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Weng Y, Kruschwitz J, Rueda-Delgado LM, Ruddy K, Boyle R, Franzen L, Serin E, Nweze T, Hanson J, Smyth A, Farnan T, Banaschewski T, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, McGrath J, Nees F, Orfanos DP, Paus T, Poustka L, Holz N, Fröhner JH, Smolka MN, Vaidya N, Schumann G, Walter H, Whelan R. A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.03.587900. [PMID: 38617224 PMCID: PMC11014614 DOI: 10.1101/2024.04.03.587900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Substance use, including cigarettes and cannabis, is associated with poorer sustained attention in late adolescence and early adulthood. Previous studies were predominantly cross-sectional or under-powered and could not indicate if impairment in sustained attention was a predictor of substance-use or a marker of the inclination to engage in such behaviour. This study explored the relationship between sustained attention and substance use across a longitudinal span from ages 14 to 23 in over 1,000 participants. Behaviours and brain connectivity associated with diminished sustained attention at age 14 predicted subsequent increases in cannabis and cigarette smoking, establishing sustained attention as a robust biomarker for vulnerability to substance use. Individual differences in network strength relevant to sustained attention were preserved across developmental stages and sustained attention networks generalized to participants in an external dataset. In summary, brain networks of sustained attention are robust, consistent, and able to predict aspects of later substance use.
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Affiliation(s)
- Yihe Weng
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Johann Kruschwitz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Collaborative Research Centre (SFB 940) "Volition and Cognitive Control", Technische Universität Dresden, 01069, Dresden, Germany
| | - Laura M Rueda-Delgado
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Kathy Ruddy
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
- School of Psychology, Queens University Belfast, Belfast, Northern Ireland, UK
| | - Rory Boyle
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Luisa Franzen
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Emin Serin
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Charité -Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, 10117, Berlin, Germany
- Bernstein Center for Computational Neuroscience, 10115, Berlin, Germany
| | | | - Jamie Hanson
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA; Learning Research & Development Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alannah Smyth
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Tom Farnan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, United Kingdom
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, Vermont, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 "Trajectoires développementales & psychiatrie", University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli; Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 "Trajectoires développementales & psychiatrie", University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli; Gif-sur-Yvette; and AP-HP. Sorbonne University, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 "Trajectoires développementales & psychiatrie", University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli; Gif-sur-Yvette; and Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Jane McGrath
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | | | - Tomáš Paus
- Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hosptalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Nathalie Holz
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Germany
| | - Nilakshi Vaidya
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Germany
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
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2
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Persson H, Li TQ, Markovic G. CHANGES IN FUNCTIONAL CONNECTIVITY FOLLOWING INTENSIVE ATTENTION TRAINING IN PATIENTS WITH TRAUMATIC BRAIN INJURY. A PILOT STUDY. JOURNAL OF REHABILITATION MEDICINE. CLINICAL COMMUNICATIONS 2024; 7:12436. [PMID: 38264065 PMCID: PMC10802785 DOI: 10.2340/jrmcc.v7.12436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024]
Abstract
Objective To explore functional connectivity after intensive attention training in the chronic phase after traumatic brain injury as clinical evidence indicates that intensive attention training improves attention dysfunction in persons with traumatic brain injury. Design and subjects A case series study. Two young adults, 13- and 18-months post traumatic brain injury, with traumatic brain injury induced attention deficits were assigned to 20 h of intensive attention training and neuroimaging. Methods Functional magnetic resonance imaging during a psychomotor vigilance test was conducted pre- and post-intervention. Results The neuroimaging indicated both increased and decreased connectivity density in frontal, posterior and subcortical brain regions, for some regions with separate change patterns for left and right hemisphere respectively, and an overall reduction in variability in functional connectivity. Conclusion The changed and decreased variability of functional connectivity in various brain regions, captured by fMRI during a psychomotor vigilance test after direct attention training in a small sample of persons with traumatic brain injury, suggests further studies of functional connectivity changes in neural networks.
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Affiliation(s)
- Hanna Persson
- Division of Rehabilitation Medicine, Danderyd University Hospital
| | - Tie-Qiang Li
- Department of Clinical Science, Intervention, and Technology, Karolinska Institutet
- Department of Medical Radiation and Nuclear Medicine, Karolinska University Hospital
| | - Gabriela Markovic
- Division of Rehabilitation Medicine, Danderyd University Hospital
- Department of Clinical Sciences, Karolinska Institutet, Danderyd University Hospital, Stockholm, Sweden
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3
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Ju Y, Wang M, Liu J, Liu B, Yan D, Lu X, Sun J, Dong Q, Zhang L, Guo H, Zhao F, Liao M, Zhang L, Zhang Y, Li L. Modulation of resting-state functional connectivity in default mode network is associated with the long-term treatment outcome in major depressive disorder. Psychol Med 2023; 53:5963-5975. [PMID: 36164996 DOI: 10.1017/s0033291722002628] [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] [Indexed: 11/07/2022]
Abstract
BACKGROUND Treatment non-response and recurrence are the main sources of disease burden in major depressive disorder (MDD). However, little is known about its neurobiological mechanism concerning the brain network changes accompanying pharmacotherapy. The present study investigated the changes in the intrinsic brain networks during 6-month antidepressant treatment phase associated with the treatment response and recurrence in MDD. METHODS Resting-state functional magnetic resonance imaging was acquired from untreated patients with MDD and healthy controls at baseline. The patients' depressive symptoms were monitored by using the Hamilton Rating Scale for Depression (HAMD). After 6 months of antidepressant treatment, patients were re-scanned and followed up every 6 months over 2 years. Traditional statistical analysis as well as machine learning approaches were conducted to investigate the longitudinal changes in macro-scale resting-state functional network connectivity (rsFNC) strength and micro-scale resting-state functional connectivity (rsFC) associated with long-term treatment outcome in MDD. RESULTS Repeated measures of the general linear model demonstrated a significant difference in the default mode network (DMN) rsFNC change before and after the 6-month antidepressant treatment between remitters and non-remitters. The difference in the rsFNC change over the 6-month antidepressant treatment between recurring and stable MDD was also specific to DMN. Machine learning analysis results revealed that only the DMN rsFC change successfully distinguished non-remitters from the remitters at 6 months and recurring from stable MDD during the 2-year follow-up. CONCLUSION Our findings demonstrated that the intrinsic DMN connectivity could be a unique and important target for treatment and recurrence prevention in MDD.
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Affiliation(s)
- Yumeng Ju
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Mi Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Jin Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Bangshan Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Danfeng Yan
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Xiaowen Lu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Jinrong Sun
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Qiangli Dong
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Liang Zhang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian, Henan 463000, China
| | - Futao Zhao
- Zhumadian Psychiatric Hospital, Zhumadian, Henan 463000, China
| | - Mei Liao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Li Zhang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Yan Zhang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Lingjiang Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
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4
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Dhamala E, Rong Ooi LQ, Chen J, Ricard JA, Berkeley E, Chopra S, Qu Y, Zhang XH, Lawhead C, Yeo BTT, Holmes AJ. Brain-Based Predictions of Psychiatric Illness-Linked Behaviors Across the Sexes. Biol Psychiatry 2023; 94:479-491. [PMID: 37031778 PMCID: PMC10524434 DOI: 10.1016/j.biopsych.2023.03.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/11/2023]
Abstract
BACKGROUND Individual differences in functional brain connectivity can be used to predict both the presence of psychiatric illness and variability in associated behaviors. However, despite evidence for sex differences in functional network connectivity and in the prevalence, presentation, and trajectory of psychiatric illnesses, the extent to which disorder-relevant aspects of network connectivity are shared or unique across the sexes remains to be determined. METHODS In this work, we used predictive modeling approaches to evaluate whether shared or unique functional connectivity correlates underlie the expression of psychiatric illness-linked behaviors in males and females in data from the Adolescent Brain Cognitive Development Study (N = 5260; 2571 females). RESULTS We demonstrate that functional connectivity profiles predict individual differences in externalizing behaviors in males and females but predict internalizing behaviors only in females. Furthermore, models trained to predict externalizing behaviors in males generalize to predict internalizing behaviors in females, and models trained to predict internalizing behaviors in females generalize to predict externalizing behaviors in males. Finally, the neurobiological correlates of many behaviors are largely shared within and across sexes: functional connections within and between heteromodal association networks, including default, limbic, control, and dorsal attention networks, are associated with internalizing and externalizing behaviors. CONCLUSIONS Taken together, these findings suggest that shared neurobiological patterns may manifest as distinct behaviors across the sexes. Based on these results, we recommend that both clinicians and researchers carefully consider how sex may influence the presentation of psychiatric illnesses, especially those along the internalizing-externalizing spectrum.
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Affiliation(s)
- Elvisha Dhamala
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York; Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut.
| | - Leon Qi Rong Ooi
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore
| | - Jocelyn A Ricard
- Department of Psychology, Yale University, New Haven, Connecticut
| | | | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Yueyue Qu
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Xi-Han Zhang
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Connor Lawhead
- Department of Psychology, Yale University, New Haven, Connecticut
| | - B T Thomas Yeo
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut; Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, New Jersey.
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5
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Chen X, Dong D, Zhou F, Gao X, Liu Y, Wang J, Qin J, Tian Y, Xiao M, Xu X, Li W, Qiu J, Feng T, He Q, Lei X, Chen H. Connectome-based prediction of eating disorder-associated symptomatology. Psychol Med 2023; 53:5786-5799. [PMID: 36177890 DOI: 10.1017/s0033291722003026] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM). METHODS CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants. RESULTS The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect. CONCLUSIONS These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.
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Affiliation(s)
- Ximei Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Feng Zhou
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xiao Gao
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Junjie Wang
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Jingmin Qin
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Yun Tian
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Mingyue Xiao
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xiaofei Xu
- School of Computing Technologies, RMIT University, Melbourne, Australia
| | - Wei Li
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, China
| | - Tingyong Feng
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
| | - Qinghua He
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, China
| | - Xu Lei
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
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6
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Dhamala E, Yeo BTT, Holmes AJ. One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry. Biol Psychiatry 2023; 93:717-728. [PMID: 36577634 DOI: 10.1016/j.biopsych.2022.09.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 09/07/2022] [Accepted: 09/23/2022] [Indexed: 12/30/2022]
Abstract
Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo neuroimaging data have led to fundamental advances in our understanding of the neurobiology of psychiatric illnesses. More recently, access to computational resources and large, publicly available datasets alongside the rise of predictive modeling and precision medicine approaches have facilitated the study of psychiatric illnesses at an individual level. Data-driven machine learning analyses can be applied to identify disease-relevant biological subtypes, predict individual symptom profiles, and recommend personalized therapeutic interventions. However, when developing these predictive models, methodological choices must be carefully considered to ensure accurate, robust, and interpretable results. Choices pertaining to algorithms, neuroimaging modalities and states, data transformation, phenotypes, parcellations, sample sizes, and populations we are specifically studying can influence model performance. Here, we review applications of neuroimaging-based machine learning models to study psychiatric illnesses and discuss the effects of different methodological choices on model performance. An understanding of these effects is crucial for the proper implementation of predictive models in psychiatry and will facilitate more accurate diagnoses, prognoses, and therapeutics.
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Affiliation(s)
- Elvisha Dhamala
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut.
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut.
