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Hermans APC, Schutter DJLG, Bethlehem RAI. Functional network characteristics in anxiety- and mania-based subgroups of bipolar I disorder. Psychiatry Res Neuroimaging 2024; 344:111868. [PMID: 39178498 DOI: 10.1016/j.pscychresns.2024.111868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 07/19/2024] [Accepted: 08/06/2024] [Indexed: 08/26/2024]
Abstract
BACKGROUND Bipolar disorder I (BD-I) is a heterogeneous disorder with a high prevalence of comorbid anxiety. The aim of this study was to investigate whether anxiety and mania symptoms define distinct subgroups within BD-I and to explore potential differences in functional network characteristics between these subgroups. METHODS Subgroups were identified using scores from clinical anxiety and mania scales. After dimension reduction of these scores, data-driven clustering analysis with cross-validation was employed to reveal the existence of subgroups. Resting-state functional magnetic resonance imaging (rs-fMRI) scans were pre-processed using fMRIPrep. After parcellation and network construction, global and regional graph theoretical measures were calculated per subgroup. RESULTS Clustering results revealed that, based on anxiety symptomatology, subjects fell into two distinct subgroups, whereas mania symptoms divided subjects into four unique subgroups. These subgroups varied notably on several symptom scales. Network assortativity was significantly associated with anxiety subgroups. Post-hoc pairwise comparisons did not reveal significant global functional network differences between the anxiety subgroups or between mania subgroups. Regional network differences between clinical subgroups were especially apparent for strength and degree in the temporal and frontal lobes. LIMITATIONS Small sample size of some subgroups is a limitation of this study as is the categorical rather than continuous representation of anxiety and mania symptoms. CONCLUSIONS BD-I populations may be stratified into robust subgroups based on anxiety and mania symptoms, showing differences in functional network connectivity. Our findings highlight new avenues of research for investigating heterogeneity in psychiatric populations.
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Affiliation(s)
- Adriana P C Hermans
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Dr. Molewaterplein 60, 3015 GJ Rotterdam, The Netherlands.
| | - Dennis J L G Schutter
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Heidelberglaan 1, 3584 CS, Utrecht, the Netherlands
| | - Richard A I Bethlehem
- Department of Psychology, University of Cambridge, Downing Site, CB2 3EB, Cambridge, UK
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2
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Wen J, Antoniades M, Yang Z, Hwang G, Skampardoni I, Wang R, Davatzikos C. Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning. Biol Psychiatry 2024; 96:564-584. [PMID: 38718880 PMCID: PMC11374488 DOI: 10.1016/j.biopsych.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/29/2024] [Accepted: 04/22/2024] [Indexed: 05/21/2024]
Abstract
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science, University of Southern California, Los Angeles, California.
| | - Mathilde Antoniades
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Gyujoon Hwang
- Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Rongguang Wang
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
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3
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DeRosa J, Friedman NP, Calhoun V, Banich MT. Neurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework. Neuroimage 2024; 299:120827. [PMID: 39245397 DOI: 10.1016/j.neuroimage.2024.120827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 08/02/2024] [Accepted: 08/31/2024] [Indexed: 09/10/2024] Open
Abstract
The current study demonstrates that an individual's resting-state functional connectivity (RSFC) is a dependable biomarker for identifying differential patterns of cognitive and emotional functioning during late childhood. Using baseline RSFC data from the Adolescent Brain Cognitive Development (ABCD) study, which includes children aged 9-11, we identified four distinct RSFC subtypes. We introduce an integrated methodological pipeline for testing the reliability and importance of these subtypes. In the Identification phase, Leiden Community Detection defined RSFC subtypes, with their reproducibility confirmed through a split-sample technique in the Validation stage. The Evaluation phase showed that distinct cognitive and mental health profiles are associated with each subtype, with the Predictive phase indicating that subtypes better predict various cognitive and mental health characteristics than individual RSFC connections. The Replication stage employed bootstrapping and down-sampling methods to substantiate the reproducibility of these subtypes further. This work allows future explorations of developmental trajectories of these RSFC subtypes.
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Affiliation(s)
- Jacob DeRosa
- Department of Psychology and Neuroscience, University of Colorado Boulder, United States; Institute of Cognitive Science, University of Colorado Boulder, United States.
| | - Naomi P Friedman
- Department of Psychology and Neuroscience, University of Colorado Boulder, United States; Institute for Behavioral Genetics, University of Colorado Boulder, United States
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, United States
| | - Marie T Banich
- Department of Psychology and Neuroscience, University of Colorado Boulder, United States; Institute of Cognitive Science, University of Colorado Boulder, United States
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4
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Cao Q, Wang P, Zhang Z, Castellanos FX, Biswal BB. Compressed cerebro-cerebellar functional gradients in children and adolescents with attention-deficit/hyperactivity disorder. Hum Brain Mapp 2024; 45:e26796. [PMID: 39254180 PMCID: PMC11386319 DOI: 10.1002/hbm.26796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 07/07/2024] [Accepted: 07/10/2024] [Indexed: 09/11/2024] Open
Abstract
Both cortical and cerebellar developmental differences have been implicated in attention-deficit/hyperactivity disorder (ADHD). Recently accumulating neuroimaging studies have highlighted hierarchies as a fundamental principle of brain organization, suggesting the importance of assessing hierarchy abnormalities in ADHD. A novel gradient-based resting-state functional connectivity analysis was applied to investigate the cerebro-cerebellar disturbed hierarchy in children and adolescents with ADHD. We found that the interaction of functional gradient between diagnosis and age was concentrated in default mode network (DMN) and visual network (VN). At the same time, we also found that the opposite gradient changes of DMN and VN caused the compression of the cortical main gradient in ADHD patients, implicating the co-occurrence of both low- (visual processing) and high-order (self-related thought) cognitive dysfunction manifesting in abnormal cerebro-cerebellar organizational hierarchy in ADHD. Our study provides a neurobiological framework to better understand the co-occurrence and interaction of both low-level and high-level functional abnormalities in the cortex and cerebellum in ADHD.
