiliation(s)
- Justin Riddle
- Department of Psychology, Florida State University, 1107 W Call St, Tallahassee, FL 32304, USA
| | - Jonathan W Schooler
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, Psychological & Brain Sciences, Santa Barbara, CA 93106, USA
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13
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Tong Z, Zhang J, Xing C, Xu X, Wu Y, Salvi R, Yin X, Zhao F, Chen YC, Cai Y. Reorganization of the cortical connectome functional gradient in age-related hearing loss. Neuroimage 2023; 284:120475. [PMID: 38013009 DOI: 10.1016/j.neuroimage.2023.120475] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 11/10/2023] [Accepted: 11/24/2023] [Indexed: 11/29/2023] Open
Abstract
Age-related hearing loss (ARHL), one of the most common sensory deficits in elderly individuals, is a risk factor for dementia; however, it is unclear how ARHL affects the decline in cognitive function. To address this issue, a connectome gradient framework was used to identify critical features of information integration between sensory and cognitive processing centers using resting-state functional magnetic resonance imaging (rs-fMRI) data from 40 individuals with ARHL and 36 healthy controls (HCs). The first three functional gradient alterations associated with ARHL were investigated at the global, network and regional levels. Using a support vector machine (SVM) model, our analysis distinguished individuals with ARHL with normal cognitive function from those with cognitive decline. Compared to HCs, individuals with ARHL had a contracted principal primary-to-transmodal gradient axis, especially in the visual and default mode networks, with an altered gradient explained ratio and variance. Among individuals with ARHL, cognitive decline was detected in the visual network in the principal gradient as well as in the limbic, salience and default mode networks in the third gradient (salience to frontoparietal/default mode). These results suggest that ARHL is associated with disrupted information processing from the primary sensory networks to higher-order cognitive networks and highlight the key nodes closely associated with cognitive decline during cognitive processing in ARHL, providing new insights into the mechanism of cognitive impairment and suggesting potential treatments related to ARHL.
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Affiliation(s)
- Zhaopeng Tong
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China; Institute of Hearing and Speech-Language Science, Sun Yat-sen University, Guangzhou, China
| | - Juan Zhang
- Department of Neurology, Nanjing Yuhua Hospital, Yuhua Branch of Nanjing First Hospital, Nanjing, China
| | - Chunhua Xing
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Xiaomin Xu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Yuanqing Wu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Richard Salvi
- Center for Hearing and Deafness, University at Buffalo, The State University of New York, Buffalo, United States
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Fei Zhao
- Department of Speech and Language Therapy and Hearing Science, Cardiff Metropolitan University, Cardiff, United Kingdom
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China.
| | - Yuexin Cai
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China; Institute of Hearing and Speech-Language Science, Sun Yat-sen University, Guangzhou, China.
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14
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Lin S, Qi R, Lin X, Chen S, Zhang L, Qiu Y. Association Between MRI-Assessed Patterns of Connectome Gradient and Gene-Expression Profiles in Two Independent Patient Cohorts With Hepatitis B Virus-Related Cirrhosis. J Magn Reson Imaging 2023; 58:1863-1874. [PMID: 37022091 DOI: 10.1002/jmri.28732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 04/07/2023] Open
Abstract
BACKGROUND Patients with hepatitis B virus-related cirrhosis (HBV-RC) exhibit progressive neurologic dysfunction from primary sensorimotor to high-order cognition, as their disease advances. However, the exact neurobiologic mechanisms and the potential association with gene-expression profiles are not fully understood. PURPOSE To explore the hierarchical disorganization in the large-scale functional connectomes in HBV-RC patients and to investigate its potential underlying molecular basis. STUDY TYPE Prospective. POPULATION Fifty HBV-RC patients and 40 controls (Cohort 1) and 30 HBV-RC patients and 38 controls (Cohort 2). FIELD STRENGTH/SEQUENCE Gradient-echo echo-planar and fast field echo sequences at 3.0 T (Cohort 1) and 1.5 T (Cohort 2). ASSESSMENT Data were processed with Dpabi and the BrainSpace package. Gradient scores were evaluated from global to voxel level. Cognitive measurement and patients grouping were based on psychometric hepatic encephalopathy scores. The whole-brain microarray-based gene-expression data were obtained from the AIBS website. STATISTICAL TESTS One-way ANOVA, chi-square test, two-sample t-test, Kruskal-Wallis test, Spearman's correlation coefficient (r), the gaussian random field correction, false discovery rate (FDR) correction and the Bonferroni correction. Significance level: P < 0.05. RESULTS HBV-RC patients exhibited a robust and replicable connectome gradient dysfunction, which was significantly associated with the gene-expression profiles in both cohorts (r = 0.52 and r = 0.56, respectively). The most correlated genes were enriched in γ-aminobutyric acid (GABA) and GABA-related receptor genes (FDR q value <0.05). Moreover, the connectome gradient dysfunction at network level observed in HBV-RC patients correlated with their poor cognitive performance (Cohort 2: visual network, r = -0.56; subcortical network, r = 0.66; frontoparietal network, r = 0.51). DATA CONCLUSION HBV-RC patients had hierarchical disorganization in the large-scale functional connectomes, which may underly their cognitive impairment. In addition, we showed the potential molecular mechanism of the connectome gradient dysfunction, which suggested the importance of GABA and GABA-related receptor genes. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Shiwei Lin
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Rongfeng Qi
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Xiaoshan Lin
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Shengli Chen
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Yingwei Qiu
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, Guangdong, China
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15
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Diveica V, Riedel MC, Salo T, Laird AR, Jackson RL, Binney RJ. Graded functional organization in the left inferior frontal gyrus: evidence from task-free and task-based functional connectivity. Cereb Cortex 2023; 33:11384-11399. [PMID: 37833772 PMCID: PMC10690868 DOI: 10.1093/cercor/bhad373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/17/2023] [Accepted: 09/18/2023] [Indexed: 10/15/2023] Open
Abstract
The left inferior frontal gyrus has been ascribed key roles in numerous cognitive domains, such as language and executive function. However, its functional organization is unclear. Possibilities include a singular domain-general function, or multiple functions that can be mapped onto distinct subregions. Furthermore, spatial transition in function may be either abrupt or graded. The present study explored the topographical organization of the left inferior frontal gyrus using a bimodal data-driven approach. We extracted functional connectivity gradients from (i) resting-state fMRI time-series and (ii) coactivation patterns derived meta-analytically from heterogenous sets of task data. We then sought to characterize the functional connectivity differences underpinning these gradients with seed-based resting-state functional connectivity, meta-analytic coactivation modeling and functional decoding analyses. Both analytic approaches converged on graded functional connectivity changes along 2 main organizational axes. An anterior-posterior gradient shifted from being preferentially associated with high-level control networks (anterior functional connectivity) to being more tightly coupled with perceptually driven networks (posterior). A second dorsal-ventral axis was characterized by higher connectivity with domain-general control networks on one hand (dorsal functional connectivity), and with the semantic network, on the other (ventral). These results provide novel insights into an overarching graded functional organization of the functional connectivity that explains its role in multiple cognitive domains.