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7
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Ovando-Tellez M, Kenett YN, Benedek M, Bernard M, Belo J, Beranger B, Bieth T, Volle E. Brain Connectivity-Based Prediction of Combining Remote Semantic Associates for Creative Thinking. CREATIVITY RESEARCH JOURNAL 2023. [DOI: 10.1080/10400419.2023.2192563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Affiliation(s)
- Marcela Ovando-Tellez
- Sorbonne University, FrontLab at Paris Brain Institute (ICM), INSERM, CNRS, Paris, France
| | - Yoed N. Kenett
- Faculty of Data and Decision Sciences, Technion – Israel Institute of Technology,Haifa Israel
| | | | - Matthieu Bernard
- Sorbonne University, FrontLab at Paris Brain Institute (ICM), INSERM, CNRS, Paris, France
| | - Joan Belo
- Sorbonne University, FrontLab at Paris Brain Institute (ICM), INSERM, CNRS, Paris, France
| | - Benoit Beranger
- Sorbonne University, CENIR at Paris Brain Institute (ICM), INSERM, CNRS, Paris, France
| | - Theophile Bieth
- Sorbonne University, FrontLab at Paris Brain Institute (ICM), INSERM, CNRS, Paris, France
- Neurology department, Pitié-Salpêtrière hospital, AP-HP, Paris, France
| | - Emmanuelle Volle
- Sorbonne University, FrontLab at Paris Brain Institute (ICM), INSERM, CNRS, Paris, France
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8
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Ren Z, Sun J, Liu C, Li X, Li X, Li X, Liu Z, Bi T, Qiu J. Individualized prediction of trait self-control from whole-brain functional connectivity. Psychophysiology 2023; 60:e14209. [PMID: 36325626 DOI: 10.1111/psyp.14209] [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: 09/18/2021] [Revised: 09/24/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022]
Abstract
Self-control is a core psychological construct for human beings and it plays a crucial role in the adaptation to society and achievement of success and happiness for individuals. Although progress has been made in behavioral studies examining self-control, its neural mechanisms remain unclear. In this study, we employed a machine-learning approach-relevance vector regression (RVR) to explore the potential predictive power of intrinsic functional connections to trait self-control in a large sample (N = 390). We used resting-state functional MRI (fMRI) to explore whole-brain functional connectivity patterns characteristic of 390 healthy adults and to confirm the effectiveness of RVR in predicting individual trait self-control scores. A set of connections across multiple neural networks that significantly predicted individual differences were identified, including the classic control network (e.g., fronto-parietal network (FPN), salience network (SAL)), the sensorimotor network (Mot), and the medial frontal network (MF). Key nodes that contributed to the predictive model included the dorsolateral prefrontal cortex (dlPFC), middle frontal gyrus (MFG), anterior cingulate and paracingulate gyri, inferior temporal gyrus (ITG) that have been associated with trait self-control. Our findings further assert that self-control is a multidimensional construct rooted in the interactions between multiple neural networks.
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Affiliation(s)
- Zhiting Ren
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Jiangzhou Sun
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
- College of International Studies, Southwest University, Chongqing, China
| | - Cheng Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Xinyue Li
- Department of Psychology, University of Washington, Seattle, Washington, USA
| | - Xianrui Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Xinyi Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Zeqing Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Taiyong Bi
- Centre for Mental Health Research in School of Management, Zunyi Medical University, Zunyi, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
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9
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Porter A, Nielsen A, Dorn M, Dworetsky A, Edmonds D, Gratton C. Masked features of task states found in individual brain networks. Cereb Cortex 2023; 33:2879-2900. [PMID: 35802477 PMCID: PMC10016040 DOI: 10.1093/cercor/bhac247] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 11/14/2022] Open
Abstract
Completing complex tasks requires that we flexibly integrate information across brain areas. While studies have shown how functional networks are altered during different tasks, this work has generally focused on a cross-subject approach, emphasizing features that are common across people. Here we used extended sampling "precision" fMRI data to test the extent to which task states generalize across people or are individually specific. We trained classifiers to decode state using functional network data in single-person datasets across 5 diverse task states. Classifiers were then tested on either independent data from the same person or new individuals. Individualized classifiers were able to generalize to new participants. However, classification performance was significantly higher within a person, a pattern consistent across model types, people, tasks, feature subsets, and even for decoding very similar task conditions. Notably, these findings also replicated in a new independent dataset. These results suggest that individual-focused approaches can uncover robust features of brain states, including features obscured in cross-subject analyses. Individual-focused approaches have the potential to deepen our understanding of brain interactions during complex cognition.
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Affiliation(s)
- Alexis Porter
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Ashley Nielsen
- Department of Neurology, Washington University in St. Louis, 1 Brookings Dr, St. Louis, MO 63130, United States
| | - Megan Dorn
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Ally Dworetsky
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Donnisa Edmonds
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Caterina Gratton
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
- Department of Neurology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
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10
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Sankar A, Shen X, Colic L, Goldman DA, Villa LM, Kim JA, Pittman B, Scheinost D, Constable RT, Blumberg HP. Predicting depressed and elevated mood symptomatology in bipolar disorder using brain functional connectomes. Psychol Med 2023; 53:1-10. [PMID: 36891769 PMCID: PMC10491744 DOI: 10.1017/s003329172300003x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 12/07/2022] [Accepted: 01/03/2023] [Indexed: 03/10/2023]
Abstract
BACKGROUND The study is aimed to identify brain functional connectomes predictive of depressed and elevated mood symptomatology in individuals with bipolar disorder (BD) using the machine learning approach Connectome-based Predictive Modeling (CPM). METHODS Functional magnetic resonance imaging data were obtained from 81 adults with BD while they performed an emotion processing task. CPM with 5000 permutations of leave-one-out cross-validation was applied to identify functional connectomes predictive of depressed and elevated mood symptom scores on the Hamilton Depression and Young Mania rating scales. The predictive ability of the identified connectomes was tested in an independent sample of 43 adults with BD. RESULTS CPM predicted the severity of depressed [concordance between actual and predicted values (r = 0.23, pperm (permutation test) = 0.031) and elevated (r = 0.27, pperm = 0.01) mood. Functional connectivity of left dorsolateral prefrontal cortex and supplementary motor area nodes, with inter- and intra-hemispheric connections to other anterior and posterior cortical, limbic, motor, and cerebellar regions, predicted depressed mood severity. Connectivity of left fusiform and right visual association area nodes with inter- and intra-hemispheric connections to the motor, insular, limbic, and posterior cortices predicted elevated mood severity. These networks were predictive of mood symptomatology in the independent sample (r ⩾ 0.45, p = 0.002). CONCLUSIONS This study identified distributed functional connectomes predictive of depressed and elevated mood severity in BD. Connectomes subserving emotional, cognitive, and psychomotor control predicted depressed mood severity, while those subserving emotional and social perceptual functions predicted elevated mood severity. Identification of these connectome networks may help inform the development of targeted treatments for mood symptoms.
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Affiliation(s)
- Anjali Sankar
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Lejla Colic
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- German Center for Mental Health, Halle-Jena-Magdeburg, Magdeburg, Germany
| | - Danielle A. Goldman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
| | - Luca M. Villa
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Jihoon A. Kim
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Brian Pittman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R. Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Hilary P. Blumberg
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
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11
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Wang X, Zwosta K, Wolfensteller U, Ruge H. Changes in global functional network properties predict individual differences in habit formation. Hum Brain Mapp 2023; 44:1565-1578. [PMID: 36413054 PMCID: PMC9921330 DOI: 10.1002/hbm.26158] [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: 06/01/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 11/23/2022] Open
Abstract
Prior evidence suggests that sensorimotor regions play a crucial role in habit formation. Yet, whether and how their global functional network properties might contribute to a more comprehensive characterization of habit formation still remains unclear. Capitalizing on advances in Elastic Net regression and predictive modeling, we examined whether learning-related functional connectivity alterations distributed across the whole brain could predict individual habit strength. Using the leave-one-subject-out cross-validation strategy, we found that the habit strength score of the novel unseen subjects could be successfully predicted. We further characterized the contribution of both, individual large-scale networks and individual brain regions by calculating their predictive weights. This highlighted the pivotal role of functional connectivity changes involving the sensorimotor network and the cingulo-opercular network in subject-specific habit strength prediction. These results contribute to the understanding the neural basis of human habit formation by demonstrating the importance of global functional network properties especially also for predicting the observable behavioral expression of habits.
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Affiliation(s)
- Xiaoyu Wang
- Fakultät Psychologie, Technische Universität Dresden, Dresden, Germany
| | - Katharina Zwosta
- Fakultät Psychologie, Technische Universität Dresden, Dresden, Germany
| | - Uta Wolfensteller
- Fakultät Psychologie, Technische Universität Dresden, Dresden, Germany
| | - Hannes Ruge
- Fakultät Psychologie, Technische Universität Dresden, Dresden, Germany
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12
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Does the Degree and/or Direction of Handedness in Children With Developmental Coordination Disorder Influence Motor and Cognitive Performance? A Pilot Study. JOURNAL OF MOTOR LEARNING AND DEVELOPMENT 2023. [DOI: 10.1123/jmld.2022-0045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Research has shown that nonright-handedness in children with developmental coordination disorder (DCD) is associated with poorer motor and cognitive performance. This study investigated the influence of degree and direction of handedness on performance using the Home Handedness Questionnaire, the Hit-the-Dot test, the Movement Assessment Battery for Children, and the digital Trail-Making Test. Eighteen children with DCD and 21 typically developing children aged 8–12 years participated in this study. The distribution of degree and direction of handedness in the group of DCD children were not different from that found in the typically developing group. In the Hit-the-Dot test, typically developing children significantly performed better than children with DCD, no matter which hand was dominant or to which degree. A significant inconsistent-handed advantage in the subdomain balance was found for children with DCD. Inconsistent handedness also seems to be an advantage for children with DCD on the digital Trail-Making Test performance. The relationship between the subcategories of the Movement Assessment Battery for Children and the digital Trail-Making Test part B is stronger for consistent than for inconsistent handedness. Our findings suggest that children with DCD and inconsistent handedness might benefit from greater crosstalk across hemispheres. In addition, these predispositions can be reinforced or discouraged throughout development and via occupational therapy.
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13
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Kardan O, Stier AJ, Cardenas-Iniguez C, Schertz KE, Pruin JC, Deng Y, Chamberlain T, Meredith WJ, Zhang X, Bowman JE, Lakhtakia T, Tindel L, Avery EW, Lin Q, Yoo K, Chun MM, Berman MG, Rosenberg MD. Differences in the functional brain architecture of sustained attention and working memory in youth and adults. PLoS Biol 2022; 20:e3001938. [PMID: 36542658 DOI: 10.1371/journal.pbio.3001938] [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: 05/03/2022] [Revised: 01/05/2023] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
Sustained attention (SA) and working memory (WM) are critical processes, but the brain networks supporting these abilities in development are unknown. We characterized the functional brain architecture of SA and WM in 9- to 11-year-old children and adults. First, we found that adult network predictors of SA generalized to predict individual differences and fluctuations in SA in youth. A WM model predicted WM performance both across and within children-and captured individual differences in later recognition memory-but underperformed in youth relative to adults. We next characterized functional connections differentially related to SA and WM in youth compared to adults. Results revealed 2 network configurations: a dominant architecture predicting performance in both age groups and a secondary architecture, more prominent for WM than SA, predicting performance in each age group differently. Thus, functional connectivity (FC) predicts SA and WM in youth, with networks predicting WM performance differing more between youths and adults than those predicting SA.