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Affiliation(s)
- Qingquan Cao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Pan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Ziqian Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - F. Xavier Castellanos
- Department of Child and Adolescent PsychiatryNew York University Grossman School of MedicineNew YorkNew YorkUSA
- Nathan Kline Institute for Psychiatric ResearchOrangeburgNew YorkUSA
| | - Bharat B. Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
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5
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Gell M, Noble S, Laumann TO, Nelson SM, Tervo-Clemmens B. Psychiatric neuroimaging designs for individualised, cohort, and population studies. Neuropsychopharmacology 2024:10.1038/s41386-024-01918-y. [PMID: 39143320 DOI: 10.1038/s41386-024-01918-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/30/2024] [Accepted: 06/11/2024] [Indexed: 08/16/2024]
Abstract
Psychiatric neuroimaging faces challenges to rigour and reproducibility that prompt reconsideration of the relative strengths and limitations of study designs. Owing to high resource demands and varying inferential goals, current designs differentially emphasise sample size, measurement breadth, and longitudinal assessments. In this overview and perspective, we provide a guide to the current landscape of psychiatric neuroimaging study designs with respect to this balance of scientific goals and resource constraints. Through a heuristic data cube contrasting key design features, we discuss a resulting trade-off among small sample, precision longitudinal studies (e.g., individualised studies and cohorts) and large sample, minimally longitudinal, population studies. Precision studies support tests of within-person mechanisms, via intervention and tracking of longitudinal course. Population studies support tests of generalisation across multifaceted individual differences. A proposed reciprocal validation model (RVM) aims to recursively leverage these complementary designs in sequence to accumulate evidence, optimise relative strengths, and build towards improved long-term clinical utility.
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Affiliation(s)
- Martin Gell
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Aachen, Germany.
- Institute of Neuroscience and Medicine (INM-7: Brain & Behaviour), Research Centre Jülich, Jülich, Germany.
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
| | - Stephanie Noble
- Psychology Department, Northeastern University, Boston, MA, USA
- Bioengineering Department, Northeastern University, Boston, MA, USA
- Center for Cognitive and Brain Health, Northeastern University, Boston, MA, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Brenden Tervo-Clemmens
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
- Institute for Translational Neuroscience, University of Minnesota, Minneapolis, MN, USA.
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Summit AG, Chen C, Pettersson E, Boersma K, D’Onofrio BM, Lichtenstein P, Quinn PD. Preliminary Validation of a General Factor Model of Chronic Overlapping Pain Conditions. THE JOURNAL OF PAIN 2024; 25:104502. [PMID: 38417595 PMCID: PMC11283990 DOI: 10.1016/j.jpain.2024.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 01/25/2024] [Accepted: 02/19/2024] [Indexed: 03/01/2024]
Abstract
Chronic overlapping pain conditions (COPCs) by definition, frequently co-occur, perhaps reflecting their shared etiologies. Their overlapping nature presents a methodological challenge, possibly masking associations between COPCs and health outcomes attributable to either general or specific processes. To address this challenge, we used population-based cohort data to evaluate the predictive validity of a bifactor model of 9 self-reported COPCs by assessing its association with incident pain-related clinical diagnoses; pain-relevant pharmacotherapy; and other health outcomes. We obtained data from a 2005 to 2006 study of Swedish adult twins linked with health data from nationwide registers through 2016 (N = 25,418). We then fit a bifactor model comprising a general COPC factor and 2 independent specific factors measuring pain-related somatic symptoms and neck and shoulder pain. Accounting for age, biological sex, and cancer, the general factor was associated with increased risk of all pain-related outcomes (eg, COPC diagnosis adjusted odds ratio [aOR], 1.71; 95% confidence interval [1.62, 1.81]), most mental health-related outcomes (eg, depression aOR, 1.72 [1.60, 1.85]), and overdose and mortality (eg, all-cause mortality aOR, 1.25 [1.09, 1.43]). The somatic symptoms specific factor was associated with pain-relevant pharmacotherapy (eg, prescribed opioids aOR, 1.25 [1.15, 1.36]), most mental health-related outcomes (eg, depression aOR, 1.95 [1.70, 2.23]), and overdose (eg, nonfatal overdose aOR, 1.66 [1.31, 2.10]). The neck and shoulder pain-specific factor was weakly and inconsistently associated with the outcomes. Findings provide initial support for the validity and utility of a general-factor model of COPCs as a tool to strengthen understanding of co-occurrence, etiology, and consequences of chronic pain. PERSPECTIVE: This article presents associations between a novel measurement model of COPCs and various health outcomes. Findings provide support for measuring pain across multiple domains rather than only measuring pain specific to one physical location in both research and clinical contexts.
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Affiliation(s)
- Alynna G. Summit
- Department of Applied Health Science, School of Public Health, Indiana University, 1025 E. 7 Street, Bloomington, IN, 47405, USA
| | - Cen Chen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Erik Pettersson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Katja Boersma
- Center for Health and Medical Psychology (CHAMP), School of Law, Psychology and Social Work, Örebro University, SE-701 82 Örebro, Sweden
| | - Brian M. D’Onofrio
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-171 77 Stockholm, Sweden
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10 Street, Bloomington, IN, 47405 USA
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Patrick D. Quinn
- Department of Applied Health Science, School of Public Health, Indiana University, 1025 E. 7 Street, Bloomington, IN, 47405, USA
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7
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Argyriou E, Gros D, Hernandez Tejada MA, Muzzy WA, Acierno R. A machine learning personalized treatment rule to optimize assignment to psychotherapies for grief among veterans. J Affect Disord 2024; 358:466-473. [PMID: 38718947 DOI: 10.1016/j.jad.2024.05.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 04/03/2024] [Accepted: 05/02/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Complex grief patterns are associated with significant suffering, functional impairments, health and mental health problems, and increased healthcare use. This burden may be even more pronounced among veterans. Behavioral Activation and Therapeutic Exposure (BATE-G) and Cognitive Therapy for Grief (CT-G) are two evidence-based interventions for grief. The goal of this study was to use a precision medicine approach to develop a personalized treatment rule to optimize assignment among these psychotherapies. METHODS We analyzed data (N = 155) from a randomized clinical trial comparing BATE-G and CT-G. Outcome weighted learning was used to estimate an optimal personalized treatment rule. Baseline characteristics including demographics, social support, variables related to the death, and psychopathology dimensions were used as prescriptive factors of treatment assignment. RESULTS The estimated rule assigned 72 veterans to CT-G and 56 to BATE-G. Assigning participants according to this rule was estimated to lead to markedly lower mean grief level following 6 months from treatment compared to assigning everyone to either BATE-G (Vdopt - VBATE-G = -18.57 [95 % CI: -29.41, -7.72]) or CT-G (Vdopt - VBATE-G = -20.89 [95 % CI: -30.7, -11.07]) regardless of their characteristics. LIMITATIONS Participants were primarily male veterans, and identified with Black or White race. The estimated rule was not externally validated. CONCLUSION The estimated rule used relatively simple, easily accessible, client characteristics to personalize assignment to treatment using a precision medicine approach based on machine learning and causal inference. Upon further validation, such a rule can be easily implemented in clinical practice to prescriptively maximize treatment benefits.
