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Fiorenzato E, Moaveninejad S, Weis L, Biundo R, Antonini A, Porcaro C. Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease. Mov Disord 2024; 39:305-317. [PMID: 38054573 DOI: 10.1002/mds.29678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/13/2023] [Accepted: 11/17/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND Higuchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting-state functional magnetic resonance imaging (fMRI) data and detect the neuronal interaction complexity underlying Parkinson's disease (PD) cognitive decline. OBJECTIVES The aim was to compare FD with a more established index of spontaneous neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), and identify through machine learning (ML) models which method could best distinguish across PD-cognitive states, ranging from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD). Finally, the aim was to explore correlations between fALFF and FD with clinical and cognitive PD features. METHODS Among 118 PD patients age-, sex-, and education matched with 35 healthy controls, 52 were classified with PD-NC, 46 with PD-MCI, and 20 with PDD based on an extensive cognitive and clinical evaluation. fALFF and FD metrics were computed on rs-fMRI data and used to train ML models. RESULTS FD outperformed fALFF metrics in differentiating between PD-cognitive states, reaching an overall accuracy of 78% (vs. 62%). PD showed increased neuronal dynamics complexity within the sensorimotor network, central executive network (CEN), and default mode network (DMN), paralleled by a reduction in spontaneous neuronal activity within the CEN and DMN, whose increased complexity was strongly linked to the presence of dementia. Further, we found that some DMN critical hubs correlated with worse cognitive performance and disease severity. CONCLUSIONS Our study indicates that PD-cognitive decline is characterized by an altered spontaneous neuronal activity and increased temporal complexity, involving the CEN and DMN, possibly reflecting an increased segregation of these networks. Therefore, we propose FD as a prognostic biomarker of PD-cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Eleonora Fiorenzato
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
| | - Sadaf Moaveninejad
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Luca Weis
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- IRCCS, San Camillo Hospital, Venice, Italy
| | - Roberta Biundo
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- Department of Neuroscience, Center for Neurodegenerative Disease Research (CESNE), University of Padova, Padova, Italy
- Department of General Psychology, University of Padua, Padua, Italy
| | - Angelo Antonini
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Department of Neuroscience, Center for Neurodegenerative Disease Research (CESNE), University of Padova, Padova, Italy
| | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Institute of Cognitive Sciences and Technologies-National Research Council, Rome, Italy
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom
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Davies C, Martins D, Dipasquale O, McCutcheon RA, De Micheli A, Ramella-Cravaro V, Provenzani U, Rutigliano G, Cappucciati M, Oliver D, Williams S, Zelaya F, Allen P, Murguia S, Taylor D, Shergill S, Morrison P, McGuire P, Paloyelis Y, Fusar-Poli P. Connectome dysfunction in patients at clinical high risk for psychosis and modulation by oxytocin. Mol Psychiatry 2024:10.1038/s41380-024-02406-x. [PMID: 38243074 DOI: 10.1038/s41380-024-02406-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 01/21/2024]
Abstract
Abnormalities in functional brain networks (functional connectome) are increasingly implicated in people at Clinical High Risk for Psychosis (CHR-P). Intranasal oxytocin, a potential novel treatment for the CHR-P state, modulates network topology in healthy individuals. However, its connectomic effects in people at CHR-P remain unknown. Forty-seven men (30 CHR-P and 17 healthy controls) received acute challenges of both intranasal oxytocin 40 IU and placebo in two parallel randomised, double-blind, placebo-controlled cross-over studies which had similar but not identical designs. Multi-echo resting-state fMRI data was acquired at approximately 1 h post-dosing. Using a graph theoretical approach, the effects of group (CHR-P vs healthy control), treatment (oxytocin vs placebo) and respective interactions were tested on graph metrics describing the topology of the functional connectome. Group effects were observed in 12 regions (all pFDR < 0.05) most localised to the frontoparietal network. Treatment effects were found in 7 regions (all pFDR < 0.05) predominantly within the ventral attention network. Our major finding was that many effects of oxytocin on network topology differ across CHR-P and healthy individuals, with significant interaction effects observed in numerous subcortical regions strongly implicated in psychosis onset, such as the thalamus, pallidum and nucleus accumbens, and cortical regions which localised primarily to the default mode network (12 regions, all pFDR < 0.05). Collectively, our findings provide new insights on aberrant functional brain network organisation associated with psychosis risk and demonstrate, for the first time, that oxytocin modulates network topology in brain regions implicated in the pathophysiology of psychosis in a clinical status (CHR-P vs healthy control) specific manner.