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Affiliation(s)
- Omid Kardan
- University of Chicago, Chicago, Illinois, United States of America
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Andrew J Stier
- University of Chicago, Chicago, Illinois, United States of America
| | | | | | - Julia C Pruin
- University of Chicago, Chicago, Illinois, United States of America
| | - Yuting Deng
- University of Chicago, Chicago, Illinois, United States of America
| | | | - Wesley J Meredith
- University of California, Los Angeles, California, United States of America
| | - Xihan Zhang
- University of Chicago, Chicago, Illinois, United States of America
- Yale University, New Haven, Connecticut, United States of America
| | - Jillian E Bowman
- University of Chicago, Chicago, Illinois, United States of America
| | - Tanvi Lakhtakia
- University of Chicago, Chicago, Illinois, United States of America
| | - Lucy Tindel
- University of Chicago, Chicago, Illinois, United States of America
| | - Emily W Avery
- Yale University, New Haven, Connecticut, United States of America
| | - Qi Lin
- Yale University, New Haven, Connecticut, United States of America
| | - Kwangsun Yoo
- Yale University, New Haven, Connecticut, United States of America
| | - Marvin M Chun
- Yale University, New Haven, Connecticut, United States of America
| | - Marc G Berman
- University of Chicago, Chicago, Illinois, United States of America
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14
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Ma SS, Zhang JT, Song KR, Zhao R, Fang RH, Wang LB, Yao ST, Hu YF, Jiang XY, Potenza MN, Fang XY. Connectome-based prediction of marital quality in husbands' processing of spousal interactions. Soc Cogn Affect Neurosci 2022; 17:1055-1067. [PMID: 35560211 PMCID: PMC9714425 DOI: 10.1093/scan/nsac034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 04/12/2022] [Accepted: 05/13/2022] [Indexed: 01/12/2023] Open
Abstract
Marital quality may decrease during the early years of marriage. Establishing models predicting individualized marital quality may help develop timely and effective interventions to maintain or improve marital quality. Given that marital interactions have an important impact on marital well-being cross-sectionally and prospectively, neural responses during marital interactions may provide insight into neural bases underlying marital well-being. The current study applies connectome-based predictive modeling, a recently developed machine-learning approach, to functional magnetic resonance imaging (fMRI) data from both partners of 25 early-stage Chinese couples to examine whether an individual's unique pattern of brain functional connectivity (FC) when responding to spousal interactive behaviors can reliably predict their own and their partners' marital quality after 13 months. Results revealed that husbands' FC involving multiple large networks, when responding to their spousal interactive behaviors, significantly predicted their own and their wives' marital quality, and this predictability showed gender specificity. Brain connectivity patterns responding to general emotional stimuli and during the resting state were not significantly predictive. This study demonstrates that husbands' differences in large-scale neural networks during marital interactions may contribute to their variability in marital quality and highlights gender-related differences. The findings lay a foundation for identifying reliable neuroimaging biomarkers for developing interventions for marital quality early in marriages.
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Affiliation(s)
- Shan-Shan Ma
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Jin-Tao Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Kun-Ru Song
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Rui Zhao
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Ren-Hui Fang
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Luo-Bin Wang
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Shu-Ting Yao
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Yi-Fan Hu
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA
| | - Xin-Ying Jiang
- Institute of Developmental Psychology, Beijing Normal University, Beijing 100875, China
| | - Marc N Potenza
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
- Child Study Center, Yale University School of Medicine, New Haven, CT 06519, USA
- Connecticut Council on Problem Gambling, Wethersfield, CT 06109, USA
- Connecticut Mental Health Center, New Haven, CT 06519, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Xiao-Yi Fang
- Correspondence should be addressed to Xiao-Yi Fang, Institute of Developmental Psychology, Beijing Normal University, No. 19, Xinjiekou Wai Street, Haidian District, Beijing 100875, China. E-mail:
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15
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Yan Y, Hulbert JC, Zhuang K, Liu W, Wei D, Qiu J, Anderson MC, Yang W. Reduced hippocampal-cortical connectivity during memory suppression predicts the ability to forget unwanted memories. Cereb Cortex 2022; 33:4189-4201. [PMID: 36156067 PMCID: PMC10110427 DOI: 10.1093/cercor/bhac336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/27/2022] [Accepted: 07/28/2022] [Indexed: 11/13/2022] Open
Abstract
The ability to suppress unwelcome memories is important for productivity and well-being. Successful memory suppression is associated with hippocampal deactivations and a concomitant disruption of this region's functionality. Much of the previous neuroimaging literature exploring such suppression-related hippocampal modulations has focused on the region's negative coupling with the prefrontal cortex. Task-based changes in functional connectivity between the hippocampus and other brain regions still need further exploration. In the present study, we utilize psychophysiological interactions and seed connectome-based predictive modeling to investigate the relationship between the hippocampus and the rest of the brain as 134 participants attempted to suppress unwanted memories during the Think/No-Think task. The results show that during retrieval suppression, the right hippocampus exhibited decreased functional connectivity with visual cortical areas (lingual and cuneus gyrus), left nucleus accumbens and the brain-stem that predicted superior forgetting of unwanted memories on later memory tests. Validation tests verified that prediction performance was not an artifact of head motion or prediction method and that the negative features remained consistent across different brain parcellations. These findings suggest that systemic memory suppression involves more than the modulation of hippocampal activity-it alters functional connectivity patterns between the hippocampus and visual cortex, leading to successful forgetting.
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Affiliation(s)
- Yuchi Yan
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, No. 2 TianSheng Road, Beibei District, Chongqing 400715, China.,Faculty of Psychology, Southwest University (SWU), No. 2 TianShen Road, Beibei District, Chongqing 400715, China
| | - Justin C Hulbert
- Psychology Program, Bard College, PO Box 5000, Annandale-on-Hudson, New York 12504, United States
| | - Kaixiang Zhuang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, No. 2 TianSheng Road, Beibei District, Chongqing 400715, China.,Faculty of Psychology, Southwest University (SWU), No. 2 TianShen Road, Beibei District, Chongqing 400715, China
| | - Wei Liu
- School of Psychology, Central China Normal University (CCNU), No. 152 Luoyu Road, Hongshan, Wuhan 430079, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, No. 2 TianSheng Road, Beibei District, Chongqing 400715, China.,Faculty of Psychology, Southwest University (SWU), No. 2 TianShen Road, Beibei District, Chongqing 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, No. 2 TianSheng Road, Beibei District, Chongqing 400715, China.,Faculty of Psychology, Southwest University (SWU), No. 2 TianShen Road, Beibei District, Chongqing 400715, China
| | - Michael C Anderson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, United Kingdom
| | - Wenjing Yang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, No. 2 TianSheng Road, Beibei District, Chongqing 400715, China.,Faculty of Psychology, Southwest University (SWU), No. 2 TianShen Road, Beibei District, Chongqing 400715, China
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16
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Michon KJ, Khammash D, Simmonite M, Hamlin AM, Polk TA. Person-specific and precision neuroimaging: Current methods and future directions. Neuroimage 2022; 263:119589. [PMID: 36030062 DOI: 10.1016/j.neuroimage.2022.119589] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/13/2022] [Accepted: 08/23/2022] [Indexed: 10/31/2022] Open
Abstract
Most neuroimaging studies of brain function analyze data in normalized space to identify regions of common activation across participants. These studies treat interindividual differences in brain organization as noise, but this approach can obscure important information about the brain's functional architecture. Recently, a number of studies have adopted a person-specific approach that aims to characterize these individual differences and explore their reliability and implications for behavior. A subset of these studies has taken a precision imaging approach that collects multiple hours of data from each participant to map brain function on a finer scale. In this review, we provide a broad overview of how person-specific and precision imaging techniques have used resting-state measures to examine individual differences in the brain's organization and their impact on behavior, followed by how task-based activity continues to add detail to these discoveries. We argue that person-specific and precision approaches demonstrate substantial promise in uncovering new details of the brain's functional organization and its relationship to behavior in many areas of cognitive neuroscience. We also discuss some current limitations in this new field and some new directions it may take.
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Affiliation(s)
| | - Dalia Khammash
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Molly Simmonite
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Abbey M Hamlin
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Thad A Polk
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
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17
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Prakash RS, Fountain-Zaragoza S, Fisher M, Gbadeyan O, Andridge R, Kiecolt-Glaser J, Manglani HR, Duraney EJ, Shankar A, McKenna MR, Teng J, Phansikar M, Canter R. Protocol for a randomized controlled trial of mindfulness-based stress reduction to improve attentional control in older adults (HealthyAgers trial). BMC Geriatr 2022; 22:666. [PMID: 35964000 PMCID: PMC9375078 DOI: 10.1186/s12877-022-03334-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/23/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Mindfulness meditation is a form of mind-body intervention that has increasing scientific support for its ability to reduce age-related declines in cognitive functioning, improve affective health, and strengthen the neural circuitry supporting improved cognitive and affective health. However, the majority of existent studies have been pilot investigations with small sample sizes, limited follow-up data, and a lack of attention to expectancy effects. Here, we present the study design of a Phase I/II, efficacy trial-HealthyAgers trial-that examines the benefits of a manualized mindfulness-based stress reduction program in improving attentional control and reducing mind-wandering in older adults. METHODS One hundred fifty older adults (ages 65-85 years) will be randomized into one of two groups: an eight-week mindfulness program or an eight-week, placebo-controlled, lifestyle education program. Behavioral and neuroimaging assessments are conducted before and after the training. Participants are then invited to booster sessions once every three months for a period of 12 months with post-intervention follow-up assessments conducted at 6-months and 12-months. The primary outcomes for the study are behavioral measures of attentional control and mind-wandering. Additional, secondary outcomes include network strength in an a priori defined neuromarker of attentional control, fluid and everyday cognition, emotion regulation strategy use, and markers of inflammation. DISCUSSION This study will establish the efficacy of a group-based, low-cost mind-body intervention for the inter-related facets of attentional control and mind-wandering in older adults. Strengths of this study include a well-designed, placebo-controlled comparison group, use of web/mobile application to track study adherence, and longitudinal follow-up. TRIAL REGISTRATION Clinicaltrials.gov (# NCT03626532 ). Registered August 4, 2018.
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Affiliation(s)
- Ruchika Shaurya Prakash
- Department of Psychology, The Ohio State University, 62 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA.