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Affiliation(s)
- Veronica Diveica
- Department of Psychology & Cognitive Neuroscience Institute, Bangor University, Bangor, Wales LL57 2AS, United Kingdom
- Department of Neurology and Neurosurgery & Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Michael C Riedel
- Department of Physics, Florida International University, Miami, FL 33199, United States
| | - Taylor Salo
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, United States
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL 33199, United States
| | - Rebecca L Jackson
- Department of Psychology & York Biomedical Research Institute, University of York, York, YO10 5DD, United Kingdom
| | - Richard J Binney
- Department of Psychology & Cognitive Neuroscience Institute, Bangor University, Bangor, Wales LL57 2AS, United Kingdom
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Chowdhury A, van Lutterveld R, Laukkonen RE, Slagter HA, Ingram DM, Sacchet MD. Investigation of advanced mindfulness meditation "cessation" experiences using EEG spectral analysis in an intensively sampled case study. Neuropsychologia 2023; 190:108694. [PMID: 37777153 PMCID: PMC10843092 DOI: 10.1016/j.neuropsychologia.2023.108694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 09/20/2023] [Accepted: 09/22/2023] [Indexed: 10/02/2023]
Abstract
Mindfulness meditation is a contemplative practice informed by Buddhism that targets the development of present-focused awareness and non-judgment of experience. Interest in mindfulness is burgeoning, and it has been shown to be effective in improving mental and physical health in clinical and non-clinical contexts. In this report, for the first time, we used electroencephalography (EEG) combined with a neurophenomenological approach to examine the neural signature of "cessation" events, which are dramatic experiences of complete discontinuation in awareness similar to the loss of consciousness, which are reported to be experienced by very experienced meditators, and are proposed to be evidence of mastery of mindfulness meditation. We intensively sampled these cessations as experienced by a single advanced meditator (with over 23,000 h of meditation training) and analyzed 37 cessation events collected in 29 EEG sessions between November 12, 2019, and March 11, 2020. Spectral analyses of the EEG data surrounding cessations showed that these events were marked by a large-scale alpha-power decrease starting around 40 s before their onset, and that this alpha-power was lowest immediately following a cessation. Region-of-interest (ROI) based examination of this finding revealed that this alpha-suppression showed a linear decrease in the occipital and parietal regions of the brain during the pre-cessation time period. Additionally, there were modest increases in theta power for the central, parietal, and right temporal ROIs during the pre-cessation timeframe, whereas power in the Delta and Beta frequency bands were not significantly different surrounding cessations. By relating cessations to objective and intrinsic measures of brain activity (i.e., EEG power) that are related to consciousness and high-level psychological functioning, these results provide evidence for the ability of experienced meditators to voluntarily modulate their state of consciousness and lay the foundation for studying these unique states using a neuroscientific approach.
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Affiliation(s)
- Avijit Chowdhury
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Remko van Lutterveld
- Brain Research and Innovation Centre, Dutch Ministry of Defence and Department of Psychiatry, University Medical Center, Utrecht, the Netherlands.
| | - Ruben E Laukkonen
- Faculty of Health, Southern Cross University, Gold Coast, QLD, Australia.
| | - Heleen A Slagter
- Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, the Netherlands; Institute for Brain and Behavior, Vrije Universiteit Amsterdam, the Netherlands.
| | | | - Matthew D Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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17
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Leite S, Mota B, Silva AR, Commons ML, Miller PM, Rodrigues PP. Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity. PLoS One 2023; 18:e0290743. [PMID: 37651418 PMCID: PMC10470958 DOI: 10.1371/journal.pone.0290743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/14/2023] [Indexed: 09/02/2023] Open
Abstract
Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing task, called the balance beam problem. Previous simulations of this developmental task do not reflect a necessary premise underlying development: a more complex structure can be built out of less complex ones, while ensuring that the more complex structure does not replace the less complex one. In order to address this necessity, we segregated the input set by subsets of increasing Orders of Hierarchical Complexity. This is a complexity measure that has been extensively shown to underlie the complexity behavior and hypothesized to underlie the complexity of the neural structure of the brain. After segregating the input set, minimal neural network models were trained separately for each input subset, and adjacent complexity models were analyzed sequentially to observe whether there was a structural progression. Results show that three different network structural progressions were found, performing with similar accuracy, pointing towards self-organization. Also, more complex structures could be built out of less complex ones without substituting them, successfully addressing catastrophic forgetting and leveraging performance of previous models in the literature. Furthermore, the model structures trained on the two highest complexity subsets performed better than simulations of the balance beam present in the literature. As a major contribution, this work was successful in addressing hierarchical complexity structural growth in neural networks, and is the first that segregates inputs by Order of Hierarchical Complexity. Since this measure can be applied to all domains of data, the present method can be applied to future simulations, systematizing the simulation of developmental and evolutionary structural growth in neural networks.
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Affiliation(s)
- Sofia Leite
- CINTESIS – Center for Health Technology and Services Research, Porto, Portugal
- Dare Association, Inc. Boston, Massachusetts, United States of America
| | - Bruno Mota
- Laboratory of Experimental Mathematics and Theoretical Biology, Physics Institute, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil
| | - António Ramos Silva
- Department of Mechanical Engineering, Faculty of Engineering University of Porto, Porto, Portugal
- INEGI Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Michael Lamport Commons
- Dare Association, Inc. Boston, Massachusetts, United States of America
- Beth Israel Deaconess Medical Center, Harvard Medical School, Cambridge, Massachusetts, United States of America
| | - Patrice Marie Miller
- Dare Association, Inc. Boston, Massachusetts, United States of America
- Department of Psychology, Salem State University, Salem, Massachusetts, United States of America
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18
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Shiwei L, Xiaojing Z, Yingli Z, Shengli C, Xiaoshan L, Ziyun X, Gangqiang H, Yingwei Q. Cortical hierarchy disorganization in major depressive disorder and its association with suicidality. Front Psychiatry 2023; 14:1140915. [PMID: 37168085 PMCID: PMC10165114 DOI: 10.3389/fpsyt.2023.1140915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/07/2023] [Indexed: 05/13/2023] Open
Abstract
Objectives To explore the suicide risk-specific disruption of cortical hierarchy in major depressive disorder (MDD) patients with diverse suicide risks. Methods Ninety-two MDD patients with diverse suicide risks and 38 matched controls underwent resting-state functional MRI. Connectome gradient analysis and stepwise functional connectivity (SFC) analysis were used to characterize the suicide risk-specific alterations of cortical hierarchy in MDD patients. Results Relative to controls, patients with suicide attempts (SA) had a prominent compression from the sensorimotor system; patients with suicide ideations (SI) had a prominent compression from the higher-level systems; non-suicide patients had a compression from both the sensorimotor system and higher-level systems, although it was less prominent relative to SA and SI patients. SFC analysis further validated this depolarization phenomenon. Conclusion This study revealed MDD patients had suicide risk-specific disruptions of cortical hierarchy, which advance our understanding of the neuromechanisms of suicidality in MDD patients.
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Affiliation(s)
- Lin Shiwei
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Zhang Xiaojing
- Guangdong Provincial Key Laboratory of Genome Stability and Disease Prevention and Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Zhang Yingli
- Department of Depressive Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong, China
| | - Chen Shengli
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Lin Xiaoshan
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Xu Ziyun
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
| | - Hou Gangqiang
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
| | - Qiu Yingwei
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
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19
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Dong D, Yao D, Wang Y, Hong SJ, Genon S, Xin F, Jung K, He H, Chang X, Duan M, Bernhardt BC, Margulies DS, Sepulcre J, Eickhoff SB, Luo C. Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia. Psychol Med 2023; 53:771-784. [PMID: 34100349 DOI: 10.1017/s0033291721002129] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. METHODS We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls). RESULTS We demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal-parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05). CONCLUSIONS The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory-motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.