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Affiliation(s)
- Cathy Davies
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
| | - Daniel Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre (BRC), South London and Maudsley NHS Foundation Trust, London, UK
- Department of Psychiatry, University Hospitals of Genève, Geneva, Switzerland
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Robert A McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Andrea De Micheli
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Outreach And Support in South London (OASIS) Service, South London and Maudsley NHS Foundation Trust, London, UK
| | - Valentina Ramella-Cravaro
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Umberto Provenzani
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Grazia Rutigliano
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Marco Cappucciati
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Dominic Oliver
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Steve Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Fernando Zelaya
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Paul Allen
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Silvia Murguia
- Tower Hamlets Early Detection Service, East London NHS Foundation Trust, London, UK
| | - David Taylor
- Institute of Pharmaceutical Science, King's College London, London, UK
| | - Sukhi Shergill
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Kent and Medway Medical School, Canterbury, UK
| | - Paul Morrison
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Philip McGuire
- Department of Psychiatry, University of Oxford, Oxford, UK
- NIHR Oxford Health Biomedical Research Centre, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
| | - Yannis Paloyelis
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions & Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre (BRC), South London and Maudsley NHS Foundation Trust, London, UK
- Outreach And Support in South London (OASIS) Service, South London and Maudsley NHS Foundation Trust, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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Hirsch F, Wohlschlaeger A. Subcortical influences on the topology of cortical networks align with functional processing hierarchies. Neuroimage 2023; 283:120417. [PMID: 37866758 DOI: 10.1016/j.neuroimage.2023.120417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 10/18/2023] [Accepted: 10/19/2023] [Indexed: 10/24/2023] Open
Abstract
fMRI of the human brain reveals spatiotemporal patterns of functional connectivity (FC), forming distinct cortical networks. Lately, subcortical contributions to these configurations are receiving renewed interest, but investigations rarely focus explicitly on their effects on cortico-cortical FC. Here, we employ a straightforward multivariable approach and graph-theoretic tools to assess subcortical impact on topological features of cortical networks. Given recent evidence showing that structures like the thalamus and basal ganglia integrate input from multiple networks, we expect increased segregation between cortical networks after removal of subcortical effects on their FC patterns. We analyze resting state data of young and healthy participants (male and female; N = 100) from the human connectome project. We find that overall, the cortical network architecture becomes less segregated, and more integrated, when subcortical influences are accounted for. Underlying these global effects are the following trends: 'Transmodal' systems become more integrated with the rest of the network, while 'unimodal' networks show the opposite effect. For single nodes this hierarchical organization is reflected by a close correspondence with the spatial layout of the principal gradient of FC (Margulies et al., 2016). Lastly, we show that the limbic system is significantly less coherent with subcortical influences removed. The findings are validated in a (split-sample) replication dataset. Our results provide new insight regarding the interplay between subcortex and cortical networks, by putting the integrative impact of subcortex in the context of macroscale patterns of cortical organization.
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Affiliation(s)
- Fabian Hirsch
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany.
| | - Afra Wohlschlaeger
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany
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Xiao M, Luo Y, Ding C, Chen X, Liu Y, Tang Y, Chen H. Social support and overeating in young women: The role of altering functional network connectivity patterns and negative emotions. Appetite 2023; 191:107069. [PMID: 37837769 DOI: 10.1016/j.appet.2023.107069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 09/20/2023] [Accepted: 09/29/2023] [Indexed: 10/16/2023]
Abstract
Research suggests that social support has a protective effect on emotional health and emotionally induced overeating. Women are especially more sensitive to benefits from social support when facing eating problems. Although it has been demonstrated that social support can affect the neural processes of emotion regulation and reward perception, it is unclear how social support alters synergistic patterns in large-scale brain networks associated with negative emotions and overeating. We used a large sample of young women aged 17-22 years (N = 360) to examine how social support influences the synchrony of five intrinsic networks (executive control network [ECN], default mode network, salience network [SN], basal ganglia network, and precuneus network [PN]) and how these networks influence negative affect and overeating. Additionally, we explored these analyses in another sample of males (N = 136). After statistically controlling for differences in age and head movement, we observed significant associations of higher levels of social support with increased intra- and inter-network functional synchrony, particularly for ECN-centered network connectivity. Subsequent chain-mediated analyses showed that social support predicted overeating through the ECN-SN and ECN-PN network connectivity and negative emotions. However, these results were not found in men. These findings suggest that social support influences the synergistic patterns within and between intrinsic networks related to inhibitory control, emotion salience, self-referential thinking, and reward sensitivity. Furthermore, they reveal that social support and its neural markers may play a key role in young women's emotional health and eating behavior.
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Affiliation(s)
- Mingyue Xiao
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Yijun Luo
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Cody Ding
- Department of Educational Psychology, Research, and Evaluation, University of Missouri, St. Louis, USA
| | - Ximei Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Yutian Tang
- Faculty of Arts, University of British Columbia, Canada
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China; Research Center of Psychology and Social Development, Southwest University, Chongqing, China.