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA.
| | - Stephanie Fountain-Zaragoza
- Department of Psychology, The Ohio State University, 62 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA
- Department of Health Sciences and Research, Medical University of South Carolina, Charleston, SC, USA
| | - Megan Fisher
- Department of Psychology, The Ohio State University, 62 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA
| | - Oyetunde Gbadeyan
- Department of Psychology, The Ohio State University, 62 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA
- National Centre for Healthy Ageing, Peninsula Clinical School, Faculty of Medicine, Monash University, Melbourne, Australia
| | - Rebecca Andridge
- Department of Biostatistics, The Ohio State University, Columbus, OH, USA
| | | | - Heena R Manglani
- Department of Psychology, The Ohio State University, 62 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA
| | - Elizabeth J Duraney
- Department of Psychology, The Ohio State University, 62 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA
| | - Anita Shankar
- Department of Psychology, The Ohio State University, 62 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA
| | - Michael R McKenna
- Department of Psychology, The Ohio State University, 62 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA
| | - James Teng
- Department of Psychology, The Ohio State University, 62 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA
| | - Madhura Phansikar
- Department of Psychology, The Ohio State University, 62 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA
| | - Rosie Canter
- Department of Psychology, The Ohio State University, 62 Psychology Building, 1835 Neil Avenue, Columbus, OH, 43210, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA
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18
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Manglani HR, Fountain-Zaragoza S, Shankar A, Nicholas JA, Prakash RS. Employing Connectome-Based Models to Predict Working Memory in Multiple Sclerosis. Brain Connect 2022; 12:502-514. [PMID: 34309408 PMCID: PMC10039278 DOI: 10.1089/brain.2021.0037] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Introduction: Individuals with multiple sclerosis (MS) are vulnerable to deficits in working memory (WM), but the search for neural correlates of WM within circumscribed areas has been inconclusive. Given the widespread neural alterations observed in MS, predictive modeling approaches that capitalize on whole-brain connectivity may better capture individual differences in WM. Materials and Methods: We applied connectome-based predictive modeling to functional magnetic resonance imaging data from WM tasks in two independent samples with relapsing-remitting MS. In the internal sample (ninternal = 36), cross-validation was used to train a model to predict accuracy on the Paced Visual Serial Addition Test from functional connectivity. We hypothesized that this MS-specific model would successfully predict performance on the N-back task in the validation cohort (nvalidation = 36). In addition, we assessed the generalizability of existing WM networks derived in healthy young adults to these samples, and we explored anatomical differences between the healthy and MS networks. Results: We successfully derived an MS-specific predictive model of WM in the internal sample (full: rs = 0.47, permuted p = 0.011), but the predictions were not significant in the validation cohort (rs = -0.047; p = 0.78, mean squared error [MSE] = 0.006, R2 = -2.21%). In contrast, the healthy networks successfully predicted WM in both MS samples (internal: rs = 0.33 p = 0.049, MSE = 0.009, R2 = 13.4%; validation cohort: rs = 0.46, p = 0.005, MSE = 0.005, R2 = 16.9%), demonstrating their translational potential. Discussion: Functional networks identified in a large sample of healthy individuals predicted significant variance in WM in MS. Networks derived in small samples of people with MS may have limited generalizability, potentially due to disease-related heterogeneity. The robustness of models derived in large clinical samples warrants further investigation. ClinicalTrials.gov ID: NCT03244696.
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Affiliation(s)
- Heena R Manglani
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, USA
| | - Stephanie Fountain-Zaragoza
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, USA
| | - Anita Shankar
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, USA
| | | | - Ruchika Shaurya Prakash
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio, USA
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19
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Yoo K, Rosenberg MD, Kwon YH, Lin Q, Avery EW, Sheinost D, Constable RT, Chun MM. A brain-based general measure of attention. Nat Hum Behav 2022; 6:782-795. [PMID: 35241793 PMCID: PMC9232838 DOI: 10.1038/s41562-022-01301-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 01/14/2022] [Indexed: 11/15/2022]
Abstract
Attention is central to many aspects of cognition, but there is no singular neural measure of a person's overall attentional functioning across tasks. Here, using original data from 92 participants performing three different attention-demanding tasks during functional magnetic resonance imaging, we constructed a suite of whole-brain models that can predict a profile of multiple attentional components (sustained attention, divided attention and tracking, and working memory capacity) for novel individuals. Multiple brain regions across the salience, subcortical and frontoparietal networks drove accurate predictions, supporting a common (general) attention factor across tasks, distinguished from task-specific ones. Furthermore, connectome-to-connectome transformation modelling generated an individual's task-related connectomes from rest functional magnetic resonance imaging, substantially improving predictive power. Finally, combining the connectome transformation and general attention factor, we built a standardized measure that shows superior generalization across four independent datasets (total N = 495) of various attentional measures, suggesting broad utility for research and clinical applications.
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Affiliation(s)
- Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT, USA.
| | - Monica D Rosenberg
- Department of Psychology, Yale University, New Haven, CT, USA,Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Young Hye Kwon
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Qi Lin
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Emily W Avery
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Dustin Sheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA,Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA,Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Marvin M Chun
- Department of Psychology, Yale University, New Haven, CT, USA. .,Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA. .,Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA. .,Wu Tsai Institute, Yale University, New Haven, CT, USA.
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20
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Fenchel D, Dimitrova R, Robinson EC, Batalle D, Chew A, Falconer S, Kyriakopoulou V, Nosarti C, Hutter J, Christiaens D, Pietsch M, Brandon J, Hughes EJ, Allsop J, O'Keeffe C, Price AN, Cordero-Grande L, Schuh A, Makropoulos A, Passerat-Palmbach J, Bozek J, Rueckert D, Hajnal JV, McAlonan G, Edwards AD, O'Muircheartaigh J. Neonatal multi-modal cortical profiles predict 18-month developmental outcomes. Dev Cogn Neurosci 2022; 54:101103. [PMID: 35364447 PMCID: PMC8971851 DOI: 10.1016/j.dcn.2022.101103] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 02/08/2022] [Accepted: 03/23/2022] [Indexed: 12/16/2022] Open
Abstract
Developmental delays in infanthood often persist, turning into life-long difficulties, and coming at great cost for the individual and community. By examining the developing brain and its relation to developmental outcomes we can start to elucidate how the emergence of brain circuits is manifested in variability of infant motor, cognitive and behavioural capacities. In this study, we examined if cortical structural covariance at birth, indexing coordinated development, is related to later infant behaviour. We included 193 healthy term-born infants from the Developing Human Connectome Project (dHCP). An individual cortical connectivity matrix derived from morphological and microstructural features was computed for each subject (morphometric similarity networks, MSNs) and was used as input for the prediction of behavioural scores at 18 months using Connectome-Based Predictive Modeling (CPM). Neonatal MSNs successfully predicted social-emotional performance. Predictive edges were distributed between and within known functional cortical divisions with a specific important role for primary and posterior cortical regions. These results reveal that multi-modal neonatal cortical profiles showing coordinated maturation are related to developmental outcomes and that network organization at birth provides an early infrastructure for future functional skills.
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Affiliation(s)
- Daphna Fenchel
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK
| | - Ralica Dimitrova
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Emma C Robinson
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | - Dafnis Batalle
- Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK; Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Andrew Chew
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Shona Falconer
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Chiara Nosarti
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK; Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK
| | - Jana Hutter
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Daan Christiaens
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Maximilian Pietsch
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Jakki Brandon
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Emer J Hughes
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Joanna Allsop
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Camilla O'Keeffe
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Anthony N Price
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | | | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK; Institute für Artificial Intelligence and Informatics in Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Joseph V Hajnal
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Grainne McAlonan
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, London SE5 8AZ, UK
| | - A David Edwards
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Jonathan O'Muircheartaigh
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK; Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK.
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21
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Atkinson J, Braddick O, Montague-Johnson C, Baker B, Parr JR, Sullivan P, Andrew MJ. Visual attention and dietary supplementation in children with perinatal brain injury. Dev Med Child Neurol 2022; 64:340-346. [PMID: 34449080 DOI: 10.1111/dmcn.15017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 07/11/2021] [Accepted: 07/12/2021] [Indexed: 11/27/2022]
Abstract
AIM To investigate whether children with perinatal brain injury have impairments in specific components of visual attention, and whether early dietary supplementation can reduce any deficits. METHOD Children participating in the Dolphin neonatal trial of dietary supplementation were tested at age 6 months with the Infant Fixation Shift Attention Test, and at 4 to 5 years with four subtests of the Early Childhood Attention Battery (ECAB) assessing different components of attention (selective, sustained, and executive function), and the Fluid Crystallized Intelligence Index of the Kaufman Assessment Battery for Children, Second Edition (KABC-II). From 59 children originally assigned to trial groups, 33 were available for testing at 4 to 5 years (18 treatment group of whom seven, six, and five showed mild, moderate, or severe neonatal brain injury; 15 controls with one, seven, and seven in the neonatal brain injury categories respectively). Given the imbalance in numbers with mild brain injury, analysis of trial group differences is restricted to moderate and severe brain injury severities (n=25). RESULTS Children with perinatal brain injury showed poorer attention across all components relative to age norms (mean standard scores 75-87; p<0.001 for three of the four subtests), with the greatest impairment in sustained attention. These impairments remained when compared with cognitive age assessed using the Fluid Crystallized Intelligence Index. Impairment was reduced in the treatment compared to the control group (p=0.04 for flanker test, p=0.002 for counterpointing, and p=0.027 for the overall ECAB score). INTERPRETATION Perinatal brain injury is associated with later impaired attention, beyond that predicted from any general cognitive disability. Impairment varies across attention components, being most severe for sustained attention. The effects on flanker and counterpointing suggest that dietary supplementation from 0 to 2 years of age may reduce attention problems. Measuring the different components of attention is important when considering assessment and interventions for children with perinatal brain injury.
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Affiliation(s)
- Janette Atkinson
- Faculty of Brain Sciences, University College London, London, UK
| | - Oliver Braddick
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | | | - Bonny Baker
- Department of Paediatrics, University of Oxford, Oxford, UK
| | - Jeremy R Parr
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Peter Sullivan
- Department of Paediatrics, University of Oxford, Oxford, UK
| | - Morag J Andrew
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
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22
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Prado P, Birba A, Cruzat J, Santamaría-García H, Parra M, Moguilner S, Tagliazucchi E, Ibáñez A. Dementia ConnEEGtome: Towards multicentric harmonization of EEG connectivity in neurodegeneration. Int J Psychophysiol 2022; 172:24-38. [PMID: 34968581 PMCID: PMC9887537 DOI: 10.1016/j.ijpsycho.2021.12.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/26/2021] [Accepted: 12/19/2021] [Indexed: 02/02/2023]
Abstract
The proposal to use brain connectivity as a biomarker for dementia phenotyping can be potentiated by conducting large-scale multicentric studies using high-density electroencephalography (hd- EEG). Nevertheless, several barriers preclude the development of a systematic "ConnEEGtome" in dementia research. Here we review critical sources of variability in EEG connectivity studies, and provide general guidelines for multicentric protocol harmonization. We describe how results can be impacted by the choice for data acquisition, and signal processing workflows. The implementation of a particular processing pipeline is conditional upon assumptions made by researchers about the nature of EEG. Due to these assumptions, EEG connectivity metrics are typically applicable to restricted scenarios, e.g., to a particular neurocognitive disorder. "Ground truths" for the choice of processing workflow and connectivity analysis are impractical. Consequently, efforts should be directed to harmonizing experimental procedures, data acquisition, and the first steps of the preprocessing pipeline. Conducting multiple analyses of the same data and a proper integration of the results need to be considered in additional processing steps. Furthermore, instead of using a single connectivity measure, using a composite metric combining different connectivity measures brings a powerful strategy to scale up the replicability of multicentric EEG connectivity studies. These composite metrics can boost the predictive strength of diagnostic tools for dementia. Moreover, the implementation of multi-feature machine learning classification systems that include EEG-based connectivity analyses may help to exploit the potential of multicentric studies combining clinical-cognitive, molecular, genetics, and neuroimaging data towards a multi-dimensional characterization of the dementia.