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Affiliation(s)
- Debo Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China
| | - Yulin Wang
- Faculty of Psychological and Educational Sciences, Department of Experimental and Applied Psychology, Vrije Universiteit Brussel, Belgium
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Belgium
| | - Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, NY, USA
- Department of Biomedical Engineering, Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, South Korea
| | - Sarah Genon
- Institute for Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Fei Xin
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
| | - Kyesam Jung
- Institute for Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
- Department of Psychiatry, The Fourth People's Hospital of Chengdu, Chengdu, China
| | - Xuebin Chang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
| | - Mingjun Duan
- Department of Psychiatry, The Fourth People's Hospital of Chengdu, Chengdu, China
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) UMR 7225, Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Jorge Sepulcre
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
- Department of Neurology, Brain Disorders and Brain Function Key Laboratory, First Affiliated Hospital of Hainan Medical University, Haikou, China
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Ibbotson P. The Development of Executive Function: Mechanisms of Change and Functional Pressures. JOURNAL OF COGNITION AND DEVELOPMENT 2023. [DOI: 10.1080/15248372.2022.2160719] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Paul Ibbotson
- School of Education, Childhood, Youth and Sport, The Open University, Milton Keynes, UK
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21
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Kody E, Diwadkar VA. Magnocellular and parvocellular contributions to brain network dysfunction during learning and memory: Implications for schizophrenia. J Psychiatr Res 2022; 156:520-531. [PMID: 36351307 DOI: 10.1016/j.jpsychires.2022.10.055] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 10/24/2022] [Accepted: 10/28/2022] [Indexed: 11/07/2022]
Abstract
Memory deficits are core features of schizophrenia, and a central aim in biological psychiatry is to identify the etiology of these deficits. Scrutiny is naturally focused on the dorsolateral prefrontal cortex and the hippocampal cortices, given these structures' roles in memory and learning. The fronto-hippocampal framework is valuable but restrictive. Network-based underpinnings of learning and memory are substantially diverse and include interactions between hetero-modal and early sensory networks. Thus, a loss of fidelity in sensory information may impact memorial and cognitive processing in higher-order brain sub-networks, becoming a sensory source for learning and memory deficits. In this overview, we suggest that impairments in magno- and parvo-cellular visual pathways result in degraded inputs to core learning and memory networks. The ascending cascade of aberrant neural events significantly contributes to learning and memory deficits in schizophrenia. We outline the network bases of these effects, and suggest that any network perspectives of dysfunction in schizophrenia must assess the impact of impaired perceptual contributions. Finally, we speculate on how this framework enriches the space of biomarkers and expands intervention strategies to ameliorate this prototypical disconnection syndrome.
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Affiliation(s)
- Elizabeth Kody
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, USA
| | - Vaibhav A Diwadkar
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, USA.
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22
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Niedernhuber M, Raimondo F, Sitt JD, Bekinschtein TA. Sensory Target Detection at Local and Global Timescales Reveals a Hierarchy of Supramodal Dynamics in the Human Cortex. J Neurosci 2022; 42:8729-8741. [PMID: 36223999 PMCID: PMC9671580 DOI: 10.1523/jneurosci.0658-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/24/2022] [Accepted: 07/20/2022] [Indexed: 11/21/2022] Open
Abstract
To ensure survival in a dynamic environment, the human neocortex monitors input streams from different sensory organs for important sensory events. Which principles govern whether different senses share common or modality-specific brain networks for sensory target detection? We examined whether complex targets evoke sustained supramodal activity while simple targets rely on modality-specific networks with short-lived supramodal contributions. In a series of hierarchical multisensory target detection studies (n = 77, of either sex) using EEG, we applied a temporal cross-decoding approach to dissociate supramodal and modality-specific cortical dynamics elicited by rule-based global and feature-based local sensory deviations within and between the visual, somatosensory, and auditory modality. Our data show that each sense implements a cortical hierarchy orchestrating supramodal target detection responses, which operate at local and global timescales in successive processing stages. Across different sensory modalities, simple feature-based sensory deviations presented in temporal vicinity to a monotonous input stream triggered a mismatch negativity-like local signal which decayed quickly and early, whereas complex rule-based targets tracked across time evoked a P3b-like global neural response which generalized across a late time window. Converging results from temporal cross-modality decoding analyses across different datasets, we reveal that global neural responses are sustained in a supramodal higher-order network, whereas local neural responses canonically thought to rely on modality-specific regions evolve into short-lived supramodal activity. Together, our findings demonstrate that cortical organization largely follows a gradient in which short-lived modality-specific as well as supramodal processes dominate local responses, whereas higher-order processes encode temporally extended abstract supramodal information fed forward from modality-specific cortices.SIGNIFICANCE STATEMENT Each sense supports a cortical hierarchy of processes tracking deviant sensory events at multiple timescales. Conflicting evidence produced a lively debate around which of these processes are supramodal. Here, we manipulated the temporal complexity of auditory, tactile, and visual targets to determine whether cortical local and global ERP responses to sensory targets share cortical dynamics between the senses. Using temporal cross-decoding, we found that temporally complex targets elicit a supramodal sustained response. Conversely, local responses to temporally confined targets typically considered modality-specific rely on early short-lived supramodal activation. Our finding provides evidence for a supramodal gradient supporting sensory target detection in the cortex, with implications for multiple fields in which these responses are studied (e.g., predictive coding, consciousness, and attention).
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Affiliation(s)
- Maria Niedernhuber
- Cambridge Consciousness and Cognition Lab, Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, United Kingdom
- Body, Self, and Plasticity Lab, Department of Psychology, University of Zurich, Zurich, 8050, Switzerland
| | - Federico Raimondo
- Brain and Spine Institute, Pitiè Salpêtrière Hospital, Paris, 75013, France
- National Institute of Health and Medical Research, Paris, 75013, France
- Institute of Neuroscience and Medicine, Brain & Behaviour, Research Centre Jülich, Jülich, 52425, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, 40225, Germany
| | - Jacobo D. Sitt
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, APHP, Hôpital de la Pitié Salpêtrière, Paris, 75013, France
| | - Tristan A. Bekinschtein
- Cambridge Consciousness and Cognition Lab, Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, United Kingdom
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23
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Dornaika F. Deep, Flexible Data Embedding with Graph-Based Feature Propagation for Semi-supervised Classification. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10056-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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24
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Litwińczuk MC, Trujillo-Barreto N, Muhlert N, Cloutman L, Woollams A. Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function. Neuroimage 2022; 262:119531. [PMID: 35931312 DOI: 10.1016/j.neuroimage.2022.119531] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 07/20/2022] [Accepted: 08/01/2022] [Indexed: 11/29/2022] Open
Abstract
The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve the fit of regression models of cognitive performance. Principal Component Analysis (PCA) was first applied to cognitive data from the Human Connectome Project to identify latent cognitive components: Executive Function, Self-regulation, Language, Encoding and Sequence Processing. A Principal Component Regression approach with embedded Step-Wise Regression (SWR-PCR) was then used to fit regression models of each cognitive domain based on structural (SC), functional (FC) or combined structural-functional (CC) connectivity. Executive Function was best explained by the CC model. Self-regulation was equally well explained by SC and FC. Language was equally well explained by CC and FC models. Encoding and Sequence Processing were best explained by SC. Evaluation of out-of-sample models' skill via cross-validation showed that SC, FC and CC produced generalisable models of Language performance. SC models performed most effectively at predicting Language performance in unseen sample. Executive Function was most effectively predicted by SC models, followed only by CC models. Self-regulation was only effectively predicted by CC models and Sequence Processing was only effectively predicted by FC models. The present study demonstrates that integrating structural and functional connectivity can help explaining cognitive performance, but that the added explanatory value (in sample) may be domain-specific and can come at the expense of reduced generalisation performance (out-of-sample).