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5
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Liu Q, Zhou B, Zhang X, Qing P, Zhou X, Zhou F, Xu X, Zhu S, Dai J, Huang Y, Wang J, Zou Z, Kendrick KM, Becker B, Zhao W. Abnormal multi-layered dynamic cortico-subcortical functional connectivity in major depressive disorder and generalized anxiety disorder. J Psychiatr Res 2023; 167:23-31. [PMID: 37820447 DOI: 10.1016/j.jpsychires.2023.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 08/16/2023] [Accepted: 10/05/2023] [Indexed: 10/13/2023]
Abstract
Comorbidity has been frequently observed between generalized anxiety disorder (GAD) and major depressive disorder (MDD), however, common and distinguishable alterations in the topological organization of functional brain networks remain poorly understood. We sought to determine a robust and sensitive functional connectivity marker for diagnostic classification and symptom severity prediction. Multi-layered dynamic functional connectivity including whole brain, network-node and node-node layers via graph theory and gradient analyses were applied to functional MRI resting-state data obtained from 31 unmedicated GAD and 34 unmedicated MDD patients as well as 33 age and education matched healthy controls (HC). GAD and MDD symptoms were assessed using Penn State Worry Questionnaire and Beck Depression Inventory II, respectively. Three network measures including global properties (i.e., global efficiency, characteristic path length), regional nodal property (i.e., degree) and connectivity gradients were computed. Results showed that both patient groups exhibited abnormal dynamic cortico-subcortical topological organization compared to healthy controls, with MDD > GAD > HC in degree of randomization. Furthermore, our multi-layered dynamic functional connectivity network model reached 77% diagnostic accuracy between GAD and MDD and was highly predictive of symptom severity, respectively. Gradients of functional connectivity for superior frontal cortex-subcortical regions, middle temporal gyrus-subcortical regions and amygdala-cortical regions contributed more in this model compared to other gradients. We found shared and distinct cortico-subcortical connectivity features in dynamic functional brain networks between GAD and MDD, which together can promote the understanding of common and disorder-specific topological organization dysregulations and facilitate early neuroimaging-based diagnosis.
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Affiliation(s)
- Qi Liu
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Bo Zhou
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Xiaodong Zhang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Peng Qing
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Xinqi Zhou
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, 610066, China
| | - Feng Zhou
- Faculty of Psychology, Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, 400715, China
| | - Xiaolei Xu
- School of Psychology, Shandong Normal University, Jinan, 250014, China
| | - Siyu Zhu
- School of Sport Training, Chengdu Sport University, Chengdu, 610041, China
| | - Jing Dai
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yulan Huang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Jinyu Wang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Zhili Zou
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China; State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, Pokfulam, Hong Kong; Department of Psychology, The University of Hong Kong, Hong Kong, Pokfulam, Hong Kong; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Weihua Zhao
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
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Liu ZQ, Shafiei G, Baillet S, Misic B. Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks. Neuroimage 2023; 278:120276. [PMID: 37451374 DOI: 10.1016/j.neuroimage.2023.120276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
The relationship between structural and functional connectivity in the brain is a key question in connectomics. Here we quantify patterns of structure-function coupling across the neocortex, by comparing structural connectivity estimated using diffusion MRI with functional connectivity estimated using both neurophysiological (MEG-based) and haemodynamic (fMRI-based) recordings. We find that structure-function coupling is heterogeneous across brain regions and frequency bands. The link between structural and functional connectivity is generally stronger in multiple MEG frequency bands compared to resting state fMRI. Structure-function coupling is greater in slower and intermediate frequency bands compared to faster frequency bands. We also find that structure-function coupling systematically follows the archetypal sensorimotor-association hierarchy, as well as patterns of laminar differentiation, peaking in granular layer IV. Finally, structure-function coupling is better explained using structure-informed inter-regional communication metrics than using structural connectivity alone. Collectively, these results place neurophysiological and haemodynamic structure-function relationships in a common frame of reference and provide a starting point for a multi-modal understanding of structure-function coupling in the brain.
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Affiliation(s)
- Zhen-Qi Liu
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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Chai Y, Sheline YI, Oathes DJ, Balderston NL, Rao H, Yu M. Functional connectomics in depression: insights into therapies. Trends Cogn Sci 2023; 27:814-832. [PMID: 37286432 PMCID: PMC10476530 DOI: 10.1016/j.tics.2023.05.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 06/09/2023]
Abstract
Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.
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Affiliation(s)
- Ya Chai
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Brain Science, Translation, Innovation and Modulation Center (brainSTIM), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hengyi Rao
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA.