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Affiliation(s)
- Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| | - Agustina Birba
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Josefina Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| | - Hernando Santamaría-García
- Pontificia Universidad Javeriana, Medical School, Physiology and Psychiatry Departments, Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
| | - Mario Parra
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, United Kingdom
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, USA,Trinity College Dublin (TCD), Dublin, Ireland
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Departamento de Física, Universidad de Buenos Aires and Instituto de Fisica de Buenos Aires (IFIBA -CONICET), Buenos Aires, Argentina
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina,Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, USA,Trinity College Dublin (TCD), Dublin, Ireland,Corresponding author at: Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile., (A. Ibáñez)
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23
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Bathelt J, Geurts HM, Borsboom D. More than the sum of its parts: Merging network psychometrics and
network neuroscience with application in autism. Netw Neurosci 2021; 6:445-466. [PMID: 35733421 PMCID: PMC9207995 DOI: 10.1162/netn_a_00222] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 12/08/2021] [Indexed: 11/05/2022] Open
Abstract
Network approaches that investigate the interaction between symptoms and behaviours have opened new ways of understanding psychological phenomena in health and disorder in recent years. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach in neuroimaging research. In this paper, we introduce a methodology for combining network psychometrics and network neuroscience. This approach utilises the information from the psychometric network to obtain neural correlates that are associated with each node in the psychometric network (network-based regression). Moreover, we combine the behavioural variables and their neural correlates in a joint network to characterise their interactions. We illustrate the approach by highlighting the interaction between the triad of autistic traits and their resting-state functional connectivity associations. To this end, we utilise data from 172 male autistic participants (10–21 years) from the autism brain data exchange (ABIDE, ABIDE-II) that completed resting-state fMRI and were assessed using the autism diagnostic interview (ADI-R). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional brain correlates. The article introduces a method to combine common practices in network psychometrics and network neuroimaging. Namely, we use the unique variance in behavioural measures as regressors to identify unique neural correlates. This enables the description of brain-level and behavioural-level data into a joint network while keeping the dimensionality of the results manageable and interpretable. We illustrate this approach by showing the network of autistic traits and their correlates in resting-state functional connectivity.
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Affiliation(s)
- Joe Bathelt
- Department of Psychology, Royal Holloway, University of London, Egham, Surrey TW20 0EX, United Kingdom
- Department of Psychology, University of Amsterdam
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24
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Predicting Fluid Intelligence via Naturalistic Functional Connectivity Using Weighted Ensemble Model and Network Analysis. NEUROSCI 2021. [DOI: 10.3390/neurosci2040032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Objectives: Functional connectivity triggered by naturalistic stimuli (e.g., movie clips), coupled with machine learning techniques provide great insight in exploring brain functions such as fluid intelligence. However, functional connectivity is multi-layered while traditional machine learning is based on individual model, which is not only limited in performance, but also fails to extract multi-dimensional and multi-layered information from the brain network. Methods: In this study, inspired by multi-layer brain network structure, we propose a new method, namely weighted ensemble model and network analysis, which combines machine learning and graph theory for improved fluid intelligence prediction. Firstly, functional connectivity analysis and graphical theory were jointly employed. The functional connectivity and graphical indices computed using the preprocessed fMRI data were then all fed into an auto-encoder parallelly for automatic feature extraction to predict the fluid intelligence. In order to improve the performance, tree regression and ridge regression models were stacked and fused automatically with weighted values. Finally, layers of auto-encoder were visualized to better illustrate the connectome patterns, followed by the evaluation of the performance to justify the mechanism of brain functions. Results: Our proposed method achieved the best performance with a 3.85 mean absolute deviation, 0.66 correlation coefficient and 0.42 R-squared coefficient; this model outperformed other state-of-the-art methods. It is also worth noting that the optimization of the biological pattern extraction was automated though the auto-encoder algorithm. Conclusion: The proposed method outperforms the state-of-the-art reports, also is able to effectively capture the biological patterns of functional connectivity during a naturalistic movie state for potential clinical explorations.
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25
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Wahlheim CN, Christensen AP, Reagh ZM, Cassidy BS. Intrinsic functional connectivity in the default mode network predicts mnemonic discrimination: A connectome-based modeling approach. Hippocampus 2021; 32:21-37. [PMID: 34821439 DOI: 10.1002/hipo.23393] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 11/09/2021] [Accepted: 11/10/2021] [Indexed: 12/31/2022]
Abstract
The ability to distinguish existing memories from similar perceptual experiences is a core feature of episodic memory. This ability is often examined using the mnemonic similarity task in which people discriminate memories of studied objects from perceptually similar lures. Studies of the neural basis of such mnemonic discrimination have mostly focused on hippocampal function and connectivity. However, default mode network (DMN) connectivity may also support such discrimination, given that the DMN includes the hippocampus, and its connectivity supports many aspects of episodic memory. Here, we used connectome-based predictive modeling to identify associations between intrinsic DMN connectivity and mnemonic discrimination. We leveraged a wide range of abilities across healthy younger and older adults to facilitate this predictive approach. Resting-state functional connectivity in the DMN predicted mnemonic discrimination outside the MRI scanner, especially among prefrontal and temporal regions and including several hippocampal regions. This predictive relationship was stronger for younger than older adults, primarily for temporal-prefrontal connectivity. The novel associations established here are consistent with mounting evidence that broader cortical networks including the hippocampus support mnemonic discrimination. They also suggest that age-related network disruptions undermine the extent that the DMN supports this ability. This study provides the first indication of how intrinsic functional properties of the DMN support mnemonic discrimination.
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Affiliation(s)
- Christopher N Wahlheim
- Department of Psychology, University of North Carolina at Greensboro, Greensboro, North Carolina, USA
| | | | - Zachariah M Reagh
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Brittany S Cassidy
- Department of Psychology, University of North Carolina at Greensboro, Greensboro, North Carolina, USA
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26
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Tian Y, Zalesky A. Machine learning prediction of cognition from functional connectivity: Are feature weights reliable? Neuroimage 2021; 245:118648. [PMID: 34673248 DOI: 10.1016/j.neuroimage.2021.118648] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 10/03/2021] [Accepted: 10/10/2021] [Indexed: 10/20/2022] Open
Abstract
Cognitive performance can be predicted from an individual's functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological processes supporting cognition. To do so, feature selection and feature weight estimation need to be reliable to ensure that important connections and circuits with high predictive utility can be reliably identified. We comprehensively investigate feature weight test-retest reliability for various predictive models of cognitive performance built from resting-state functional connectivity networks in healthy young adults (n=400). Despite achieving modest prediction accuracies (r=0.2-0.4), we find that feature weight reliability is generally poor for all predictive models (ICC< 0.3), and significantly poorer than predictive models for overt biological attributes such as sex (ICC≈0.5). Larger sample sizes (n=800), the Haufe transformation, non-sparse feature selection/regularization and smaller feature spaces marginally improve reliability (ICC< 0.4). We elucidate a tradeoff between feature weight reliability and prediction accuracy and find that univariate statistics are marginally more reliable than feature weights from predictive models. Finally, we show that measuring agreement in feature weights between cross-validation folds provides inflated estimates of feature weight reliability. We thus recommend for reliability to be estimated out-of-sample, if possible. We argue that rebalancing focus from prediction accuracy to model reliability may facilitate mechanistic understanding of cognition with machine learning approaches.
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Affiliation(s)
- Ye Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Australia; Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Australia.
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Prakash RS. Mindfulness Meditation: Impact on Attentional Control and Emotion Dysregulation. ARCHIVES OF CLINICAL NEUROPSYCHOLOGY : THE OFFICIAL JOURNAL OF THE NATIONAL ACADEMY OF NEUROPSYCHOLOGISTS 2021; 36:1283-1290. [PMID: 34651648 DOI: 10.1093/arclin/acab053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Accepted: 06/16/2021] [Indexed: 12/30/2022]
Abstract
OBJECTIVE Theoretical models of mindfulness meditation conceptualize the cultivation of focused attention and regulation of emotional states, with the attitudinal foundations that promote nonjudgment and acceptance, to facilitate cognitive and affective processing resulting in improved brain health. Within the scientific study of mindfulness meditation, outcomes assessing behavioral and neural correlates of attentional control and emotion regulation have been examined for their malleability as a function of engagement in mindfulness practices. This review synthesizes the results of our pilot trials examining the preliminary effects of mindfulness meditation on metrics of cognitive, affective, and brain health in older adults and in individuals with multiple sclerosis. CONCLUSIONS There is promising support for mindfulness meditation to enhance attentional control, reduce mind-wandering, and reduce emotion dysregulation. However, well-powered efficacy trials, with an objective assessment of mindfulness practice data are needed to further provide causal and comprehensive evidence supporting the efficacy of mindfulness meditation for brain health. Inclusion of independently derived and validated brain-based signatures of cognitive and affective functioning can additionally enable a parsimonious understanding of how mindfulness meditation can causally impact metrics of functional and structural integrity of the human brain.
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Affiliation(s)
- Ruchika Shaurya Prakash
- The Ohio State University, Department of Psychology, 1835 Neil Ave Columbus, OH 43210 USA.,The Ohio State University, Center for Cognitive and Behavioral Brain Imaging, 1835 Neil Ave Columbus, OH 43210 USA
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Wang XH, Li L. A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning. Front Genet 2021; 12:728913. [PMID: 34630522 PMCID: PMC8495194 DOI: 10.3389/fgene.2021.728913] [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: 06/22/2021] [Accepted: 08/23/2021] [Indexed: 11/13/2022] Open
Abstract
Inattention is one of the most significant clinical symptoms for evaluating attention deficit hyperactivity disorder (ADHD). Previous inattention estimations were performed using clinical scales. Recently, predictive models for inattention have been established for brain-behavior estimation using neuroimaging features. However, the performance of inattention estimation could be improved for conventional brain-behavior models with additional feature selection, machine learning algorithms, and validation procedures. This paper aimed to propose a unified framework for inattention estimation from resting state fMRI to improve the classical brain-behavior models. Phase synchrony was derived as raw features, which were selected with minimum-redundancy maximum-relevancy (mRMR) method. Six machine learning algorithms were applied as regression methods. 100 runs of 10-fold cross-validations were performed on the ADHD-200 datasets. The relevance vector machines (RVMs) based on the mRMR features for the brain-behavior models significantly improve the performance of inattention estimation. The mRMR-RVM models could achieve a total accuracy of 0.53. Furthermore, predictive patterns for inattention were discovered by the mRMR technique. We found that the bilateral subcortical-cerebellum networks exhibited the most predictive phase synchrony patterns for inattention. Together, an optimized strategy named mRMR-RVM for brain-behavior models was found for inattention estimation. The predictive patterns might help better understand the phase synchrony mechanisms for inattention.
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Affiliation(s)
- Xun-Heng Wang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
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29
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Song H, Rosenberg MD. Predicting attention across time and contexts with functional brain connectivity. Curr Opin Behav Sci 2021. [DOI: 10.1016/j.cobeha.2020.12.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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30
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Dissociable neural substrates of opioid and cocaine use identified via connectome-based modelling. Mol Psychiatry 2021; 26:4383-4393. [PMID: 31719641 PMCID: PMC7214212 DOI: 10.1038/s41380-019-0586-y] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 10/14/2019] [Accepted: 10/28/2019] [Indexed: 02/07/2023]
Abstract
Opioid use disorder is a major public health crisis. While effective treatments are available, outcomes vary widely across individuals and relapse rates remain high. Understanding neural mechanisms of treatment response may facilitate the development of personalized and/or novel treatment approaches. Methadone-maintained, polysubstance-using individuals (n = 53) participated in fMRI scanning before and after substance-use treatment. Connectome-based predictive modeling (CPM)-a recently developed, whole-brain approach-was used to identify pretreatment connections associated with abstinence during the 3-month treatment. Follow-up analyses were conducted to determine the specificity of the identified opioid abstinence network across different brain states (cognitive vs. reward task vs. resting-state) and different substance use outcomes (opioid vs. cocaine abstinence). Posttreatment fMRI data were used to assess network changes over time and within-subject replication. To determine further clinical relevance, opioid abstinence network strength was compared with healthy subjects (n = 38). CPM identified an opioid abstinence network (p = 0.018), characterized by stronger within-network motor/sensory connectivity, and reduced connectivity between the motor/sensory network and medial frontal, default mode, and frontoparietal networks. This opioid abstinence network was anatomically distinct from a previously identified cocaine abstinence network. Relationships between abstinence and opioid and cocaine abstinence networks replicated across multiple brain states but did not generalize across substances. Network connectivity measured at posttreatment related to abstinence at 6-month follow-up (p < 0.009). Healthy comparison subjects displayed intermediate network strengths relative to treatment responders and nonresponders. These data indicate dissociable anatomical substrates of opioid vs. cocaine abstinence. Results may inform the development of novel opioid-specific treatment approaches to combat the opioid epidemic.