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Affiliation(s)
- Evangelia Argyriou
- Department of Psychiatry & Behavioral Sciences, Medical University of South Carolina, United States; Department of Psychology, Indiana University Indianapolis, United States
| | - Daniel Gros
- Department of Psychiatry & Behavioral Sciences, Medical University of South Carolina, United States; Mental Health Service, Ralph H. Johnson VA Healthcare System, United States.
| | - Melba A Hernandez Tejada
- Faillace Department of Psychiatry, University of Texas Health Science Center at Houston, United States
| | - Wendy A Muzzy
- Department of Psychiatry & Behavioral Sciences, Medical University of South Carolina, United States; Mental Health Service, Ralph H. Johnson VA Healthcare System, United States
| | - Ronald Acierno
- Faillace Department of Psychiatry, University of Texas Health Science Center at Houston, United States
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Ibidoja OJ, Shan FP, Ali MKM. Modified sparse regression to solve heterogeneity and hybrid models for increasing the prediction accuracy of seaweed big data with outliers. Sci Rep 2024; 14:17599. [PMID: 39080303 PMCID: PMC11289475 DOI: 10.1038/s41598-024-60612-7] [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: 01/17/2024] [Accepted: 04/25/2024] [Indexed: 08/02/2024] Open
Abstract
The linear regression is critical for data modelling, especially for scientists. Nevertheless, with the plenty of high-dimensional data, there are data with more explanatory variables than the number of observations. In such circumstances, traditional approaches fail. This paper proposes a modified sparse regression model that solves the problem of heterogeneity using seaweed big data as a use case. The modified heterogeneity models for ridge, LASSO and Elastic net were used to model the data. Robust estimations M Bi-Square, M Hampel, M Huber, MM and S were used. Based on the results, the hybrid model of sparse regression for before, after, and modified heterogeneity robust regression with the 45 high ranking variables and a 2-sigma limit can be used efficiently and effectively to reduce the outliers. The obtained results confirm that the hybrid model of the modified sparse LASSO with the M Bi-Square estimator for the 45 high ranking parameters performed better compared with other existing methods.
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Affiliation(s)
- Olayemi Joshua Ibidoja
- Department of Mathematics, Federal University Gusau, Gusau, Nigeria.
- School of Mathematical Sciences, Universiti Sains Malaysia (USM), 11800, Penang, Malaysia.
| | - Fam Pei Shan
- School of Mathematical Sciences, Universiti Sains Malaysia (USM), 11800, Penang, Malaysia
| | - Majid Khan Majahar Ali
- School of Mathematical Sciences, Universiti Sains Malaysia (USM), 11800, Penang, Malaysia.
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Kashyap R, Holla B, Bhattacharjee S, Sharma E, Mehta UM, Vaidya N, Bharath RD, Murthy P, Basu D, Nanjayya SB, Singh RL, Lourembam R, Chakrabarti A, Kartik K, Kalyanram K, Kumaran K, Krishnaveni G, Krishna M, Kuriyan R, Kurpad SS, Desrivieres S, Purushottam M, Barker G, Orfanos DP, Hickman M, Heron J, Toledano M, Schumann G, Benegal V. Childhood adversities characterize the heterogeneity in the brain pattern of individuals during neurodevelopment. Psychol Med 2024:1-13. [PMID: 38509831 DOI: 10.1017/s0033291724000710] [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] [Indexed: 03/22/2024]
Abstract
BACKGROUND Several factors shape the neurodevelopmental trajectory. A key area of focus in neurodevelopmental research is to estimate the factors that have maximal influence on the brain and can tip the balance from typical to atypical development. METHODS Utilizing a dissimilarity maximization algorithm on the dynamic mode decomposition (DMD) of the resting state functional MRI data, we classified subjects from the cVEDA neurodevelopmental cohort (n = 987, aged 6-23 years) into homogeneously patterned DMD (representing typical development in 809 subjects) and heterogeneously patterned DMD (indicative of atypical development in 178 subjects). RESULTS Significant DMD differences were primarily identified in the default mode network (DMN) regions across these groups (p < 0.05, Bonferroni corrected). While the groups were comparable in cognitive performance, the atypical group had more frequent exposure to adversities and faced higher abuses (p < 0.05, Bonferroni corrected). Upon evaluating brain-behavior correlations, we found that correlation patterns between adversity and DMN dynamic modes exhibited age-dependent variations for atypical subjects, hinting at differential utilization of the DMN due to chronic adversities. CONCLUSION Adversities (particularly abuse) maximally influence the DMN during neurodevelopment and lead to the failure in the development of a coherent DMN system. While DMN's integrity is preserved in typical development, the age-dependent variability in atypically developing individuals is contrasting. The flexibility of DMN might be a compensatory mechanism to protect an individual in an abusive environment. However, such adaptability might deprive the neural system of the faculties of normal functioning and may incur long-term effects on the psyche.