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Yu Y, Gratton C, Smith DM. From correlation to communication: Disentangling hidden factors from functional connectivity changes. Netw Neurosci 2023; 7:411-430. [PMID: 37397894 PMCID: PMC10312287 DOI: 10.1162/netn_a_00290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 11/02/2022] [Indexed: 01/11/2024] Open
Abstract
While correlations in the BOLD fMRI signal are widely used to capture functional connectivity (FC) and its changes across contexts, its interpretation is often ambiguous. The entanglement of multiple factors including local coupling of two neighbors and nonlocal inputs from the rest of the network (affecting one or both regions) limits the scope of the conclusions that can be drawn from correlation measures alone. Here we present a method of estimating the contribution of nonlocal network input to FC changes across different contexts. To disentangle the effect of task-induced coupling change from the network input change, we propose a new metric, "communication change," utilizing BOLD signal correlation and variance. With a combination of simulation and empirical analysis, we demonstrate that (1) input from the rest of the network accounts for a moderate but significant amount of task-induced FC change and (2) the proposed "communication change" is a promising candidate for tracking the local coupling in task context-induced change. Additionally, when compared to FC change across three different tasks, communication change can better discriminate specific task types. Taken together, this novel index of local coupling may have many applications in improving our understanding of local and widespread interactions across large-scale functional networks.
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Affiliation(s)
- Yuhua Yu
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Neurology, Northwestern University, Evanston, IL, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Derek M. Smith
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Neurology, Division of Cognitive Neurology/Neuropsychology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Chen X, Dai Z, Lin Y. Biotypes of major depressive disorder identified by a multiview clustering framework. J Affect Disord 2023; 329:257-272. [PMID: 36863463 DOI: 10.1016/j.jad.2023.02.118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 02/11/2023] [Accepted: 02/22/2023] [Indexed: 03/04/2023]
Abstract
BACKGROUND The advances in resting-state functional magnetic resonance imaging techniques motivate parsing heterogeneity in major depressive disorder (MDD) through neurophysiological subtypes (i.e., biotypes). Based on graph theories, researchers have observed the functional organization of the human brain as a complex system with modular structures and have found wide-spread but variable MDD-related abnormality regarding the modules. The evidence implies the possibility of identifying biotypes using high-dimensional functional connectivity (FC) data in ways that suit the potentially multifaceted biotypes taxonomy. METHODS We proposed a multiview biotype discovery framework that involves theory-driven feature subspace partition (i.e., "view") and independent subspace clustering. Six views were defined using intra- and intermodule FC regarding three MDD focal modules (i.e., the sensory-motor system, default mode network, and subcortical network). For robust biotypes, the framework was applied to a large multisite sample (805 MDD participants and 738 healthy controls). RESULTS Two biotypes were stably obtained in each view, respectively characterized by significantly increased and decreased FC compared to healthy controls. These view-specific biotypes promoted the diagnosis of MDD and showed different symptom profiles. By integrating the view-specific biotypes into biotype profiles, a broad spectrum in the neural heterogeneity of MDD and its separation from symptom-based subtypes was further revealed. LIMITATIONS The power of clinical effects is limited and the cross-sectional nature cannot predict the treatment effects of the biotypes. CONCLUSIONS Our findings not only contribute to the understanding of heterogeneity in MDD, but also provide a novel subtyping framework that could transcend current diagnostic boundaries and data modality.
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Affiliation(s)
- Xitian Chen
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.
| | - Ying Lin
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.
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10
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de Lange SC, Helwegen K, van den Heuvel MP. Structural and functional connectivity reconstruction with CATO - A Connectivity Analysis TOolbox. Neuroimage 2023; 273:120108. [PMID: 37059156 DOI: 10.1016/j.neuroimage.2023.120108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/13/2023] [Accepted: 04/11/2023] [Indexed: 04/16/2023] Open
Abstract
We describe a Connectivity Analysis TOolbox (CATO) for the reconstruction of structural and functional brain connectivity based on diffusion weighted imaging and resting-state functional MRI data. CATO is a multimodal software package that enables researchers to run end-to-end reconstructions from MRI data to structural and functional connectome maps, customize their analyses and utilize various software packages to preprocess data. Structural and functional connectome maps can be reconstructed with respect to user-defined (sub)cortical atlases providing aligned connectivity matrices for integrative multimodal analyses. We outline the implementation and usage of the structural and functional processing pipelines in CATO. Performance was calibrated with respect to simulated diffusion weighted imaging from the ITC2015 challenge, test-retest diffusion weighted imaging data and resting-state functional MRI data from the Human Connectome Project. CATO is open-source software distributed under the MIT License and available as a MATLAB toolbox and as a stand-alone application at www.dutchconnectomelab.nl/CATO.