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31
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Kwak S, Kim H, Kim H, Youm Y, Chey J. Distributed functional connectivity predicts neuropsychological test performance among older adults. Hum Brain Mapp 2021; 42:3305-3325. [PMID: 33960591 PMCID: PMC8193511 DOI: 10.1002/hbm.25436] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 01/30/2023] Open
Abstract
Neuropsychological test is an essential tool in assessing cognitive and functional changes associated with late-life neurocognitive disorders. Despite the utility of the neuropsychological test, the brain-wide neural basis of the test performance remains unclear. Using the predictive modeling approach, we aimed to identify the optimal combination of functional connectivities that predicts neuropsychological test scores of novel individuals. Resting-state functional connectivity and neuropsychological tests included in the OASIS-3 dataset (n = 428) were used to train the predictive models, and the identified models were iteratively applied to the holdout internal test set (n = 216) and external test set (KSHAP, n = 151). We found that the connectivity-based predicted score tracked the actual behavioral test scores (r = 0.08-0.44). The predictive models utilizing most of the connectivity features showed better accuracy than those composed of focal connectivity features, suggesting that its neural basis is largely distributed across multiple brain systems. The discriminant and clinical validity of the predictive models were further assessed. Our results suggest that late-life neuropsychological test performance can be formally characterized with distributed connectome-based predictive models, and further translational evidence is needed when developing theoretically valid and clinically incremental predictive models.
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Affiliation(s)
- Seyul Kwak
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
| | - Hairin Kim
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
| | - Hoyoung Kim
- Department of PsychologyChonbuk National UniversityJeonjuRepublic of Korea
| | - Yoosik Youm
- Department of SociologyYonsei UniversitySeoulRepublic of Korea
| | - Jeanyung Chey
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
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32
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Koba C, Notaro G, Tamm S, Nilsonne G, Hasson U. Spontaneous eye movements during eyes-open rest reduce resting-state-network modularity by increasing visual-sensorimotor connectivity. Netw Neurosci 2021; 5:451-476. [PMID: 34189373 PMCID: PMC8233114 DOI: 10.1162/netn_a_00186] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 01/28/2021] [Indexed: 12/21/2022] Open
Abstract
During wakeful rest, individuals make small eye movements during fixation. We examined how these endogenously driven oculomotor patterns impact topography and topology of functional brain networks. We used a dataset consisting of eyes-open resting-state (RS) fMRI data with simultaneous eye tracking. The eye-tracking data indicated minor movements during rest, which correlated modestly with RS BOLD data. However, eye-tracking data correlated well with echo-planar imaging time series sampled from the area of the eye-orbit (EO-EPI), which is a signal previously used to identify eye movements during exogenous saccades and movie viewing. Further analyses showed that EO-EPI data were correlated with activity in an extensive motor and sensorimotor network, including components of the dorsal attention network and the frontal eye fields. Partialling out variance related to EO-EPI from RS data reduced connectivity, primarily between sensorimotor and visual areas. It also produced networks with higher modularity, lower mean connectivity strength, and lower mean clustering coefficient. Our results highlight new aspects of endogenous eye movement control during wakeful rest. They show that oculomotor-related contributions form an important component of RS network topology, and that those should be considered in interpreting differences in network structure between populations or as a function of different experimental conditions. We studied how subtle eye movements made during fixation, in absence of any other task, are related to resting-state connectivity measured using fMRI. We used a dataset for which eye tracking and BOLD resting-state were acquired simultaneously. We correlated brain activity with both eye-tracking metrics as well as time series sampled from the area of the eye orbits (EO-EPI). Eye-tracking data correlated well with the EO-EPI data. Furthermore, EO-EPI correlated with BOLD signal in sensorimotor and visual brain systems. Removing variance related to EO-EPI reduced connectivity between sensorimotor and visual areas and resulted in more modular resting-state networks. Our findings show that oculomotor-related contributions are an important component of resting-state network topology, and that they can be studied using EPI data from the eye orbits.
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Affiliation(s)
- Cemal Koba
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Giuseppe Notaro
- Center for Mind/Brain Sciences (CIMeC), The University of Trento, Trento, Italy
| | - Sandra Tamm
- Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Gustav Nilsonne
- Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Uri Hasson
- Center for Mind/Brain Sciences (CIMeC), The University of Trento, Trento, Italy
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33
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Zhu Y, Zang F, Wang Q, Zhang Q, Tan C, Zhang S, Hu T, Qi L, Xu S, Ren Q, Xie C. Connectome-based model predicts episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment. Behav Brain Res 2021; 411:113387. [PMID: 34048872 DOI: 10.1016/j.bbr.2021.113387] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 04/19/2021] [Accepted: 05/22/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE To explore whether the whole brain resting-state functional connectivity (rs-FC) could predict episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment. METHOD This study included 33 cognitive normal (CN), 26 subjective cognitive decline (SCD) and 27 amnestic mild cognitive impairment (aMCI) patients, and all the participants completed resting-state fMRI (rs-fMRI) scan and neuropsychological scale test data. Connectome-based predictive modeling (CPM) based on the rs-FC data was used to predict the auditory verbal learning test-delayed recall (AVLT-DR) scores, which measured episodic memory in individuals. Pearson correlation between each brain connection in the connectivity matrices and AVLT-DR scores was computed across the patients in predementia stages of Alzheimer's disease (AD). The Pearson correlation coefficient values separated into a positive network and a negative network. Predictive networks were then defined and employed by calculating positive and negative network strengths. CPM with leave-one-out cross-validation (LOOCV) was conducted to train linear models to respectively relate positive and negative network strengths to AVLT-DR scores in the training set. During the testing procedure, each left-out testing subject's strengths of positive and negative network was normalized using the parameters acquired during training procedure, and then the trained models were used to predict the testing participant's AVLT-DR score. RESULTS The negative network predictive model tested LOOCV significantly predicted individual differences in episodic memory from rs-FC. Key nodes that brain regions contributed to the prediction model were mainly located in the prefrontal cortex, frontal cortex, parietal cortex and temporal lobe. CONCLUSION Our findings demonstrated that rs-FC among multiple neural systems could predict episodic memory at the individual level.
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Affiliation(s)
- Yao Zhu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Feifei Zang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Qianqian Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Chang Tan
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Shaoke Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Tianjian Hu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Lingyu Qi
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Shouyong Xu
- Department of Radiology, Geriatric Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210000, China.
| | - Qingguo Ren
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China.
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China.
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Barron DS, Gao S, Dadashkarimi J, Greene AS, Spann MN, Noble S, Lake EMR, Krystal JH, Constable RT, Scheinost D. Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders. Cereb Cortex 2020; 31:2523-2533. [PMID: 33345271 PMCID: PMC8023861 DOI: 10.1093/cercor/bhaa371] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 10/31/2020] [Accepted: 11/02/2020] [Indexed: 12/17/2022] Open
Abstract
Memory deficits are observed in a range of psychiatric disorders, but it is unclear whether memory deficits arise from a shared brain correlate across disorders or from various dysfunctions unique to each disorder. Connectome-based predictive modeling is a computational method that captures individual differences in functional connectomes associated with behavioral phenotypes such as memory. We used publicly available task-based functional MRI data from patients with schizophrenia (n = 33), bipolar disorder (n = 34), attention deficit hyper-activity disorder (n = 32), and healthy controls (n = 73) to model the macroscale brain networks associated with working, short- and long-term memory. First, we use 10-fold and leave-group-out analyses to demonstrate that the same macroscale brain networks subserve memory across diagnostic groups and that individual differences in memory performance are related to individual differences within networks distributed throughout the brain, including the subcortex, default mode network, limbic network, and cerebellum. Next, we show that diagnostic groups are associated with significant differences in whole-brain functional connectivity that are distinct from the predictive models of memory. Finally, we show that models trained on the transdiagnostic sample generalize to novel, healthy participants (n = 515) from the Human Connectome Project. These results suggest that despite significant differences in whole-brain patterns of functional connectivity between diagnostic groups, the core macroscale brain networks that subserve memory are shared.
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Affiliation(s)
- Daniel S Barron
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06510, USA.,Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98112, USA
| | - Siyuan Gao
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT 06520, USA
| | - Javid Dadashkarimi
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA
| | - Marisa N Spann
- Irving Medical Center, Columbia University, New York, NY 10032, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
| | - Evelyn M R Lake
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06510, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA.,Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT 06520, USA.,Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA.,Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA.,Child Study Center, Yale School of Medicine, New Haven, CT 06520, USA
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35
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Similarity in functional brain connectivity at rest predicts interpersonal closeness in the social network of an entire village. Proc Natl Acad Sci U S A 2020; 117:33149-33160. [PMID: 33318188 DOI: 10.1073/pnas.2013606117] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
People often have the intuition that they are similar to their friends, yet evidence for homophily (being friends with similar others) based on self-reported personality is inconsistent. Functional connectomes-patterns of spontaneous synchronization across the brain-are stable within individuals and predict how people tend to think and behave. Thus, they may capture interindividual variability in latent traits that are particularly similar among friends but that might elude self-report. Here, we examined interpersonal similarity in functional connectivity at rest-that is, in the absence of external stimuli-and tested if functional connectome similarity is associated with proximity in a real-world social network. The social network of a remote village was reconstructed; a subset of residents underwent functional magnetic resonance imaging. Similarity in functional connectomes was positively related to social network proximity, particularly in the default mode network. Controlling for similarities in demographic and personality data (the Big Five personality traits) yielded similar results. Thus, functional connectomes may capture latent interpersonal similarities between friends that are not fully captured by commonly used demographic or personality measures. The localization of these results suggests how friends may be particularly similar to one another. Additionally, geographic proximity moderated the relationship between neural similarity and social network proximity, suggesting that such associations are particularly strong among people who live particularly close to one another. These findings suggest that social connectivity is reflected in signatures of brain functional connectivity, consistent with the common intuition that friends share similarities that go beyond, for example, demographic similarities.