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Affiliation(s)
| | | | - Nils Muhlert
- Division of Neuroscience and Experimental Psychology, University of Manchester, UK
| | - Lauren Cloutman
- Division of Neuroscience and Experimental Psychology, University of Manchester, UK
| | - Anna Woollams
- Division of Neuroscience and Experimental Psychology, University of Manchester, UK
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25
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Ochi R, Ueno F, Sakuma M, Tani H, Tsugawa S, Graff-Guerrero A, Uchida H, Mimura M, Oshima S, Matsushita S, Nakajima S. Patterns of functional connectivity alterations induced by alcohol reflect somatostatin interneuron expression in the human cerebral cortex. Sci Rep 2022; 12:7896. [PMID: 35550587 PMCID: PMC9098480 DOI: 10.1038/s41598-022-12035-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 04/25/2022] [Indexed: 12/12/2022] Open
Abstract
Acute alcohol administration affects functional connectivity, yet the underlying mechanism is unknown. Previous work suggested that a moderate dose of alcohol reduces the activity of gamma-aminobutyric acidergic (GABAergic) interneurons, thereby leading to a state of pyramidal disinhibition and hyperexcitability. The present study aims to relate alcohol-induced changes in functional connectivity to regional genetic markers of GABAergic interneurons. Healthy young adults (N = 15, 5 males) underwent resting state functional MRI scanning prior to alcohol administration, immediately and 90 min after alcohol administration. Functional connectivity density mapping was performed to quantify alcohol-induced changes in resting brain activity between conditions. Patterns of differences between conditions were related to regional genetic markers that express the primary GABAergic cortical interneuron subtypes (parvalbumin, somatostatin, and 5-hydroxytryptamine receptor 3A) obtained from the Allen Human Brain Atlas. Acute alcohol administration increased local functional connectivity density within the visual cortex, sensorimotor cortex, thalamus, striatum, and cerebellum. Patterns of alcohol-induced changes in local functional connectivity density inversely correlated with somatostatin cortical gene expression. These findings suggest that somatostatin-expressing interneurons modulate alcohol-induced changes in functional connectivity in healthy individuals.
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Affiliation(s)
- Ryo Ochi
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Fumihiko Ueno
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Mutsuki Sakuma
- National Hospital Organization Kurihama Medical and Addiction Center, Kanagawa, Japan
| | - Hideaki Tani
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Sakiko Tsugawa
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Ariel Graff-Guerrero
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Hiroyuki Uchida
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Masaru Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Shunji Oshima
- Sustainable Technology Laboratories, Asahi Quality and Innovations, Ltd., Ibaraki, Japan
| | - Sachio Matsushita
- National Hospital Organization Kurihama Medical and Addiction Center, Kanagawa, Japan
| | - Shinichiro Nakajima
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
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Çatal Y, Gomez-Pilar J, Northoff G. Intrinsic dynamics and topography of sensory input systems. Cereb Cortex 2022; 32:4592-4604. [PMID: 35094077 PMCID: PMC9614113 DOI: 10.1093/cercor/bhab504] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/07/2021] [Accepted: 12/08/2021] [Indexed: 02/01/2023] Open
Abstract
The brain is continuously bombarded by external stimuli, which are processed in different input systems. The intrinsic features of these sensory input systems remain yet unclear. Investigating topography and dynamics of input systems is the goal of our study in order to better understand the intrinsic features that shape their neural processing. Using a functional magnetic resonance imaging dataset, we measured neural topography and dynamics of the input systems during rest and task states. Neural dynamics were probed by scale-free activity, measured with the power-law exponent (PLE), as well as by order/disorder as measured with sample entropy (SampEn). Our main findings during both rest and task states are: 1) differences in neural dynamics (PLE, SampEn) between regions within each of the three sensory input systems 2) differences in topography and dynamics among the three input systems; 3) PLE and SampEn correlate and, as demonstrated in simulation, show non-linear relationship in the critical range of PLE; 4) scale-free activity during rest mediates the transition of SampEn from rest to task as probed in a mediation model. We conclude that the sensory input systems are characterized by their intrinsic topographic and dynamic organization which, through scale-free activity, modulates their input processing.
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Affiliation(s)
- Yasir Çatal
- The Royal's Institute of Mental Health Research & University of Ottawa. Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, Ottawa, 145 Carling Avenue, Rm. 6435, Ottawa, Ontario K1Z 7K4, Canada
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, Higher Technical School of Telecommunications Engineering, University of Valladolid, Valladolid 47011, Spain,Centro de Investigación Biomédica en Red—Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid 28029, Spain
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Arvin S, Glud AN, Yonehara K. Short- and Long-Range Connections Differentially Modulate the Dynamics and State of Small-World Networks. Front Comput Neurosci 2022; 15:783474. [PMID: 35145389 PMCID: PMC8821822 DOI: 10.3389/fncom.2021.783474] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 12/03/2021] [Indexed: 11/13/2022] Open
Abstract
The human brain contains billions of neurons that flexibly interconnect to support local and global computational spans. As neuronal activity propagates through the neural medium, it approaches a critical state hedged between ordered and disordered system regimes. Recent work demonstrates that this criticality coincides with the small-world topology, a network arrangement that accommodates both local (subcritical) and global (supercritical) system properties. On one hand, operating near criticality is thought to offer several neurocomputational advantages, e.g., high-dynamic range, efficient information capacity, and information transfer fidelity. On the other hand, aberrations from the critical state have been linked to diverse pathologies of the brain, such as post-traumatic epileptiform seizures and disorders of consciousness. Modulation of brain activity, through neuromodulation, presents an attractive mode of treatment to alleviate such neurological disorders, but a tractable neural framework is needed to facilitate clinical progress. Using a variation on the generative small-world model of Watts and Strogatz and Kuramoto's model of coupled oscillators, we show that the topological and dynamical properties of the small-world network are divided into two functional domains based on the range of connectivity, and that these domains play distinct roles in shaping the behavior of the critical state. We demonstrate that short-range network connections shape the dynamics of the system, e.g., its volatility and metastability, whereas long-range connections drive the system state, e.g., a seizure. Together, these findings lend support to combinatorial neuromodulation approaches that synergistically normalize the system dynamic while mobilizing the system state.
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Affiliation(s)
- Simon Arvin
- Department of Neurosurgery, Center for Experimental Neuroscience – CENSE, Institute of Clinical Medicine, Aarhus University Hospital, Aarhus C, Denmark
- Department of Biomedicine, Danish Research Institute of Translational Neuroscience – DANDRITE, Nordic-EMBL Partnership for Molecular Medicine, Aarhus University, Aarhus C, Denmark
- *Correspondence: Simon Arvin
| | - Andreas Nørgaard Glud
- Department of Neurosurgery, Center for Experimental Neuroscience – CENSE, Institute of Clinical Medicine, Aarhus University Hospital, Aarhus C, Denmark
| | - Keisuke Yonehara
- Department of Biomedicine, Danish Research Institute of Translational Neuroscience – DANDRITE, Nordic-EMBL Partnership for Molecular Medicine, Aarhus University, Aarhus C, Denmark
- Multiscale Sensory Structure Laboratory, National Institute of Genetics, Mishima, Japan
- Department of Genetics, The Graduate University for Advanced Studies (SOKENDAI), Mishima, Japan
- Keisuke Yonehara
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Abstract
AbstractThis paper traces the empiricist program from early debates between nativism and behaviorism within philosophy, through debates about early connectionist approaches within the cognitive sciences, and up to their recent iterations within the domain of deep learning. We demonstrate how current debates on the nature of cognition via deep network architecture echo some of the core issues from the Chomsky/Quine debate and investigate the strength of support offered by these various lines of research to the empiricist standpoint. Referencing literature from both computer science and philosophy, we conclude that the current state of deep learning does not offer strong encouragement to the empiricist side despite some arguments to the contrary.