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Affiliation(s)
- Siemon C de Lange
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands.
| | - Koen Helwegen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands; Department of Child Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam
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11
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Feng P, Jiang R, Wei L, Calhoun VD, Jing B, Li H, Sui J. Determining four confounding factors in individual cognitive traits prediction with functional connectivity: an exploratory study. Cereb Cortex 2023; 33:2011-2020. [PMID: 35567795 PMCID: PMC9977351 DOI: 10.1093/cercor/bhac189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/24/2022] [Accepted: 04/25/2022] [Indexed: 11/14/2022] Open
Abstract
Resting-state functional connectivity (RSFC) has been widely adopted for individualized trait prediction. However, multiple confounding factors may impact the predicted brain-behavior relationships. In this study, we investigated the impact of 4 confounding factors including time series length, functional connectivity (FC) type, brain parcellation choice, and variance of the predicted target. The data from Human Connectome Project including 1,206 healthy subjects were employed, with 3 cognitive traits including fluid intelligence, working memory, and picture vocabulary ability as the prediction targets. We compared the prediction performance under different settings of these 4 factors using partial least square regression. Results demonstrated appropriate time series length (300 time points) and brain parcellation (independent component analysis, ICA100/200) can achieve better prediction performance without too much time consumption. FC calculated by Pearson, Spearman, and Partial correlation achieves higher accuracy and lower time cost than mutual information and coherence. Cognitive traits with larger variance among subjects can be better predicted due to the well elaboration of individual variability. In addition, the beneficial effects of increasing scan duration to prediction partially arise from the improved test-retest reliability of RSFC. Taken together, the study highlights the importance of determining these factors in RSFC-based prediction, which can facilitate standardization of RSFC-based prediction pipelines going forward.
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Affiliation(s)
- Pujie Feng
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, 300 Cedar Street, New Haven, 06510 CT, United States
| | - Lijiang Wei
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekou Outer Street, Haidian District, 100875 Beijing, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Pl NE, Atlanta, 30303, GA, United States
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China
| | - Haiyun Li
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekou Outer Street, Haidian District, 100875 Beijing, China.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Pl NE, Atlanta, 30303, GA, United States
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12
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Chen S, Zhang Y, Zhang Z, Zhou TD, Duan W, Weinschenk G, Luh WM, Anderson AK, Dai W. Global Functional Connectivity at Rest Is Associated with Attention: An Arterial Spin Labeling Study. Brain Sci 2023; 13. [PMID: 36831771 DOI: 10.3390/brainsci13020228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 01/25/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
Neural markers of attention, including those frequently linked to the event-related potential P3 (P300) or P3b component, vary widely within and across participants. Understanding the neural mechanisms of attention that contribute to the P3 is crucial for better understanding attention-related brain disorders. All ten participants were scanned twice with a resting-state PCASL perfusion MRI and an ERP with a visual oddball task to measure brain resting-state functional connectivity (rsFC) and P3 parameters (P3 amplitudes and P3 latencies). Global rsFC (average rsFC across the entire brain) was associated with both P3 amplitudes (r = 0.57, p = 0.011) and P3 onset latencies (r = -0.56, p = 0.012). The observed P3 parameters were correlated with predicted P3 amplitude from the global rsFC (amplitude: r = +0.48, p = 0.037; latency: r = +0.40, p = 0.088) but not correlated with the rsFC over the most significant individual edge. P3 onset latency was primarily related to long-range connections between the prefrontal and parietal/limbic regions, while P3 amplitudes were related to connections between prefrontal and parietal/occipital, between sensorimotor and subcortical, and between limbic/subcortical and parietal/occipital regions. These results demonstrated the power of resting-state PCASL and P3 correlation with brain global functional connectivity.
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13
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Zhang P, He Z, Mao Y, Sun R, Qu Y, Chen L, Ma P, Yin S, Yin T, Zeng F. Aberrant resting-state functional connectivity and topological properties of the subcortical network in functional dyspepsia patients. Front Mol Neurosci 2022; 15:1001557. [PMCID: PMC9606653 DOI: 10.3389/fnmol.2022.1001557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 09/08/2022] [Indexed: 11/13/2022] Open
Abstract
Functional dyspepsia (FD) is a disorder of gut-brain interaction. Previous studies have demonstrated a wide range of abnormalities in functional brain activity and connectivity patterns in FD. However, the connectivity pattern of the subcortical network (SCN), which is a hub of visceral information transmission and processing, remains unclear in FD patients. The study compared the resting-state functional connectivity (rsFC) and the global and nodal topological properties of SCN between 109 FD patients and 98 healthy controls, and then explored the correlations between the connectivity metrics and clinical symptoms in FD patients. The results demonstrated that FD patients manifested the increased rsFC in seventeen edges among the SCN, decreased small-worldness and local efficiency in SCN, as well as increased nodal efficiency and nodal degree centrality in the anterior thalamus than healthy controls (p < 0.05, false discovery rate corrected). Moreover, the rsFC of the right anterior thalamus-left nucleus accumbens edge was significantly correlated with the NDSI scores (r = 0.255, p = 0.008, uncorrected) and NDLQI scores (r = −0.241, p = 0.013, uncorrected), the nodal efficiency of right anterior thalamus was significantly correlated with NDLQI scores (r = 0.204, p = 0.036, uncorrected) in FD patients. This study indicated the abnormal rsFC pattern, as well as global and nodal topological properties of the SCN, especially the bilateral anterior thalamus in FD patients, which enhanced our understanding of the central pathophysiology of FD and will lay the foundation for the objective diagnosis of FD and the development of new therapies.