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Sui J, Jiang R, Bustillo J, Calhoun V. Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises. Biol Psychiatry 2020; 88:818-828. [PMID: 32336400 PMCID: PMC7483317 DOI: 10.1016/j.biopsych.2020.02.016] [Citation(s) in RCA: 140] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 02/13/2020] [Accepted: 02/17/2020] [Indexed: 01/08/2023]
Abstract
The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move toward a translational neuroscience era. Regression-based multivariate models (hereafter "predictive modeling") provide a powerful and widely used approach to predict human behavior with neuroimaging features. These studies maintain a focus on decoding individual differences in a continuously behavioral phenotype from neuroimaging data, opening up an exciting opportunity to describe the human brain at the single-subject level. In this survey, we provide an overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade. We first review regression-based approaches and highlight connectome-based predictive modeling, which has grown in popularity in recent years. Next, we systematically describe recent representative studies using these tools in the context of cognitive function, symptom severity, personality traits, and emotion processing. Finally, we highlight a few challenges related to combining multimodal data, longitudinal prediction, external validations, and the employment of deep learning methods that have emerged from our review of the existing literature, as well as present some promising and challenging future directions.
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Affiliation(s)
- Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia.
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia.
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Li W, Hu X, Long X, Tang L, Chen J, Wang F, Zhang D. EEG responses to emotional videos can quantitatively predict big-five personality traits. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.07.123] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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38
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Ju Y, Horien C, Chen W, Guo W, Lu X, Sun J, Dong Q, Liu B, Liu J, Yan D, Wang M, Zhang L, Guo H, Zhao F, Zhang Y, Shen X, Constable RT, Li L. Connectome-based models can predict early symptom improvement in major depressive disorder. J Affect Disord 2020; 273:442-452. [PMID: 32560939 DOI: 10.1016/j.jad.2020.04.028] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 03/24/2020] [Accepted: 04/21/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a debilitating mental illness with more than 50% of patients not achieving an adequate response using first-line treatments. Reliable models that predict antidepressant treatment outcome are needed to guide clinical decision making. We aimed to build predictive models of treatment improvement for MDD patients using machine learning approaches based on fMRI resting-state functional connectivity patterns. METHODS Resting-state fMRI data were acquired from 192 untreated MDD patients at recruitment, and their severity of depression was assessed by Hamilton Rating Scale for Depression (HAMD) at baseline. Patients were given medication after the initial MR scan and their symptoms were monitored through HAMD for a period of six months. Connectome-based predictive modeling (CPM) algorithms were implemented to predict the improvement in HAMD score at one month from resting-state connectivity at baseline. Additionally, by selectively combining the features from all leave-one-out iterations in the model building stage, we created a consensus model that could be generalized to predict improvement in HAMD score in samples of non-overlapping subjects at different time points. RESULTS Using baseline functional connectivity, CPM successfully predicted symptom improvement of depression at one month. In addition, a consensus 'MDD improvement model' could predict symptom improvement for novel individuals at the two-week, one-month, two-month and three-month time points after antidepressant treatment. CONCLUSIONS Individual pre-treatment functional brain networks contain meaningful information that can be gleaned to build predictors of treatment outcome. The identified MDD improvement networks could be an appropriate biomarker for predicting individual therapeutic response of antidepressant treatment. Replication and validation using other large datasets will be a key next step before these models can be used in clinical practice.
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Affiliation(s)
- Yumeng Ju
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA
| | - Wentao Chen
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Weilong Guo
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Xiaowen Lu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Jinrong Sun
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Qiangli Dong
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Bangshan Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Jin Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Danfeng Yan
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Mi Wang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Liang Zhang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian, Henan 463000, China
| | - Futao Zhao
- Zhumadian Psychiatric Hospital, Zhumadian, Henan 463000, China
| | - Yan Zhang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China.
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, USA; Interdepartmental Neuroscience Program, Yale School of Medicine, USA
| | - Lingjiang Li
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China.
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Wang XH, Xu J, Li L. Estimating individual scores of inattention and impulsivity based on dynamic features of intrinsic connectivity network. Neurosci Lett 2020; 724:134874. [PMID: 32114120 DOI: 10.1016/j.neulet.2020.134874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 02/18/2020] [Accepted: 02/26/2020] [Indexed: 11/30/2022]
Abstract
Inattention and impulsivity are the two most important indices for evaluations of ADHD. Currently, inattention and impulsivity were evaluated by clinical scales. The intelligent evaluation of the two indices using machine learning remains largely unexplored. This paper aimed to build regression modes for inattention and impulsivity based on resting state fMRI and additional measures, and discover the associating features for the two indices. To achieve these goals, a cohort of 95 children with ADHD as well as 105 healthy controls were selected from the ADHD-200 database. The raw features were consisted of univariate dynamic estimators of intrinsic connectivity network (ICNs), head motion, and additional measures. The regression models were solved using support vector regression (SVR). The performance of the regression models was evaluated by cross-validations. The performance of regression models based on ICNs outperformed that based on regional measures. The estimated clinical scores were significantly correlated to inattention (r = 0.4 ± 0.02, p < 0.01) and impulsivity (r = 0.31 ± 0.02, p < 0.01). The most associating ICNs are sensorimotor network (SMN) for inattention and executive control network (ECN) for impulsivity. The results suggested that inattention and impulsivity could be estimated using machine learning, and the intra-ICN dynamics could be supplementary features for regression models of clinical scores of ADHD.
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Affiliation(s)
- Xun-Heng Wang
- Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Jie Xu
- Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
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40
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Overlapping attentional networks yield divergent behavioral predictions across tasks: Neuromarkers for diffuse and focused attention? Neuroimage 2020; 209:116535. [DOI: 10.1016/j.neuroimage.2020.116535] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 01/08/2020] [Accepted: 01/09/2020] [Indexed: 01/02/2023] Open
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41
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Jiang R, Calhoun VD, Fan L, Zuo N, Jung R, Qi S, Lin D, Li J, Zhuo C, Song M, Fu Z, Jiang T, Sui J. Gender Differences in Connectome-based Predictions of Individualized Intelligence Quotient and Sub-domain Scores. Cereb Cortex 2020; 30:888-900. [PMID: 31364696 PMCID: PMC7132922 DOI: 10.1093/cercor/bhz134] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Revised: 05/08/2019] [Accepted: 05/28/2019] [Indexed: 12/15/2022] Open
Abstract
Scores on intelligence tests are strongly predictive of various important life outcomes. However, the gender discrepancy on intelligence quotient (IQ) prediction using brain imaging variables has not been studied. To this aim, we predicted individual IQ scores for males and females separately using whole-brain functional connectivity (FC). Robust predictions of intellectual capabilities were achieved across three independent data sets (680 subjects) and two intelligence measurements (IQ and fluid intelligence) using the same model within each gender. Interestingly, we found that intelligence of males and females were underpinned by different neurobiological correlates, which are consistent with their respective superiority in cognitive domains (visuospatial vs verbal ability). In addition, the identified FC patterns are uniquely predictive on IQ and its sub-domain scores only within the same gender but neither for the opposite gender nor on the IQ-irrelevant measures such as temperament traits. Moreover, females exhibit significantly higher IQ predictability than males in the discovery cohort. This findings facilitate our understanding of the biological basis of intelligence by demonstrating that intelligence is underpinned by a variety of complex neural mechanisms that engage an interacting network of regions-particularly prefrontal-parietal and basal ganglia-whereas the network pattern differs between genders.
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Affiliation(s)
- Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, 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, Atlanta, GA 30303, USA
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Rex Jung
- Department of Neurosurgery, University of New Mexico, Albuquerque, NM 87131, USA
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
| | - Dongdong Lin
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
| | - Jin Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory (PNGC-Lab), Nankai University Affiliated Anding Hospital, Tianjin Mental Health Center, Tianjin, 300222, China
| | - Ming Song
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- University of Electronic Science and Technology of China, Chengdu, 610054, China
- Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, 100190, China
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
- Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, 100190, China
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42
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Scheinost D, Hsu TW, Avery EW, Hampson M, Constable RT, Chun MM, Rosenberg MD. Connectome-based neurofeedback: A pilot study to improve sustained attention. Neuroimage 2020; 212:116684. [PMID: 32114151 DOI: 10.1016/j.neuroimage.2020.116684] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 02/23/2020] [Accepted: 02/24/2020] [Indexed: 12/27/2022] Open
Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback is a non-invasive, non-pharmacological therapeutic tool that may be useful for training behavior and alleviating clinical symptoms. Although previous work has used rt-fMRI to target brain activity in or functional connectivity between a small number of brain regions, there is growing evidence that symptoms and behavior emerge from interactions between a number of distinct brain areas. Here, we propose a new method for rt-fMRI, connectome-based neurofeedback, in which intermittent feedback is based on the strength of complex functional networks spanning hundreds of regions and thousands of functional connections. We first demonstrate the technical feasibility of calculating whole-brain functional connectivity in real-time and provide resources for implementing connectome-based neurofeedback. We next show that this approach can be used to provide accurate feedback about the strength of a previously defined connectome-based model of sustained attention, the saCPM, during task performance. Although, in our initial pilot sample, neurofeedback based on saCPM strength did not improve performance on out-of-scanner attention tasks, future work characterizing effects of network target, training duration, and amount of feedback on the efficacy of rt-fMRI can inform experimental or clinical trial designs.
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Affiliation(s)
- Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA.
| | - Tiffany W Hsu
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Emily W Avery
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Michelle Hampson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Marvin M Chun
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Monica D Rosenberg
- Department of Psychology, Yale University, New Haven, CT, USA; Department of Psychology, University of Chicago, Chicago, IL, USA.
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43
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Functional connectivity predicts changes in attention observed across minutes, days, and months. Proc Natl Acad Sci U S A 2020; 117:3797-3807. [PMID: 32019892 DOI: 10.1073/pnas.1912226117] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same patterns capture fluctuations in attention within individuals remains unclear. Here, across five independent studies, we demonstrate that the sustained attention connectome-based predictive model (CPM), a validated model of sustained attention function, generalizes to predict attentional state from data collected across minutes, days, weeks, and months. Furthermore, the sustained attention CPM is sensitive to within-subject state changes induced by propofol as well as sevoflurane, such that individuals show functional connectivity signatures of stronger attentional states when awake than when under deep sedation and light anesthesia. Together, these results demonstrate that fluctuations in attentional state reflect variability in the same functional connectivity patterns that predict individual differences in sustained attention.
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44
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Feng C, Wang L, Li T, Xu P. Connectome-based individualized prediction of loneliness. Soc Cogn Affect Neurosci 2020; 14:353-365. [PMID: 30874805 PMCID: PMC6523423 DOI: 10.1093/scan/nsz020] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 02/12/2019] [Accepted: 03/12/2019] [Indexed: 12/20/2022] Open
Abstract
Loneliness is an increasingly prevalent condition linking with enhanced morbidity and premature mortality. Despite recent proposal on medicalization of loneliness, so far no effort has been made to establish a model capable of predicting loneliness at the individual level. Here, we applied a machine-learning approach to decode loneliness from whole-brain resting-state functional connectivity (RSFC). The relationship between whole-brain RSFC and loneliness was examined in a linear predictive model. The results revealed that individual loneliness could be predicted by within- and between-network connectivity of prefrontal, limbic and temporal systems, which are involved in cognitive control, emotional processing and social perceptions and communications, respectively. Key nodes that contributed to the prediction model comprised regions previously implicated in loneliness, including the dorsolateral prefrontal cortex, lateral orbital frontal cortex, ventromedial prefrontal cortex, caudate, amygdala and temporal regions. Our findings also demonstrated that both loneliness and associated neural substrates are modulated by levels of neuroticism and extraversion. The current data-driven approach provides the first evidence on the predictive brain features of loneliness based on organizations of intrinsic brain networks. Our work represents initial efforts in the direction of making individualized prediction of loneliness that could be useful for diagnosis, prognosis and treatment.