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Affiliation(s)
- Rajan Kashyap
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Bharath Holla
- Department of Integrative Medicine, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Sagarika Bhattacharjee
- Department of Neurophysiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Eesha Sharma
- Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Urvakhsh Meherwan Mehta
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Nilakshi Vaidya
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, PONS Centre, Charité Mental Health, Germany
- Department of Psychiatry, Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Rose Dawn Bharath
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Pratima Murthy
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Debashish Basu
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | | | | | - Roshan Lourembam
- Department of Psychiatry, Regional Institute of Medical Sciences, Imphal, India
| | - Amit Chakrabarti
- Division of Mental Health, ICMR-Centre for Ageing and Mental Health, Kolkata, India
| | - Kamakshi Kartik
- Rishi Valley Rural Health Centre, Madanapalle, Chittoor, India
| | | | - Kalyanaraman Kumaran
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
- MRC Lifecourse Epidemiology Unit, University of Southampton, UK
| | - Ghattu Krishnaveni
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | - Murali Krishna
- Health Equity Cluster, Institute of Public Health, Bangalore, India
| | - Rebecca Kuriyan
- Division of Nutrition, St John's Research Institute, Bengaluru, India
| | - Sunita Simon Kurpad
- Department of Psychiatry & Department of Medical Ethics, St John's Research Institute, Bengaluru, India
| | - Sylvane Desrivieres
- SGDP Centre, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | - Meera Purushottam
- Molecular Genetics Laboratory, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Gareth Barker
- Department of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | | | - Matthew Hickman
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jon Heron
- Center for Public Health, Bristol Medical School, University of Bristol, Bristol, UK
| | - Mireille Toledano
- MRC Centre for Environment and Health, School of Public Health, Imperial College, London, UK
| | - Gunter Schumann
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, PONS Centre, Charité Mental Health, Germany
- PONS Centre, Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Vivek Benegal
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
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Goldstein SP, Mwenda KM, Hoover AW, Shenkle O, Jones RN, Thomas JG. The Fully Understanding Eating and Lifestyle Behaviors (FUEL) trial: Protocol for a cohort study harnessing digital health tools to phenotype dietary non-adherence behaviors during lifestyle intervention. Digit Health 2024; 10:20552076241271783. [PMID: 39175923 PMCID: PMC11339753 DOI: 10.1177/20552076241271783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 06/25/2024] [Indexed: 08/24/2024] Open
Abstract
Objective Lifestyle intervention can produce clinically significant weight loss and reduced disease risk/severity for many individuals with overweight/obesity. Dietary lapses, instances of non-adherence to the recommended dietary goal(s) in lifestyle intervention, are associated with less weight loss and higher energy intake. There are distinct "types" of dietary lapse (e.g., eating an off-plan food, eating a larger portion), and behavioral, psychosocial, and contextual mechanisms may differ across dietary lapse types. Some lapse types also appear to impact weight more than others. Elucidating clear lapse types thus has potential for understanding and improving adherence to lifestyle intervention. Methods This 18-month observational cohort study will use real-time digital assessment tools within a multi-level factor analysis framework to uncover "lapse phenotypes" and understand their impact on clinical outcomes. Adults with overweight/obesity (n = 150) will participate in a 12-month online lifestyle intervention and 6-month weight loss maintenance period. Participants will complete 14-day lapse phenotyping assessment periods at baseline, 3, 6, 12, and 18 months in which smartphone surveys, wearable devices, and geolocation will assess dietary lapses and relevant phenotyping characteristics. Energy intake (via 24-h dietary recall) and weight will be collected at each assessment period. Results This trial is ongoing; data collection began on 31 October 2022 and is scheduled to complete by February 2027. Conclusion Results will inform novel precision tools to improve dietary adherence in lifestyle intervention, and support updated theoretical models of adherence behavior. Additionally, these phenotyping methods can likely be leveraged to better understand non-adherence to other health behavior interventions. Trial Registration This study was prospectively registered https://clinicaltrials.gov/study/NCT05562427.
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Affiliation(s)
- Stephanie P. Goldstein
- Department of Psychiatry and Human Behavior, Weight Control and Diabetes Research Center, The Miriam Hospital/Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Kevin M. Mwenda
- Spatial Structures in the Social Sciences, Population Studies and Training Center, Brown University, Providence, Rhode Island, USA
| | - Adam W. Hoover
- Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, South Carolina, USA
| | - Olivia Shenkle
- Weight Control and Diabetes Research Center, The Miriam Hospital, Providence, Rhode Island, USA
| | - Richard N. Jones
- Quantitative Science Program, Department of Psychiatry and Human Behavior, Department of Neurology, Warren Alpert Medical School, Brown University, Butler Hospital, Providence, Rhode Island, USA
| | - John Graham Thomas
- Department of Psychiatry and Human Behavior, Weight Control and Diabetes Research Center, The Miriam Hospital/Alpert Medical School of Brown University, Providence, Rhode Island, USA
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11
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Sun X, Sun J, Lu X, Dong Q, Zhang L, Wang W, Liu J, Ma Q, Wang X, Wei D, Chen Y, Liu B, Huang CC, Zheng Y, Wu Y, Chen T, Cheng Y, Xu X, Gong Q, Si T, Qiu S, Lin CP, Cheng J, Tang Y, Wang F, Qiu J, Xie P, Li L, He Y, Xia M. Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome. Biol Psychiatry 2023; 94:936-947. [PMID: 37295543 DOI: 10.1016/j.biopsych.2023.05.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/15/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a highly heterogeneous disorder that typically emerges in adolescence and can occur throughout adulthood. Studies aimed at quantitatively uncovering the heterogeneity of individual functional connectome abnormalities in MDD and identifying reproducibly distinct neurophysiological MDD subtypes across the lifespan, which could provide promising insights for precise diagnosis and treatment prediction, are still lacking. METHODS Leveraging resting-state functional magnetic resonance imaging data from 1148 patients with MDD and 1079 healthy control participants (ages 11-93), we conducted the largest multisite analysis to date for neurophysiological MDD subtyping. First, we characterized typical lifespan trajectories of functional connectivity strength based on the normative model and quantitatively mapped the heterogeneous individual deviations among patients with MDD. Then, we identified neurobiological MDD subtypes using an unsupervised clustering algorithm and evaluated intersite reproducibility. Finally, we validated the subtype differences in baseline clinical variables and longitudinal treatment predictive capacity. RESULTS Our findings indicated great intersubject heterogeneity in the spatial distribution and severity of functional connectome deviations among patients with MDD, which inspired the identification of 2 reproducible neurophysiological subtypes. Subtype 1 showed severe deviations, with positive deviations in the default mode, limbic, and subcortical areas and negative deviations in the sensorimotor and attention areas. Subtype 2 showed a moderate but converse deviation pattern. More importantly, subtype differences were observed in depressive item scores and the predictive ability of baseline deviations for antidepressant treatment outcomes. CONCLUSIONS These findings shed light on our understanding of different neurobiological mechanisms underlying the clinical heterogeneity of MDD and are essential for developing personalized treatments for this disorder.