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Golesorkhi M, Gomez-Pilar J, Zilio F, Berberian N, Wolff A, Yagoub MCE, Northoff G. The brain and its time: intrinsic neural timescales are key for input processing. Commun Biol 2021; 4:970. [PMID: 34400800 PMCID: PMC8368044 DOI: 10.1038/s42003-021-02483-6] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 07/19/2021] [Indexed: 02/07/2023] Open
Abstract
We process and integrate multiple timescales into one meaningful whole. Recent evidence suggests that the brain displays a complex multiscale temporal organization. Different regions exhibit different timescales as described by the concept of intrinsic neural timescales (INT); however, their function and neural mechanisms remains unclear. We review recent literature on INT and propose that they are key for input processing. Specifically, they are shared across different species, i.e., input sharing. This suggests a role of INT in encoding inputs through matching the inputs' stochastics with the ongoing temporal statistics of the brain's neural activity, i.e., input encoding. Following simulation and empirical data, we point out input integration versus segregation and input sampling as key temporal mechanisms of input processing. This deeply grounds the brain within its environmental and evolutionary context. It carries major implications in understanding mental features and psychiatric disorders, as well as going beyond the brain in integrating timescales into artificial intelligence.
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Affiliation(s)
- Mehrshad Golesorkhi
- grid.28046.380000 0001 2182 2255School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada ,grid.28046.380000 0001 2182 2255Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Javier Gomez-Pilar
- grid.5239.d0000 0001 2286 5329Biomedical Engineering Group, University of Valladolid, Valladolid, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain
| | - Federico Zilio
- grid.5608.b0000 0004 1757 3470Department of Philosophy, Sociology, Education and Applied Psychology, University of Padova, Padua, Italy
| | - Nareg Berberian
- grid.28046.380000 0001 2182 2255Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Annemarie Wolff
- grid.28046.380000 0001 2182 2255Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Mustapha C. E. Yagoub
- grid.28046.380000 0001 2182 2255School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
| | - Georg Northoff
- grid.28046.380000 0001 2182 2255Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada ,grid.410595.c0000 0001 2230 9154Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China ,grid.13402.340000 0004 1759 700XMental Health Centre, Zhejiang University School of Medicine, Hangzhou, Zhejiang China
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Koltcov S, Ignatenko V, Terpilovskii M, Rosso P. Analysis and tuning of hierarchical topic models based on Renyi entropy approach. PeerJ Comput Sci 2021; 7:e608. [PMID: 34401473 PMCID: PMC8330431 DOI: 10.7717/peerj-cs.608] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/31/2021] [Indexed: 06/13/2023]
Abstract
Hierarchical topic modeling is a potentially powerful instrument for determining topical structures of text collections that additionally allows constructing a hierarchy representing the levels of topic abstractness. However, parameter optimization in hierarchical models, which includes finding an appropriate number of topics at each level of hierarchy, remains a challenging task. In this paper, we propose an approach based on Renyi entropy as a partial solution to the above problem. First, we introduce a Renyi entropy-based metric of quality for hierarchical models. Second, we propose a practical approach to obtaining the "correct" number of topics in hierarchical topic models and show how model hyperparameters should be tuned for that purpose. We test this approach on the datasets with the known number of topics, as determined by the human mark-up, three of these datasets being in the English language and one in Russian. In the numerical experiments, we consider three different hierarchical models: hierarchical latent Dirichlet allocation model (hLDA), hierarchical Pachinko allocation model (hPAM), and hierarchical additive regularization of topic models (hARTM). We demonstrate that the hLDA model possesses a significant level of instability and, moreover, the derived numbers of topics are far from the true numbers for the labeled datasets. For the hPAM model, the Renyi entropy approach allows determining only one level of the data structure. For hARTM model, the proposed approach allows us to estimate the number of topics for two levels of hierarchy.
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Affiliation(s)
- Sergei Koltcov
- Laboratory for Social and Cognitive Informatics, National Research University Higher School of Economics, St. Petersburg, Russia
| | - Vera Ignatenko
- Laboratory for Social and Cognitive Informatics, National Research University Higher School of Economics, St. Petersburg, Russia
| | - Maxim Terpilovskii
- Laboratory for Social and Cognitive Informatics, National Research University Higher School of Economics, St. Petersburg, Russia
| | - Paolo Rosso
- Laboratory for Social and Cognitive Informatics, National Research University Higher School of Economics, St. Petersburg, Russia
- Pattern Recognition and Human Language Technology Research Center, Universitat Politècnica de València, Valencia, Spain
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From many to (n)one: Meditation and the plasticity of the predictive mind. Neurosci Biobehav Rev 2021; 128:199-217. [PMID: 34139248 DOI: 10.1016/j.neubiorev.2021.06.021] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/07/2021] [Accepted: 06/09/2021] [Indexed: 12/21/2022]
Abstract
How profoundly can humans change their own minds? In this paper we offer a unifying account of deconstructive meditation under the predictive processing view. We start from simple axioms. First, the brain makes predictions based on past experience, both phylogenetic and ontogenetic. Second, deconstructive meditation brings one closer to the here and now by disengaging anticipatory processes. We propose that practicing meditation therefore gradually reduces counterfactual temporally deep cognition, until all conceptual processing falls away, unveiling a state of pure awareness. Our account also places three main styles of meditation (focused attention, open monitoring, and non-dual) on a single continuum, where each technique relinquishes increasingly engrained habits of prediction, including the predicted self. This deconstruction can also permit certain insights by making the above processes available to introspection. Our framework is consistent with the state of empirical and (neuro)phenomenological evidence and illuminates the top-down plasticity of the predictive mind. Experimental rigor, neurophenomenology, and no-report paradigms are needed to further understanding of how meditation affects predictive processing and the self.
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Kocoń J, Maziarz M. Mapping WordNet onto human brain connectome in emotion processing and semantic similarity recognition. Inf Process Manag 2021. [DOI: 10.1016/j.ipm.2021.102530] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Rasero J, Sentis AI, Yeh FC, Verstynen T. Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability. PLoS Comput Biol 2021; 17:e1008347. [PMID: 33667224 PMCID: PMC7984650 DOI: 10.1371/journal.pcbi.1008347] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 03/22/2021] [Accepted: 02/10/2021] [Indexed: 01/08/2023] Open
Abstract
Variation in cognitive ability arises from subtle differences in underlying neural architecture. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N = 1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to more than 3% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.