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Affiliation(s)
- Pan Zhang
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zhaoxuan He
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yangke Mao
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ruirui Sun
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yuzhu Qu
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Li Chen
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Peihong Ma
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
| | - Shuai Yin
- First Affiliated Hospital, Henan University of Traditional Chinese Medicine, Zhengzhou, Henan, China
| | - Tao Yin
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- *Correspondence: Tao Yin,
| | - Fang Zeng
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Fang Zeng,
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14
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Vedaei F, Alizadeh M, Romo V, Mohamed FB, Wu C. The effect of general anesthesia on the test–retest reliability of resting-state fMRI metrics and optimization of scan length. Front Neurosci 2022; 16:937172. [PMID: 36051647 PMCID: PMC9425911 DOI: 10.3389/fnins.2022.937172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/27/2022] [Indexed: 01/01/2023] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has been known as a powerful tool in neuroscience. However, exploring the test–retest reliability of the metrics derived from the rs-fMRI BOLD signal is essential, particularly in the studies of patients with neurological disorders. Here, two factors, namely, the effect of anesthesia and scan length, have been estimated on the reliability of rs-fMRI measurements. A total of nine patients with drug-resistant epilepsy (DRE) requiring interstitial thermal therapy (LITT) were scanned in two states. The first scan was performed in an awake state before surgery on the same patient. The second scan was performed 2 weeks later under general anesthesia necessary for LITT surgery. At each state, two rs-fMRI sessions were obtained that each one lasted 15 min, and the effect of scan length was evaluated. Voxel-wise rs-fMRI metrics, including the amplitude of low-frequency fluctuation (ALFF), the fractional amplitude of low-frequency fluctuation (fALFF), functional connectivity (FC), and regional homogeneity (ReHo), were measured. Intraclass correlation coefficient (ICC) was calculated to estimate the reliability of the measurements in two states of awake and under anesthesia. Overall, it appeared that the reliability of rs-fMRI metrics improved under anesthesia. From the 15-min data, we found mean ICC values in awake state including 0.81, 0.51, 0.65, and 0.84 for ALFF, fALFF, FC, and ReHo, respectively, as well as 0.80, 0.59, 0.83, and 0.88 for ALFF, fALFF, FC, and ReHo, respectively, under anesthesia. Additionally, our findings revealed that reliability increases as the function of scan length. We showed that the optimized scan length to achieve less variability of rs-fMRI measurements was 3.1–7.5 min shorter in an anesthetized, compared to a wakeful state.
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Affiliation(s)
- Faezeh Vedaei
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- *Correspondence: Faezeh Vedaei
| | - Mahdi Alizadeh
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, PA, United States
| | - Victor Romo
- Department of Anesthesiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B. Mohamed
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chengyuan Wu
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, PA, United States
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15
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Nebel MB, Lidstone DE, Wang L, Benkeser D, Mostofsky SH, Risk BB. Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder? Neuroimage 2022; 257:119296. [PMID: 35561944 PMCID: PMC9233079 DOI: 10.1016/j.neuroimage.2022.119296] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 05/03/2022] [Accepted: 05/09/2022] [Indexed: 12/13/2022] Open
Abstract
The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder in children without an intellectual disability to illustrate the problem and the potential solution. We aggregated data from 545 children (8-13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 28.5% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 81.0% and 60.1% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.