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Affiliation(s)
- Xiaoyi Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; School of Systems Science, Beijing Normal University, Beijing, China
| | - Jinrong Sun
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 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, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou Mental Health Centre, Yangzhou, China
| | - Xiaowen Lu
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 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, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Affiliated Wuhan Mental Health Center, Huazhong University of Science and Technology, Wuhan, China
| | - Qiangli Dong
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 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, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Department of Psychiatry, Lanzhou University Second Hospital, Lanzhou, China
| | - Liang Zhang
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 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, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Mental Health Education and Counseling Center, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Wenxu Wang
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Jin Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qing Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bangshan Liu
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 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, Hunan Medical Center for Mental Health, Changsha, Hunan, China
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Yanting Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yankun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Health Commission Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Taolin Chen
- Huaxi Magnetic Resonance Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Qiyong Gong
- Huaxi Magnetic Resonance Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Tianmei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Health Commission Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Peng Xie
- Chongqing Key Laboratory of Neurobiology, Chongqing, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lingjiang Li
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 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, Hunan Medical Center for Mental Health, Changsha, Hunan, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
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12
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Pfarr JK, Meller T, Brosch K, Stein F, Thomas-Odenthal F, Evermann U, Wroblewski A, Ringwald KG, Hahn T, Meinert S, Winter A, Thiel K, Flinkenflügel K, Jansen A, Krug A, Dannlowski U, Kircher T, Gaser C, Nenadić I. Data-driven multivariate identification of gyrification patterns in a transdiagnostic patient cohort: A cluster analysis approach. Neuroimage 2023; 281:120349. [PMID: 37683808 DOI: 10.1016/j.neuroimage.2023.120349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 07/14/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Multivariate data-driven statistical approaches offer the opportunity to study multi-dimensional interdependences between a large set of biological parameters, such as high-dimensional brain imaging data. For gyrification, a putative marker of early neurodevelopment, direct comparisons of patterns among multiple psychiatric disorders and investigations of potential heterogeneity of gyrification within one disorder and a transdiagnostic characterization of neuroanatomical features are lacking. METHODS In this study we used a data-driven, multivariate statistical approach to analyze cortical gyrification in a large cohort of N = 1028 patients with major psychiatric disorders (Major depressive disorder: n = 783, bipolar disorder: n = 129, schizoaffective disorder: n = 44, schizophrenia: n = 72) to identify cluster patterns of gyrification beyond diagnostic categories. RESULTS Cluster analysis applied on gyrification data of 68 brain regions (DK-40 atlas) identified three clusters showing difference in overall (global) gyrification and minor regional variation (regions). Newly, data-driven subgroups are further discriminative in cognition and transdiagnostic disease risk factors. CONCLUSIONS Results indicate that gyrification is associated with transdiagnostic risk factors rather than diagnostic categories and further imply a more global role of gyrification related to mental health than a disorder specific one. Our findings support previous studies highlighting the importance of association cortices involved in psychopathology. Explorative, data-driven approaches like ours can help to elucidate if the brain imaging data on hand and its a priori applied grouping actually has the potential to find meaningful effects or if previous hypotheses about the phenotype as well as its grouping have to be revisited.
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Affiliation(s)
- Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Department of Psychology, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany.
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Ulrika Evermann
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Kai G Ringwald
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Germany; Institute for Translational Neuroscience, University of Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Germany
| | | | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany; Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Germany
| | - Axel Krug
- Department of Psychiatry und Psychotherapy, University Hospital Bonn, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
| | - Christian Gaser
- Department of Neurology, Jena University Hospital, Germany; Department of Psychiatry and Psychotherapy, Jena University Hospital, Germany; German Center for Mental Health (DZPG), Site Jena-Magdeburg-Halle, Germany; Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps-University Marburg, Germany
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13
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Tiego J, Verdejo-Garcia A, Anderson A, Koutoulogenis J, Bellgrove MA. Mechanisms of cognitive disinhibition explain individual differences in adult attention deficit hyperactivity disorder traits. Cortex 2023; 167:178-196. [PMID: 37567053 DOI: 10.1016/j.cortex.2023.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/12/2023] [Accepted: 06/08/2023] [Indexed: 08/13/2023]
Abstract
BACKGROUND Attention deficit hyperactivity disorder (ADHD) in adults is strongly associated with psychiatric comorbidity and functional impairment. Here, we aimed to use a newly developed online cognitive battery with strong psychometric properties for measuring individual differences in three cognitive mechanisms proposed to underlie ADHD traits in adults: 1) attentional control - the ability to mobilize cognitive resources to stop a prepotent motor response; 2) information sampling/gathering - adequate sampling of information in a stimulus detection task prior to making a decision; and 3) shifting - the ability to adapt behavior in response to positive and negative contingencies. METHODS This cross-sectional and correlational study recruited 650 adults (330 males) aged 18-69 years (M = 33.06; MD = 31.00; SD = 10.50), with previously diagnosed ADHD (n = 329) and those from the general community without a history of ADHD (n = 321). Self-report measures of ADHD traits (i.e., inattention/disorganization, impulsivity, hyperactivity) and the cognitive battery were completed online. RESULTS Latent class analysis, exploratory structural equation modeling and factor mixture modeling revealed self-reported ADHD traits formed a unidimensional and approximately normally distributed phenotype. Bayesian structural equation modeling demonstrated that all three mechanisms measured by the cognitive battery, explained unique, incremental variance in ADHD traits, with a total of 15.9% explained in the ADHD trait factor. CONCLUSIONS Attentional control and shifting, as well as the less researched cognitive process of information gathering, explain individual difference variance in self-reported ADHD traits with potential to yield genetic and neurobiological insights into adult ADHD.