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Affiliation(s)
- Javier Rasero
- Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Amy Isabella Sentis
- Carnegie Mellon Neuroscience Institute, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Program in Neural Computation, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Fang-Cheng Yeh
- Program in Neural Computation, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, United States of America
| | - Timothy Verstynen
- Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, United States of America
- Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
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Shadi K, Dyer E, Dovrolis C. Multisensory integration in the mouse cortical connectome using a network diffusion model. Netw Neurosci 2020; 4:1030-1054. [PMID: 33195947 PMCID: PMC7655044 DOI: 10.1162/netn_a_00164] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 08/03/2020] [Indexed: 01/05/2023] Open
Abstract
Having a structural network representation of connectivity in the brain is instrumental in analyzing communication dynamics and neural information processing. In this work, we make steps towards understanding multisensory information flow and integration using a network diffusion approach. In particular, we model the flow of evoked activity, initiated by stimuli at primary sensory regions, using the asynchronous linear threshold (ALT) diffusion model. The ALT model captures how evoked activity that originates at a given region of the cortex “ripples through” other brain regions (referred to as an activation cascade). We find that a small number of brain regions–the claustrum and the parietal temporal cortex being at the top of the list–are involved in almost all cortical sensory streams. This suggests that the cortex relies on an hourglass architecture to first integrate and compress multisensory information from multiple sensory regions, before utilizing that lower dimensionality representation in higher level association regions and more complex cognitive tasks. Having a structural network representation of connectivity in the brain is instrumental in analyzing communication dynamics and neural information processing. In this work, we make steps towards understanding multisensory information flow and integration using a network diffusion approach. In particular, we model the flow of evoked activity, initiated by stimuli at primary sensory regions, using the asynchronous linear threshold (ALT) diffusion model. The ALT model captures how evoked activity that originates at a given region of the cortex “ripples through” other brain regions (referred to as an activation cascade). We apply the ALT model to the mouse connectome provided by the Allen Institute for Brain Science. A first result, using functional datasets based on voltage-sensitive dye (VSD) imaging, is that the ALT model, despite its simplicity, predicts the temporal ordering of each sensory activation cascade quite accurately. We further apply this model to study multisensory integration and find that a small number of brain regionsthe claustrum and the parietal temporal cortex being at the top of the listare involved in almost all cortical sensory streams. This suggests that the cortex relies on an hourglass architecture to first integrate and compress multisensory information from multiple sensory regions, before utilizing that lower dimensionality representation in higher level association regions and more complex cognitive tasks.
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Affiliation(s)
- Kamal Shadi
- School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
| | - Eva Dyer
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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Dohmatob E, Dumas G, Bzdok D. Dark control: The default mode network as a reinforcement learning agent. Hum Brain Mapp 2020; 41:3318-3341. [PMID: 32500968 PMCID: PMC7375062 DOI: 10.1002/hbm.25019] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 03/22/2020] [Accepted: 04/12/2020] [Indexed: 12/11/2022] Open
Abstract
The default mode network (DMN) is believed to subserve the baseline mental activity in humans. Its higher energy consumption compared to other brain networks and its intimate coupling with conscious awareness are both pointing to an unknown overarching function. Many research streams speak in favor of an evolutionarily adaptive role in envisioning experience to anticipate the future. In the present work, we propose a process model that tries to explain how the DMN may implement continuous evaluation and prediction of the environment to guide behavior. The main purpose of DMN activity, we argue, may be described by Markov decision processes that optimize action policies via value estimates through vicarious trial and error. Our formal perspective on DMN function naturally accommodates as special cases previous interpretations based on (a) predictive coding, (b) semantic associations, and (c) a sentinel role. Moreover, this process model for the neural optimization of complex behavior in the DMN offers parsimonious explanations for recent experimental findings in animals and humans.
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Affiliation(s)
- Elvis Dohmatob
- Criteo AI LabParisFrance
- INRIA, Parietal TeamSaclayFrance
- Neurospin, CEAGif‐sur‐YvetteFrance
| | - Guillaume Dumas
- Institut Pasteur, Human Genetics and Cognitive Functions UnitParisFrance
- CNRS UMR 3571 Genes, Synapses and Cognition, Institut PasteurParisFrance
- University Paris Diderot, Sorbonne Paris CitéParisFrance
- Centre de Bioinformatique, Biostatistique et Biologie IntégrativeParisFrance
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, School of Computer ScienceMcGill UniversityMontrealCanada
- Mila—Quebec Artificial Intelligence InstituteMontrealCanada
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Spanoudis G, Demetriou A. Mapping Mind-Brain Development: Towards a Comprehensive Theory. J Intell 2020; 8:E19. [PMID: 32357452 PMCID: PMC7713015 DOI: 10.3390/jintelligence8020019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 04/13/2020] [Accepted: 04/20/2020] [Indexed: 12/12/2022] Open
Abstract
The relations between the developing mind and developing brain are explored. We outline a theory of intellectual development postulating that the mind comprises four systems of processes (domain-specific, attention and working memory, reasoning, and cognizance) developing in four cycles (episodic, realistic, rule-based, and principle-based representations, emerging at birth, 2, 6, and 11 years, respectively), with two phases in each. Changes in reasoning relate to processing efficiency in the first phase and working memory in the second phase. Awareness of mental processes is recycled with the changes in each cycle and drives their integration into the representational unit of the next cycle. Brain research shows that each type of processes is served by specialized brain networks. Domain-specific processes are rooted in sensory cortices; working memory processes are mainly rooted in hippocampal, parietal, and prefrontal cortices; abstraction and alignment processes are rooted in parietal, frontal, and prefrontal and medial cortices. Information entering these networks is available to awareness processes. Brain networks change along the four cycles, in precision, connectivity, and brain rhythms. Principles of mind-brain interaction are discussed.
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Affiliation(s)
- George Spanoudis
- Psychology Department, University of Cyprus, 1678 Nicosia, Cyprus
| | - Andreas Demetriou
- Department of Psychology, University of Nicosia, 1700 Nicosia, Cyprus;
- Cyprus Academy of Science, Letters, and Arts, 1700 Nicosia, Cyprus
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37
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Cortical gyrification in relation to age and cognition in older adults. Neuroimage 2020; 212:116637. [PMID: 32081782 DOI: 10.1016/j.neuroimage.2020.116637] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 01/31/2020] [Accepted: 02/12/2020] [Indexed: 12/21/2022] Open
Abstract
Gyrification of the cerebral cortex changes with aging and relates to development of cognitive function during early life and midlife. Little is known about how gyrification relates to age and cognitive function later in life. We investigated this in 4397 individuals (mean age: 63.5 years, range: 45.7 to 97.9) from the Rotterdam Study, a population-based cohort. Global and local gyrification were assessed from T1-weighted images. A measure for global cognition, the g-factor, was calculated from five cognitive tests. Older age was associated with lower gyrification (mean difference per year = -0.0021; 95% confidence interval = -0.0025; -0.0017). Non-linear terms did not improve the models. Age related to lower gyrification in the parietal, frontal, temporal and occipital regions, and higher gyrification in the medial prefrontal cortex. Higher levels of the g-factor were associated with higher global gyrification (mean difference per g-factor unit = 0.0044; 95% confidence interval = 0.0015; 0.0073). Age and the g-factor did not interact in relation to gyrification (p > 0.05). The g-factor bilaterally associated with gyrification in three distinct clusters. The first cluster encompassed the superior temporal gyrus, the insular cortex and the postcentral gyrus, the second cluster the lingual gyrus and the precuneus, and the third cluster the orbitofrontal cortex. These clusters largely remained statistically significant after correction for cortical surface area. Overall, the results support the notion that gyrification varies with aging and cognition during and after midlife, and suggest that gyrification is a potential marker for age-related brain and cognitive decline beyond midlife.