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Affiliation(s)
- Chunliang Feng
- Guangdong Provincial Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China
| | - Li Wang
- Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Ting Li
- Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Pengfei Xu
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China.,Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China.,Department of Neuroscience, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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45
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Clarke T, Jamieson JD, Malone P, Rayhan RU, Washington S, VanMeter JW, Baraniuk JN. Connectivity differences between Gulf War Illness (GWI) phenotypes during a test of attention. PLoS One 2019; 14:e0226481. [PMID: 31891592 PMCID: PMC6938369 DOI: 10.1371/journal.pone.0226481] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2018] [Accepted: 11/26/2019] [Indexed: 01/05/2023] Open
Abstract
One quarter of veterans returning from the 1990–1991 Persian Gulf War have developed Gulf War Illness (GWI) with chronic pain, fatigue, cognitive and gastrointestinal dysfunction. Exertion leads to characteristic, delayed onset exacerbations that are not relieved by sleep. We have modeled exertional exhaustion by comparing magnetic resonance images from before and after submaximal exercise. One third of the 27 GWI participants had brain stem atrophy and developed postural tachycardia after exercise (START: Stress Test Activated Reversible Tachycardia). The remainder activated basal ganglia and anterior insulae during a cognitive task (STOPP: Stress Test Originated Phantom Perception). Here, the role of attention in cognitive dysfunction was assessed by seed region correlations during a simple 0-back stimulus matching task (“see a letter, push a button”) performed before exercise. Analysis was analogous to resting state, but different from psychophysiological interactions (PPI). The patterns of correlations between nodes in task and default networks were significantly different for START (n = 9), STOPP (n = 18) and control (n = 8) subjects. Edges shared by the 3 groups may represent co-activation caused by the 0-back task. Controls had a task network of right dorsolateral and left ventrolateral prefrontal cortex, dorsal anterior cingulate cortex, posterior insulae and frontal eye fields (dorsal attention network). START had a large task module centered on the dorsal anterior cingulate cortex with direct links to basal ganglia, anterior insulae, and right dorsolateral prefrontal cortex nodes, and through dorsal attention network (intraparietal sulci and frontal eye fields) nodes to a default module. STOPP had 2 task submodules of basal ganglia–anterior insulae, and dorsolateral prefrontal executive control regions. Dorsal attention and posterior insulae nodes were embedded in the default module and were distant from the task networks. These three unique connectivity patterns during an attention task support the concept of Gulf War Disease with recognizable, objective patterns of cognitive dysfunction.
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Affiliation(s)
- Tomas Clarke
- Center for Functional and Molecular Imaging, Georgetown University, Washington, DC, United States of America
| | - Jessie D. Jamieson
- Department of Mathematics, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America
| | - Patrick Malone
- Center for Functional and Molecular Imaging, Georgetown University, Washington, DC, United States of America
| | - Rakib U. Rayhan
- Department of Physiology and Biophysics, Howard University College of Medicine, Washington, DC, United States of America
| | - Stuart Washington
- Division of Rheumatology, Immunology and Allergy, Georgetown University, Washington, DC, United States of America
| | - John W. VanMeter
- Center for Functional and Molecular Imaging, Georgetown University, Washington, DC, United States of America
| | - James N. Baraniuk
- Division of Rheumatology, Immunology and Allergy, Georgetown University, Washington, DC, United States of America
- * E-mail:
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46
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Building functional connectivity neuromarkers of behavioral self-regulation across children with and without Autism Spectrum Disorder. Dev Cogn Neurosci 2019; 41:100747. [PMID: 31826838 PMCID: PMC6994646 DOI: 10.1016/j.dcn.2019.100747] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 11/25/2019] [Accepted: 12/03/2019] [Indexed: 01/10/2023] Open
Abstract
Behavioral self-regulation develops rapidly during childhood and struggles in this area can have lifelong negative outcomes. Challenges with self-regulation are common to several neurodevelopmental conditions, including Autism Spectrum Disorder (ASD). Little is known about the neural expression of behavioral regulation in children with and without neurodevelopmental conditions. We examined whole-brain brain functional correlations (FC) and behavioral regulation through connectome predictive modelling (CPM). CPM is a data-driven protocol for developing predictive models of brain–behavior relationships and assessing their potential as ‘neuromarkers’ using cross-validation. The data stems from the ABIDE II and comprises 276 children with and without ASD (8–13 years). We identified networks whose FC predicted individual differences in behavioral regulation. These network models predicted novel individuals’ inhibition and shifting from FC data in both a leave-one-out, and split halves, cross-validation. We observed commonalities and differences, with inhibition relying on more posterior networks, shifting relying on more anterior networks, and both involving regions of the DMN. Our findings substantially add to our knowledge on the neural expressions of inhibition and shifting across children with and without a neurodevelopmental condition. Given the numerous behavioral issues that can be quantified dimensionally, refinement of whole-brain neuromarker techniques may prove useful in the future.
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Avery EW, Yoo K, Rosenberg MD, Greene AS, Gao S, Na DL, Scheinost D, Constable TR, Chun MM. Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals. J Cogn Neurosci 2019; 32:241-255. [PMID: 31659926 DOI: 10.1162/jocn_a_01487] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Individual differences in working memory relate to performance differences in general cognitive ability. The neural bases of such individual differences, however, remain poorly understood. Here, using a data-driven technique known as connectome-based predictive modeling, we built models to predict individual working memory performance from whole-brain functional connectivity patterns. Using n-back or rest data from the Human Connectome Project, connectome-based predictive models significantly predicted novel individuals' 2-back accuracy. Model predictions also correlated with measures of fluid intelligence and, with less strength, sustained attention. Separate fluid intelligence models predicted working memory score, as did sustained attention models, again with less strength. Anatomical feature analysis revealed significant overlap between working memory and fluid intelligence models, particularly in utilization of prefrontal and parietal regions, and less overlap in predictive features between working memory and sustained attention models. Furthermore, showing the generality of these models, the working memory model developed from Human Connectome Project data generalized to predict memory in an independent data set of 157 older adults (mean age = 69 years; 48 healthy, 54 amnestic mild cognitive impairment, 55 Alzheimer disease). The present results demonstrate that distributed functional connectivity patterns predict individual variation in working memory capability across the adult life span, correlating with constructs including fluid intelligence and sustained attention.
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Affiliation(s)
| | | | | | | | | | - Duk L Na
- Samsung Medical Center, Seoul, South Korea
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48
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Salehi M, Karbasi A, Barron DS, Scheinost D, Constable RT. Individualized functional networks reconfigure with cognitive state. Neuroimage 2019; 206:116233. [PMID: 31574322 DOI: 10.1016/j.neuroimage.2019.116233] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 09/22/2019] [Accepted: 09/27/2019] [Indexed: 02/08/2023] Open
Abstract
There is extensive evidence that functional organization of the human brain varies dynamically as the brain switches between task demands, or cognitive states. This functional organization also varies across subjects, even when engaged in similar tasks. To date, the functional network organization of the brain has been considered static. In this work, we use fMRI data obtained across multiple cognitive states (task-evoked and rest conditions) and across multiple subjects, to measure state- and subject-specific functional network parcellation (the assignment of nodes to networks). Our parcellation approach provides a measure of how node-to-network assignment (NNA) changes across states and across subjects. We demonstrate that the brain's functional networks are not spatially fixed, but that many nodes change their network membership as a function of cognitive state. Such reconfigurations are highly robust and reliable to the extent that they can be used to predict cognitive state with up to 97% accuracy. Our findings suggest that if functional networks are to be defined via functional clustering of nodes, then it is essential to consider that such definitions may be fluid and cognitive-state dependent.
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Affiliation(s)
- Mehraveh Salehi
- Department of Electrical Engineering, Yale University, New Haven, CT, 06511, USA; Yale Institute for Network Science (YINS), Yale University, New Haven, CT, 06511, USA.
| | - Amin Karbasi
- Department of Electrical Engineering, Yale University, New Haven, CT, 06511, USA; Yale Institute for Network Science (YINS), Yale University, New Haven, CT, 06511, USA
| | - Daniel S Barron
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA; Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06520, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, 06520, USA
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49
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Li X, Qiao Y, Shen H, Niu Z, Shang Y, Guo H. Topological reorganization after partial auditory deprivation—a structural connectivity study in single-sided deafness. Hear Res 2019; 380:75-83. [DOI: 10.1016/j.heares.2019.05.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 05/24/2019] [Accepted: 05/30/2019] [Indexed: 12/22/2022]
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50
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Lake EMR, Finn ES, Noble SM, Vanderwal T, Shen X, Rosenberg MD, Spann MN, Chun MM, Scheinost D, Constable RT. The Functional Brain Organization of an Individual Allows Prediction of Measures of Social Abilities Transdiagnostically in Autism and Attention-Deficit/Hyperactivity Disorder. Biol Psychiatry 2019; 86:315-326. [PMID: 31010580 PMCID: PMC7311928 DOI: 10.1016/j.biopsych.2019.02.019] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 02/01/2019] [Accepted: 02/02/2019] [Indexed: 12/17/2022]
Abstract
BACKGROUND Autism spectrum disorder and attention-deficit/hyperactivity disorder (ADHD) are associated with complex changes as revealed by functional magnetic resonance imaging. To date, neuroimaging-based models are not able to characterize individuals with sufficient sensitivity and specificity. Further, although evidence shows that ADHD traits occur in individuals with autism spectrum disorder, and autism spectrum disorder traits in individuals with ADHD, the neurofunctional basis of the overlap is undefined. METHODS Using individuals from the Autism Brain Imaging Data Exchange and ADHD-200, we apply a data-driven, subject-level approach, connectome-based predictive modeling, to resting-state functional magnetic resonance imaging data to identify brain-behavior associations that are predictive of symptom severity. We examine cross-diagnostic commonalities and differences. RESULTS Using leave-one-subject-out and split-half analyses, we define networks that predict Social Responsiveness Scale, Autism Diagnostic Observation Schedule, and ADHD Rating Scale scores and confirm that these networks generalize to novel subjects. Networks share minimal overlap of edges (<2%) but some common regions of high hubness (Brodmann areas 10, 11, and 21, cerebellum, and thalamus). Further, predicted Social Responsiveness Scale scores for individuals with ADHD are linked to ADHD symptoms, supporting the hypothesis that brain organization relevant to autism spectrum disorder severity shares a component associated with attention in ADHD. Predictive connections and high-hubness regions are found within a wide range of brain areas and across conventional networks. CONCLUSIONS An individual's functional connectivity profile contains information that supports dimensional, nonbinary classification in autism spectrum disorder and ADHD. Furthermore, we can determine disorder-specific and shared neurofunctional pathology using our method.
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Affiliation(s)
- Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut.
| | - Emily S Finn
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, Maryland
| | - Stephanie M Noble
- Interdepartmental Neuroscience Program, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Tamara Vanderwal
- Yale Child Study Center, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Monica D Rosenberg
- Department of Psychology, Yale School of Medicine, Yale University, New Haven, Connecticut; Department of Psychology, University of Chicago, Chicago, Illinois
| | - Marisa N Spann
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, New York
| | - Marvin M Chun
- Interdepartmental Neuroscience Program, Yale School of Medicine, Yale University, New Haven, Connecticut; Department of Psychology, Yale School of Medicine, Yale University, New Haven, Connecticut; Department of Neurobiology, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut; Interdepartmental Neuroscience Program, Yale School of Medicine, Yale University, New Haven, Connecticut; Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, Connecticut
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