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Affiliation(s)
- Jeggan Tiego
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Level 5, 18 Innovation Walk, Monash University, Clayton, Victoria, Australia 3800.
| | - Antonio Verdejo-Garcia
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Level 5, 18 Innovation Walk, Monash University, Clayton, Victoria, Australia 3800.
| | - Alexandra Anderson
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Level 5, 18 Innovation Walk, Monash University, Clayton, Victoria, Australia 3800.
| | - Julia Koutoulogenis
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Level 5, 18 Innovation Walk, Monash University, Clayton, Victoria, Australia 3800.
| | - Mark A Bellgrove
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Level 5, 18 Innovation Walk, Monash University, Clayton, Victoria, Australia 3800.
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14
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Yu G, Liu Z, Wu X, Becker B, Zhang K, Fan H, Peng S, Kuang N, Kang J, Dong G, Zhao XM, Schumann G, Feng J, Sahakian BJ, Robbins TW, Palaniyappan L, Zhang J. Common and disorder-specific cortical thickness alterations in internalizing, externalizing and thought disorders during early adolescence: an Adolescent Brain and Cognitive Development study. J Psychiatry Neurosci 2023; 48:E345-E356. [PMID: 37673436 PMCID: PMC10495167 DOI: 10.1503/jpn.220202] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 02/13/2023] [Accepted: 05/17/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND A growing body of neuroimaging studies has reported common neural abnormalities among mental disorders in adults. However, it is unclear whether the distinct disorder-specific mechanisms operate during adolescence despite the overlap among disorders. METHODS We studied a large cohort of more than 11 000 preadolescent (age 9-10 yr) children from the Adolescent Brain and Cognitive Development cohort. We adopted a regrouping approach to compare cortical thickness (CT) alterations and longitudinal changes between healthy controls (n = 4041) and externalizing (n = 1182), internalizing (n = 1959) and thought disorder (n = 347) groups. Genome-wide association study (GWAS) was performed on regional CT across 4468 unrelated European youth. RESULTS Youth with externalizing or internalizing disorders exhibited increased regional CT compared with controls. Externalizing (p = 8 × 10-4, Cohen d = 0.10) and internalizing disorders (p = 2 × 10-3, Cohen d = 0.08) shared thicker CT in the left pars opercularis. The somatosensory and the primary auditory cortex were uniquely affected in externalizing disorders, whereas the primary motor cortex and higher-order visual association areas were uniquely affected in internalizing disorders. Only youth with externalizing disorders showed decelerated cortical thinning from age 10-12 years. The GWAS found 59 genome-wide significant associated genetic variants across these regions. Cortical thickness in common regions was associated with glutamatergic neurons, while internalizing-specific regional CT was associated with astrocytes, oligodendrocyte progenitor cells and GABAergic neurons. LIMITATIONS The sample size of the GWAS was relatively small. CONCLUSION Our study provides strong evidence for the presence of specificity in CT, developmental trajectories and underlying genetic underpinnings among externalizing and internalizing disorders during early adolescence. Our results support the neurobiological validity of the regrouping approach that could supplement the use of a dimensional approach in future clinical practice.
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Affiliation(s)
- Gechang Yu
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Zhaowen Liu
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Xinran Wu
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Benjamin Becker
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Kai Zhang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Huaxin Fan
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Songjun Peng
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Nanyu Kuang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Jujiao Kang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Guiying Dong
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Xing-Ming Zhao
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Gunter Schumann
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Jianfeng Feng
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Barbara J Sahakian
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Trevor W Robbins
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Lena Palaniyappan
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Jie Zhang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
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15
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Gao CX, Dwyer D, Zhu Y, Smith CL, Du L, Filia KM, Bayer J, Menssink JM, Wang T, Bergmeir C, Wood S, Cotton SM. An overview of clustering methods with guidelines for application in mental health research. Psychiatry Res 2023; 327:115265. [PMID: 37348404 DOI: 10.1016/j.psychres.2023.115265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 05/20/2023] [Accepted: 05/21/2023] [Indexed: 06/24/2023]
Abstract
Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements. In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently introduced. How to choose algorithms to address common issues as well as methods for pre-clustering data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms, we provide information on R functions and libraries.
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Affiliation(s)
- Caroline X Gao
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia; Department of Epidemiology and Preventative Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
| | - Dominic Dwyer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Ye Zhu
- School of Information Technology, Deakin University, Geelong, VIC, Australia
| | - Catherine L Smith
- Department of Epidemiology and Preventative Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Lan Du
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Kate M Filia
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Johanna Bayer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Jana M Menssink
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Teresa Wang
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Christoph Bergmeir
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia; Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
| | - Stephen Wood
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Sue M Cotton
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
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16
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Pommy J, Conant L, Butts AM, Nencka A, Wang Y, Franczak M, Glass-Umfleet L. A graph theoretic approach to neurodegeneration: five data-driven neuropsychological subtypes in mild cognitive impairment. NEUROPSYCHOLOGY, DEVELOPMENT, AND COGNITION. SECTION B, AGING, NEUROPSYCHOLOGY AND COGNITION 2023; 30:903-922. [PMID: 36648118 DOI: 10.1080/13825585.2022.2163973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 12/26/2022] [Indexed: 01/18/2023]
Abstract
Mild cognitive Impairment (MCI) is notoriously heterogenous in terms of clinical presentation, neuroimaging correlates, and subsequent progression. Predicting who will progress to dementia, which type of dementia, and over what timeframe is challenging. Previous work has attempted to identify MCI subtypes using neuropsychological measures in an effort to address this challenge; however, there is no consensus on approach, which may account for some of the variability. Using a hierarchical community detection approach, we examined cognitive subtypes within an MCI sample (from the Alzheimer's Disease Neuroimaging Initiative [ADNI] study). We then examined whether these subtypes were related to biomarkers (e.g., cortical volumes, fluorodeoxyglucose (FDG)-positron emission tomography (PET) hypometabolism) or clinical progression. We identified five communities (i.e., cognitive subtypes) within the MCI sample: 1) predominantly memory impairment, 2) predominantly language impairment, 3) cognitively normal, 4) multidomain, with notable executive dysfunction, 5) multidomain, with notable processing speed impairment. Community membership was significantly associated with 1) cortical volume in the hippocampus, entorhinal cortex, and fusiform cortex; 2) FDG PET hypometabolism in the posterior cingulate, angular gyrus, and inferior/middle temporal gyrus; and 3) conversion to dementia at follow up. Overall, community detection as an approach appears a viable method for identifying unique cognitive subtypes in a neurodegenerative sample that were linked to several meaningful biomarkers and modestly with progression at one year follow up.