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38
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Larivière S, Vos de Wael R, Hong SJ, Paquola C, Tavakol S, Lowe AJ, Schrader DV, Bernhardt BC. Multiscale Structure-Function Gradients in the Neonatal Connectome. Cereb Cortex 2020; 30:47-58. [PMID: 31220215 PMCID: PMC7029695 DOI: 10.1093/cercor/bhz069] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Revised: 03/07/2019] [Accepted: 03/08/2019] [Indexed: 11/13/2022] Open
Abstract
The adult functional connectome is well characterized by a macroscale spatial gradient of connectivity traversing from unimodal toward higher-order transmodal cortices that recapitulates known principles of hierarchical organization and myelination patterns. Despite an emerging literature assessing connectome properties in neonates, the presence of connectome gradients and particularly their correspondence to microstructure remains largely unknown. We derived connectome gradients using unsupervised techniques applied to functional connectivity data from 40 term-born neonates. A series of cortex-wide analysis examined associations to magnetic resonance imaging-derived morphological parameters (cortical thickness, sulcal depth, curvature), measures of tissue microstructure (intracortical T1w/T2w intensity, superficial white matter diffusion parameters), and subcortico-cortical functional connectivity. Our findings indicate that the primary neonatal connectome gradient runs between sensorimotor and visual anchors and captures specific associations to cortical and superficial white matter microstructure as well as thalamo-cortical connectivity. A second gradient indicated an anterior-to-posterior asymmetry in macroscale connectivity alongside an immature differentiation between unimodal and transmodal areas, indicating a connectome-level circuitry en route to an adult-like organization. Our findings reveal an important coordination of structural and functional interactions in the neonatal connectome across spatial scales. Observed associations were replicable across individual neonates, suggesting consistency and generalizability.
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Affiliation(s)
- Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Seok-Jun Hong
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Center of the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Alexander J Lowe
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Dewi V Schrader
- BC Children’s Hospital, Division of Neurology, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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39
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Prefrontal neural dynamics in consciousness. Neuropsychologia 2019; 131:25-41. [DOI: 10.1016/j.neuropsychologia.2019.05.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 05/17/2019] [Accepted: 05/20/2019] [Indexed: 12/11/2022]
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40
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Hong SJ, Vos de Wael R, Bethlehem RAI, Lariviere S, Paquola C, Valk SL, Milham MP, Di Martino A, Margulies DS, Smallwood J, Bernhardt BC. Atypical functional connectome hierarchy in autism. Nat Commun 2019; 10:1022. [PMID: 30833582 PMCID: PMC6399265 DOI: 10.1038/s41467-019-08944-1] [Citation(s) in RCA: 286] [Impact Index Per Article: 47.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 02/06/2019] [Indexed: 12/11/2022] Open
Abstract
One paradox of autism is the co-occurrence of deficits in sensory and higher-order socio-cognitive processing. Here, we examined whether these phenotypical patterns may relate to an overarching system-level imbalance-specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. Combining connectome gradient and stepwise connectivity analysis based on task-free functional magnetic resonance imaging (fMRI), we demonstrated atypical connectivity transitions between sensory and higher-order default mode regions in a large cohort of individuals with autism relative to typically-developing controls. Further analyses indicated that reduced differentiation related to perturbed stepwise connectivity from sensory towards transmodal areas, as well as atypical long-range rich-club connectivity. Supervised pattern learning revealed that hierarchical features predicted deficits in social cognition and low-level behavioral symptoms, but not communication-related symptoms. Our findings provide new evidence for imbalances in network hierarchy in autism, which offers a parsimonious reference frame to consolidate its diverse features.
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Affiliation(s)
- Seok-Jun Hong
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada.
- Center for the Developing Brain, Child Mind Institute, 10022, New York, NY, USA.
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada
| | - Richard A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, CB28AH, Cambridge, UK
| | - Sara Lariviere
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada
| | - Sofie L Valk
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425, Jülich, Germany
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, 10022, New York, NY, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 10962, Orangeburg, NY, USA
| | | | - Daniel S Margulies
- Frontlab, Institut du Cerveau et de la Moelle épinière, UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
| | | | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada.
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41
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Kan KJ, van der Maas HL, Levine SZ. Extending psychometric network analysis: Empirical evidence against g in favor of mutualism? INTELLIGENCE 2019. [DOI: 10.1016/j.intell.2018.12.004] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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42
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Murphy C, Wang HT, Konu D, Lowndes R, Margulies DS, Jefferies E, Smallwood J. Modes of operation: A topographic neural gradient supporting stimulus dependent and independent cognition. Neuroimage 2019; 186:487-496. [DOI: 10.1016/j.neuroimage.2018.11.009] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 10/30/2018] [Accepted: 11/07/2018] [Indexed: 12/25/2022] Open
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43
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Pereira WO, Lima FT. Challenge, Discussion and Conclusion: an active teaching strategy to turn traditional lectures into collaborative classes. EINSTEIN-SAO PAULO 2018; 16:eED4362. [PMID: 29898092 PMCID: PMC5995554 DOI: 10.1590/s1679-45082018ed4362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 02/06/2018] [Indexed: 11/22/2022] Open
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44
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Demetriou A, Makris N, Spanoudis G, Kazi S, Shayer M, Kazali E. Mapping the Dimensions of General Intelligence: An Integrated Differential-Developmental Theory. Hum Dev 2018. [DOI: 10.1159/000484450] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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45
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Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proc Natl Acad Sci U S A 2018. [PMID: 29382744 DOI: 10.1073/pnas.1715766115.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The frontoparietal control network (FPCN) plays a central role in executive control. It has been predominantly viewed as a unitary domain general system. Here, we examined patterns of FPCN functional connectivity (FC) across multiple conditions of varying cognitive demands, to test for FPCN heterogeneity. We identified two distinct subsystems within the FPCN based on hierarchical clustering and machine learning classification analyses of within-FPCN FC patterns. These two FPCN subsystems exhibited distinct patterns of FC with the default network (DN) and the dorsal attention network (DAN). FPCNA exhibited stronger connectivity with the DN than the DAN, whereas FPCNB exhibited the opposite pattern. This twofold FPCN differentiation was observed across four independent datasets, across nine different conditions (rest and eight tasks), at the level of individual-participant data, as well as in meta-analytic coactivation patterns. Notably, the extent of FPCN differentiation varied across conditions, suggesting flexible adaptation to task demands. Finally, we used meta-analytic tools to identify several functional domains associated with the DN and DAN that differentially predict activation in the FPCN subsystems. These findings reveal a flexible and heterogeneous FPCN organization that may in part emerge from separable DN and DAN processing streams. We propose that FPCNA may be preferentially involved in the regulation of introspective processes, whereas FPCNB may be preferentially involved in the regulation of visuospatial perceptual attention.
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46
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Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proc Natl Acad Sci U S A 2018; 115:E1598-E1607. [PMID: 29382744 DOI: 10.1073/pnas.1715766115] [Citation(s) in RCA: 342] [Impact Index Per Article: 48.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The frontoparietal control network (FPCN) plays a central role in executive control. It has been predominantly viewed as a unitary domain general system. Here, we examined patterns of FPCN functional connectivity (FC) across multiple conditions of varying cognitive demands, to test for FPCN heterogeneity. We identified two distinct subsystems within the FPCN based on hierarchical clustering and machine learning classification analyses of within-FPCN FC patterns. These two FPCN subsystems exhibited distinct patterns of FC with the default network (DN) and the dorsal attention network (DAN). FPCNA exhibited stronger connectivity with the DN than the DAN, whereas FPCNB exhibited the opposite pattern. This twofold FPCN differentiation was observed across four independent datasets, across nine different conditions (rest and eight tasks), at the level of individual-participant data, as well as in meta-analytic coactivation patterns. Notably, the extent of FPCN differentiation varied across conditions, suggesting flexible adaptation to task demands. Finally, we used meta-analytic tools to identify several functional domains associated with the DN and DAN that differentially predict activation in the FPCN subsystems. These findings reveal a flexible and heterogeneous FPCN organization that may in part emerge from separable DN and DAN processing streams. We propose that FPCNA may be preferentially involved in the regulation of introspective processes, whereas FPCNB may be preferentially involved in the regulation of visuospatial perceptual attention.