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Affiliation(s)
- Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
| | - Daniel E Lidstone
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Liwei Wang
- Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA, United States
| | - David Benkeser
- Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA, United States
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Department of Psychiatry and Behavioral Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Benjamin B Risk
- Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA, United States
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16
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Yin T, He Z, Chen Y, Sun R, Yin S, Lu J, Yang Y, Liu X, Ma P, Qu Y, Zhang T, Suo X, Lei D, Gong Q, Tang Y, Liang F, Zeng F. Predicting acupuncture efficacy for functional dyspepsia based on functional brain network features: a machine learning study. Cereb Cortex 2022; 33:3511-3522. [PMID: 35965072 DOI: 10.1093/cercor/bhac288] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/28/2022] [Accepted: 06/29/2022] [Indexed: 12/19/2022] Open
Abstract
Acupuncture is effective in treating functional dyspepsia (FD), while its efficacy varies significantly from different patients. Predicting the responsiveness of different patients to acupuncture treatment based on the objective biomarkers would assist physicians to identify the candidates for acupuncture therapy. One hundred FD patients were enrolled, and their clinical characteristics and functional brain MRI data were collected before and after treatment. Taking the pre-treatment functional brain network as features, we constructed the support vector machine models to predict the responsiveness of FD patients to acupuncture treatment. These features contributing critically to the accurate prediction were identified, and the longitudinal analyses of these features were performed on acupuncture responders and non-responders. Results demonstrated that prediction models achieved an accuracy of 0.76 ± 0.03 in predicting acupuncture responders and non-responders, and a R2 of 0.24 ± 0.02 in predicting dyspeptic symptoms relief. Thirty-eight functional brain network features associated with the orbitofrontal cortex, caudate, hippocampus, and anterior insula were identified as the critical predictive features. Changes in these predictive features were more pronounced in responders than in non-responders. In conclusion, this study provided a promising approach to predicting acupuncture efficacy for FD patients and is expected to facilitate the optimization of personalized acupuncture treatment plans for FD.
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Affiliation(s)
- Tao Yin
- Acupuncture and Tuina School, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Zhaoxuan He
- Acupuncture and Tuina School, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China.,Key Laboratory of Sichuan Province for Acupuncture and Chronobiology, Chengdu, Sichuan 610075, China
| | - Yuan Chen
- International Education College, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China
| | - Ruirui Sun
- Acupuncture and Tuina School, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Shuai Yin
- First Affiliated Hospital, Henan University of Traditional Chinese Medicine, Zhengzhou, Henan 450002, China
| | - Jin Lu
- Acupuncture and Tuina School, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Yue Yang
- Acupuncture and Tuina School, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Xiaoyan Liu
- Acupuncture and Tuina School, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Peihong Ma
- Acupuncture and Tuina School, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China.,School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yuzhu Qu
- Acupuncture and Tuina School, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Tingting Zhang
- Acupuncture and Tuina School, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Xueling Suo
- Departments of Radiology, Huaxi Magnetic Resonance Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Du Lei
- Departments of Radiology, Huaxi Magnetic Resonance Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Qiyong Gong
- Departments of Radiology, Huaxi Magnetic Resonance Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Yong Tang
- Acupuncture and Tuina School, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China.,Key Laboratory of Sichuan Province for Acupuncture and Chronobiology, Chengdu, Sichuan 610075, China
| | - Fanrong Liang
- Acupuncture and Tuina School, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Fang Zeng
- Acupuncture and Tuina School, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China.,Key Laboratory of Sichuan Province for Acupuncture and Chronobiology, Chengdu, Sichuan 610075, China
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17
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van Balkom TD, van den Heuvel OA, Berendse HW, van der Werf YD, Vriend C. Eight-week multi-domain cognitive training does not impact large-scale resting-state brain networks in Parkinson's disease. Neuroimage Clin 2022; 33:102952. [PMID: 35123203 PMCID: PMC8819471 DOI: 10.1016/j.nicl.2022.102952] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 12/23/2021] [Accepted: 01/26/2022] [Indexed: 11/25/2022]
Abstract
There is meta-analytic evidence for the efficacy of cognitive training (CT) in Parkinson's disease (PD). We performed a randomized controlled trial where we found small positive effects of CT on executive function and processing speed in individuals with PD (ntotal = 140). In this study, we assessed the effects of CT on brain network connectivity and topology in a subsample of the full study population (nmri = 86). Participants were randomized into an online multi-domain CT and an active control condition and performed 24 sessions of either intervention in eight weeks. Resting-state functional MRI scans were acquired in addition to extensive clinical and neuropsychological assessments pre- and post-intervention. In line with our preregistered analysis plan (osf.io/3st82), we computed connectivity between 'cognitive' resting-state networks and computed topological outcomes at the whole-brain and sub-network level. We assessed group differences after the intervention with mixed-model analyses adjusting for baseline performance and analyzed the association between network and cognitive performance changes with repeated measures correlation analyses. The final analysis sample consisted of 71 participants (n CT = 37). After intervention there were no group differences on between-network connectivity and network topological outcomes. No associations between neural network and neuropsychological performance change were found. CT increased segregated network topology in a small sub-sample of cognitively intact participants. Post-hoc nodal analyses showed post-intervention enhanced connectivity of both the dorsal anterior cingulate cortex and dorsolateral prefrontal cortex in the CT group. The results suggest no large-scale brain network effects of eight-week computerized CT, but rather localized connectivity changes of key regions in cognitive function, that potentially reflect the specific effects of the intervention.