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Affiliation(s)
- Jessica Pommy
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
| | - L Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
| | - A M Butts
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
| | - A Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, United States
| | - Y Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, United States
| | - M Franczak
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
| | - L Glass-Umfleet
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
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17
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Chai Y, Sheline YI, Oathes DJ, Balderston NL, Rao H, Yu M. Functional connectomics in depression: insights into therapies. Trends Cogn Sci 2023; 27:814-832. [PMID: 37286432 PMCID: PMC10476530 DOI: 10.1016/j.tics.2023.05.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 06/09/2023]
Abstract
Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.
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Affiliation(s)
- Ya Chai
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Brain Science, Translation, Innovation and Modulation Center (brainSTIM), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hengyi Rao
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA.
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18
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Allardyce J, Cardno AG, Gordon-Smith K, Jones L, Di Florio A, Walters JTR, Holmans PA, Craddock NJ, Jones I, Owen MJ, Escott-Price V, O'Donovan MC. Specificity of polygenic signatures across symptom dimensions in bipolar disorder: an analysis of UK Bipolar Disorder Research Network data. Lancet Psychiatry 2023; 10:623-631. [PMID: 37437579 DOI: 10.1016/s2215-0366(23)00186-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 05/15/2023] [Accepted: 05/17/2023] [Indexed: 07/14/2023]
Abstract
BACKGROUND Current definitions and clinical heterogeneity in bipolar disorder are major concerns as they obstruct aetiological research and impede drug development. Therefore, stratification of bipolar disorder is a high priority. To inform stratification, our analysis aimed to examine the patterns and relationships between polygenic liability for bipolar disorder, major depressive disorder (MDD), and schizophrenia with multidimensional symptom representations of bipolar disorder. METHODS In this analysis, data from the UK Bipolar Disorder Research Network (BDRN) were assessed with the Operational Checklist for Psychotic Disorders. Individuals with bipolar disorder as defined in DSM-IV, of European ancestry (self-reported), aged 18 years or older at time of interview, living in the UK, and registered with the BDRN were eligible for inclusion. Psychopathological variables obtained via interview by trained research psychologists or psychiatrists and psychiatric case notes were used to identify statistically distinct symptom dimensions, calibrated with exploratory factor analysis and validated with confirmatory factor analysis (CFA). CFA was extended to include three polygenic risk scores (PRSs) indexing liability for bipolar disorder, MDD, and schizophrenia in a multiple indicator multiple cause (MIMIC) structural equation model to estimate PRS relationships with symptom dimensions. FINDINGS Of 4198 individuals potentially eligible for inclusion, 4148 (2804 [67·6%] female individuals and 1344 [32·4%] male individuals) with a mean age at interview of 45 years (SD 12·03) were available for analysis. Three reliable dimensions (mania, depression, and psychosis) were identified. The MIMIC model fitted the data well (root mean square error of approximation 0·021, 90% CI 0·019-0·023; comparative fit index 0·99) and suggests statistically distinct symptom dimensions also have distinct polygenic profiles. The PRS for MDD was strongly associated with the depression dimension (standardised β 0·125, 95% CI 0·080-0·171) and the PRS for schizophrenia was strongly associated with the psychosis dimension (0·108, 0·082-0·175). For the mania dimension, the PRS for bipolar disorder was weakly associated (0·050, 0·002-0·097). INTERPRETATION Our findings support the hypothesis that genetic heterogeneity underpins clinical heterogeneity, suggesting that different symptom dimensions within bipolar disorder have partly distinct causes. Furthermore, our results suggest that a specific symptom dimension has a similar cause regardless of the primary psychiatric diagnosis, supporting the use of symptom dimensions in precision psychiatry. FUNDING Wellcome Trust and UK Medical Research Council.
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Affiliation(s)
- Judith Allardyce
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK; Centre of Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
| | - Alastair G Cardno
- Leeds Institute of Health Sciences, Division of Psychological and Social Medicine, Faculty of Medicine and Health, University of Leeds, Leeds, UK
| | | | - Lisa Jones
- Psychological Medicine, University of Worcester, Worcester, UK
| | - Arianna Di Florio
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - James T R Walters
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Peter A Holmans
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Nicholas J Craddock
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Ian Jones
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Michael J Owen
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Valentina Escott-Price
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Michael C O'Donovan
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
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19
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Mattoni M, Smith DV, Olino TM. Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes. Netw Neurosci 2023; 7:787-810. [PMID: 37397889 PMCID: PMC10312268 DOI: 10.1162/netn_a_00306] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/06/2023] [Indexed: 07/10/2024] Open
Abstract
Associations between connectivity networks and behavioral outcomes such as depression are typically examined by comparing average networks between known groups. However, neural heterogeneity within groups may limit the ability to make inferences at the individual level as qualitatively distinct processes across individuals may be obscured in group averages. This study characterizes the heterogeneity of effective connectivity reward networks among 103 early adolescents and examines associations between individualized features and multiple behavioral and clinical outcomes. To characterize network heterogeneity, we used extended unified structural equation modeling to identify effective connectivity networks for each individual and an aggregate network. We found that an aggregate reward network was a poor representation of individuals, with most individual-level networks sharing less than 50% of the group-level network paths. We then used Group Iterative Multiple Model Estimation to identify a group-level network, subgroups of individuals with similar networks, and individual-level networks. We identified three subgroups that appear to reflect differences in network maturity, but this solution had modest validity. Finally, we found numerous associations between individual-specific connectivity features and behavioral reward functioning and risk for substance use disorders. We suggest that accounting for heterogeneity is necessary to use connectivity networks for inferences precise to the individual.