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47
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Safron A, Klimaj V, Sylva D, Rosenthal AM, Li M, Walter M, Bailey JM. Neural Correlates of Sexual Orientation in Heterosexual, Bisexual, and Homosexual Women. Sci Rep 2018; 8:673. [PMID: 29330483 PMCID: PMC5766543 DOI: 10.1038/s41598-017-18372-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 12/11/2017] [Indexed: 11/24/2022] Open
Abstract
We used fMRI to investigate neural correlates of responses to erotic pictures and videos in heterosexual (N = 26), bisexual (N = 26), and homosexual (N = 24) women, ages 25–50. We focused on the ventral striatum, an area of the brain associated with desire, extending previous findings from the sexual psychophysiology literature in which homosexual women had greater category specificity (relative to heterosexual and bisexual women) in their responses to male and female erotic stimuli. We found that homosexual women’s subjective and neural responses reflected greater bias towards female stimuli, compared with bisexual and heterosexual women, whose responses did not significantly differ. These patterns were also suggested by whole brain analyses, with homosexual women showing category-specific activations of greater extents in visual and auditory processing areas. Bisexual women tended to show more mixed patterns, with activations more responsive to female stimuli in sensory processing areas, and activations more responsive to male stimuli in areas associated with social cognition.
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Affiliation(s)
- Adam Safron
- Department of Psychology, Northwestern University, Evanston, Illinois, USA.
| | - Victoria Klimaj
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
| | - David Sylva
- Department of Psychiatry, Kaiser Permanente, Oakland, California, USA
| | - A M Rosenthal
- Department of Psychiatry, Kaiser Permanente, Oakland, California, USA
| | - Meng Li
- Department of Psychiatry, Otto von Guericke University, Magdeburg, Germany.,Leibniz Institute for Neurobiology, Magdeburg, Germany.,Department of Psychiatry, Eberhard Karls University, Tubingen, Germany
| | - Martin Walter
- Department of Psychiatry, Otto von Guericke University, Magdeburg, Germany.,Leibniz Institute for Neurobiology, Magdeburg, Germany.,Department of Psychiatry, Eberhard Karls University, Tubingen, Germany
| | - J Michael Bailey
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
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Huntenburg JM, Bazin PL, Margulies DS. Large-Scale Gradients in Human Cortical Organization. Trends Cogn Sci 2017; 22:21-31. [PMID: 29203085 DOI: 10.1016/j.tics.2017.11.002] [Citation(s) in RCA: 539] [Impact Index Per Article: 67.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 10/31/2017] [Accepted: 11/01/2017] [Indexed: 01/19/2023]
Abstract
Recent advances in mapping cortical areas in the human brain provide a basis for investigating the significance of their spatial arrangement. Here we describe a dominant gradient in cortical features that spans between sensorimotor and transmodal areas. We propose that this gradient constitutes a core organizing axis of the human cerebral cortex, and describe an intrinsic coordinate system on its basis. Studying the cortex with respect to these intrinsic dimensions can inform our understanding of how the spectrum of cortical function emerges from structural constraints.
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Affiliation(s)
- Julia M Huntenburg
- Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, 04103 Leipzig, Germany; Neurocomputation and Neuroimaging Unit, Department of Education and Psychology, Free University of Berlin, 14195 Berlin, Germany.
| | - Pierre-Louis Bazin
- Social Brain Lab, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA Amsterdam, Netherlands; Spinoza Centre for Neuroimaging, Meibergdreef 75, 1105 BK Amsterdam, Netherlands; Departments of Neurology and Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, 04103 Leipzig, Germany
| | - Daniel S Margulies
- Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, 04103 Leipzig, Germany.
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Bhat AA, Mohan V, Sandini G, Morasso P. Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative learning experiences. J R Soc Interface 2017; 13:rsif.2016.0310. [PMID: 27466440 PMCID: PMC4971221 DOI: 10.1098/rsif.2016.0310] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 06/28/2016] [Indexed: 11/12/2022] Open
Abstract
Emerging studies indicate that several species such as corvids, apes and children solve 'The Crow and the Pitcher' task (from Aesop's Fables) in diverse conditions. Hidden beneath this fascinating paradigm is a fundamental question: by cumulatively interacting with different objects, how can an agent abstract the underlying cause-effect relations to predict and creatively exploit potential affordances of novel objects in the context of sought goals? Re-enacting this Aesop's Fable task on a humanoid within an open-ended 'learning-prediction-abstraction' loop, we address this problem and (i) present a brain-guided neural framework that emulates rapid one-shot encoding of ongoing experiences into a long-term memory and (ii) propose four task-agnostic learning rules (elimination, growth, uncertainty and status quo) that correlate predictions from remembered past experiences with the unfolding present situation to gradually abstract the underlying causal relations. Driven by the proposed architecture, the ensuing robot behaviours illustrated causal learning and anticipation similar to natural agents. Results further demonstrate that by cumulatively interacting with few objects, the predictions of the robot in case of novel objects converge close to the physical law, i.e. the Archimedes principle: this being independent of both the objects explored during learning and the order of their cumulative exploration.
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Affiliation(s)
- Ajaz Ahmad Bhat
- Robotics, Brain and Cognitive Science Department, Istituto Italiano di Tecnologia, Via Morego 30, Genova, Italy
| | - Vishwanathan Mohan
- Robotics, Brain and Cognitive Science Department, Istituto Italiano di Tecnologia, Via Morego 30, Genova, Italy
| | - Giulio Sandini
- Robotics, Brain and Cognitive Science Department, Istituto Italiano di Tecnologia, Via Morego 30, Genova, Italy
| | - Pietro Morasso
- Robotics, Brain and Cognitive Science Department, Istituto Italiano di Tecnologia, Via Morego 30, Genova, Italy
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50
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Ruffini G. An algorithmic information theory of consciousness. Neurosci Conscious 2017; 2017:nix019. [PMID: 30042851 PMCID: PMC6007168 DOI: 10.1093/nc/nix019] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 07/24/2017] [Accepted: 07/27/2017] [Indexed: 11/13/2022] Open
Abstract
Providing objective metrics of conscious state is of great interest across multiple research and clinical fields-from neurology to artificial intelligence. Here we approach this challenge by proposing plausible mechanisms for the phenomenon of structured experience. In earlier work, we argued that the experience we call reality is a mental construct derived from information compression. Here we show that algorithmic information theory provides a natural framework to study and quantify consciousness from neurophysiological or neuroimaging data, given the premise that the primary role of the brain is information processing. We take as an axiom that "there is consciousness" and focus on the requirements for structured experience: we hypothesize that the existence and use of compressive models by cognitive systems, e.g. in biological recurrent neural networks, enables and provides the structure to phenomenal experience. Self-awareness is seen to arise naturally (as part of a better model) in cognitive systems interacting bidirectionally with the external world. Furthermore, we argue that by running such models to track data, brains can give rise to apparently complex (entropic but hierarchically organized) data. We compare this theory, named KT for its basis on the mathematical theory of Kolmogorov complexity, to other information-centric theories of consciousness. We then describe methods to study the complexity of the brain's output streams or of brain state as correlates of conscious state: we review methods such as (i) probing the brain through its input streams (e.g. event-related potentials in oddball paradigms or mutual algorithmic information between world and brain), (ii) analyzing spontaneous brain state, (iii) perturbing the brain by non-invasive transcranial stimulation, and (iv) quantifying behavior (e.g. eye movements or body sway).
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Affiliation(s)
- Giulio Ruffini
- Starlab Barcelona, Avda. Tibidabo 47bis, 08035 Barcelona, Spain and Neuroelectrics Corporation, 210 Broadway, Cambridge, MA 02139, USA
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