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Affiliation(s)
- Tim D van Balkom
- Amsterdam UMC, Vrije Universiteit Amsterdam, Psychiatry, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, Netherlands.
| | - Odile A van den Heuvel
- Amsterdam UMC, Vrije Universiteit Amsterdam, Psychiatry, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, Netherlands.
| | - Henk W Berendse
- Amsterdam UMC, Vrije Universiteit Amsterdam, Neurology, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, Netherlands.
| | - Ysbrand D van der Werf
- Amsterdam UMC, Vrije Universiteit Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, Netherlands.
| | - Chris Vriend
- Amsterdam UMC, Vrije Universiteit Amsterdam, Psychiatry, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, Netherlands.
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Xiao M, Chen X, Yi H, Luo Y, Yan Q, Feng T, He Q, Lei X, Qiu J, Chen H. Stronger functional network connectivity and social support buffer against negative affect during the COVID-19 outbreak and after the pandemic peak. Neurobiol Stress 2021; 15:100418. [PMID: 34805450 PMCID: PMC8592855 DOI: 10.1016/j.ynstr.2021.100418] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/27/2021] [Accepted: 11/15/2021] [Indexed: 01/17/2023] Open
Abstract
Health and financial uncertainties, as well as enforced social distancing, during the COVID-19 pandemic have adversely affected the mental health of people. These impacts are expected to continue even after the pandemic, particularly for those who lack support from family and friends. The salience network (SN), default mode network (DMN), and frontoparietal network (FPN) function in an interconnected manner to support information processing and emotional regulation processes in stressful contexts. In this study, we examined whether functional connectivity of the SN, DMN, and FPN, measured using resting-state functional magnetic resonance imaging before the pandemic, is a neurobiological marker of negative affect (NA) during the COVID-19 pandemic and after its peak in a large sample (N = 496, 360 females); the moderating role of social support in the brain-NA association was also investigated. We found that participants reported an increase in NA during the pandemic compared to before the pandemic, and the NA did not decrease, even after the peak period. People with higher connectivity within the SN and between the SN and the other two networks reported less NA during and after the COVID-19 outbreak peak, and the buffer effect was stronger if their social support was greater. These findings suggest that the functional networks that are responsible for affective processing and executive functioning, as well as the social support from family and friends, play an important role in protecting against NA under stressful and uncontrollable situations.
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Affiliation(s)
- Mingyue Xiao
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Ximei Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Haijing Yi
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Yijun Luo
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Qiaoling Yan
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Tingyong Feng
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Qinghua He
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Xu Lei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
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Wang F, Jiang Z, Li X, Bu L, Ji Y. Functional Brain Network Analysis of Knowledge Transfer While Engineering Problem-Solving. Front Hum Neurosci 2021; 15:713692. [PMID: 34759806 PMCID: PMC8573420 DOI: 10.3389/fnhum.2021.713692] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 09/09/2021] [Indexed: 12/04/2022] Open
Abstract
As a complex cognitive activity, knowledge transfer is mostly correlated to cognitive processes such as working memory, behavior control, and decision-making in the human brain while engineering problem-solving. It is crucial to explain how the alteration of the functional brain network occurs and how to express it, which causes the alteration of the cognitive structure of knowledge transfer. However, the neurophysiological mechanisms of knowledge transfer are rarely considered in existing studies. Thus, this study proposed functional connectivity (FC) to describe and evaluate the dynamic brain network of knowledge transfer while engineering problem-solving. In this study, we adopted the modified Wisconsin Card-Sorting Test (M-WCST) reported in the literature. The neural activation of the prefrontal cortex was continuously recorded for 31 participants using functional near-infrared spectroscopy (fNIRS). Concretely, we discussed the prior cognitive level, knowledge transfer distance, and transfer performance impacting the wavelet amplitude and wavelet phase coherence. The paired t-test results showed that the prior cognitive level and transfer distance significantly impact FC. The Pearson correlation coefficient showed that both wavelet amplitude and phase coherence are significantly correlated to the cognitive function of the prefrontal cortex. Therefore, brain FC is an available method to evaluate cognitive structure alteration in knowledge transfer. We also discussed why the dorsolateral prefrontal cortex (DLPFC) and occipital face area (OFA) distinguish themselves from the other brain areas in the M-WCST experiment. As an exploratory study in NeuroManagement, these findings may provide neurophysiological evidence about the functional brain network of knowledge transfer while engineering problem-solving.
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Affiliation(s)
- Fuhua Wang
- Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China
| | - Zuhua Jiang
- Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China
| | - Xinyu Li
- College of Mechanical Engineering, Donghua University, Shanghai, China.,School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | - Lingguo Bu
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China.,School of Software, Shandong University, Jinan, China
| | - Yongjun Ji
- Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China
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