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Affiliation(s)
- Matthew Mattoni
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - David V. Smith
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Thomas M. Olino
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
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20
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Vik A, Kociński M, Rye I, Lundervold AJ, Lundervold AS. Functional activity level reported by an informant is an early predictor of Alzheimer's disease. BMC Geriatr 2023; 23:205. [PMID: 37003981 PMCID: PMC10067216 DOI: 10.1186/s12877-023-03849-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 02/24/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND Loss of autonomy in day-to-day functioning is one of the feared outcomes of Alzheimer's disease (AD), and relatives may have been worried by subtle behavioral changes in ordinary life situations long before these changes are given medical attention. In the present study, we ask if such subtle changes should be given weight as an early predictor of a future AD diagnosis. METHODS Longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to define a group of adults with a mild cognitive impairment (MCI) diagnosis remaining stable across several visits (sMCI, n=360; 55-91 years at baseline), and a group of adults who over time converted from having an MCI diagnosis to an AD diagnosis (cAD, n=320; 55-88 years at baseline). Eleven features were used as input in a Random Forest (RF) binary classifier (sMCI vs. cAD) model. This model was tested on an unseen holdout part of the dataset, and further explored by three different permutation-driven importance estimates and a comprehensive post hoc machine learning exploration. RESULTS The results consistently showed that measures of daily life functioning, verbal memory function, and a volume measure of hippocampus were the most important predictors of conversion from an MCI to an AD diagnosis. Results from the RF classification model showed a prediction accuracy of around 70% in the test set. Importantly, the post hoc analyses showed that even subtle changes in everyday functioning noticed by a close informant put MCI patients at increased risk for being on a path toward the major cognitive impairment of an AD diagnosis. CONCLUSION The results showed that even subtle changes in everyday functioning should be noticed when reported by relatives in a clinical evaluation of patients with MCI. Information of these changes should also be included in future longitudinal studies to investigate different pathways from normal cognitive aging to the cognitive decline characterizing different stages of AD and other neurodegenerative disorders.
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Affiliation(s)
- Alexandra Vik
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Marek Kociński
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ingrid Rye
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Astri J Lundervold
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Alexander S Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway.
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21
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Katahira K. Evaluating the predictive performance of subtyping: A criterion for cluster mean-based prediction. Stat Med 2023; 42:1045-1065. [PMID: 36646466 DOI: 10.1002/sim.9656] [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: 04/14/2022] [Revised: 11/21/2022] [Accepted: 01/04/2023] [Indexed: 01/18/2023]
Abstract
Heterogeneity is a frequent issue in population data analyses in medicine, biology, and the social sciences. A common approach for handling heterogeneity is to use a clustering algorithm to group similar samples, considering samples within the same group to be homogeneous. This approach is known as "subtyping" or "subgrouping." Methods for evaluating the validity of subtyping have yet to be fully established. In this study, we propose the cost of cluster mean-based prediction (CCMP) as a metric for evaluating the accuracy of predictions based on subtyping. By selecting the minimum CCMP among several candidate clustering results, the optimal subtype classification in terms of prediction accuracy can be determined. The computational implementation of the CCMP is validated with numerical experiments. We also examine some properties of subtype classification selected by CCMP.
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Affiliation(s)
- Kentaro Katahira
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
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22
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Soda T, Ahmadi A, Tani J, Honda M, Hanakawa T, Yamashita Y. Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy. Front Psychiatry 2023; 14:1080668. [PMID: 37009124 PMCID: PMC10050443 DOI: 10.3389/fpsyt.2023.1080668] [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: 10/26/2022] [Accepted: 02/21/2023] [Indexed: 03/17/2023] Open
Abstract
Introduction Investigating the pathological mechanisms of developmental disorders is a challenge because the symptoms are a result of complex and dynamic factors such as neural networks, cognitive behavior, environment, and developmental learning. Recently, computational methods have started to provide a unified framework for understanding developmental disorders, enabling us to describe the interactions among those multiple factors underlying symptoms. However, this approach is still limited because most studies to date have focused on cross-sectional task performance and lacked the perspectives of developmental learning. Here, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as in silico neurodevelopment framework for atypical representation learning. Methods Simple simulation experiments were conducted using the proposed framework to examine whether manipulating the neural stochasticity and noise levels in external environments during the learning process can lead to the altered acquisition of hierarchical Bayesian representation and reduced flexibility. Results Networks with normal neural stochasticity acquired hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good behavioral and cognitive flexibility. When the neural stochasticity was high during learning, top-down generation using higher-order representation became atypical, although the flexibility did not differ from that of the normal stochasticity settings. However, when the neural stochasticity was low in the learning process, the networks demonstrated reduced flexibility and altered hierarchical representation. Notably, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli. Discussion These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment.
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Affiliation(s)
- Takafumi Soda
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
- Department of NCNP Brain Physiology and Pathology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | | | - Jun Tani
- Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Manabu Honda
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Takashi Hanakawa
- Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yuichi Yamashita
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
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23
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Chen J, Patil KR, Yeo BTT, Eickhoff SB. Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research. Biol Psychiatry 2023; 93:18-28. [PMID: 36307328 DOI: 10.1016/j.biopsych.2022.07.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/06/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022]
Abstract
Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.
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Affiliation(s)
- Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China; Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences & Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
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24
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Wu X, Yu G, Zhang K, Feng J, Zhang J, Sahakian BJ, Robbins TW. Symptom-Based Profiling and Multimodal Neuroimaging of a Large Preteenage Population Identifies Distinct Obsessive-Compulsive Disorder-like Subtypes With Neurocognitive Differences. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:1078-1089. [PMID: 34224907 DOI: 10.1016/j.bpsc.2021.06.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/06/2021] [Accepted: 06/21/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND Obsessive-compulsive disorder (OCD) is characterized by both internalizing (anxiety) and externalizing (compulsivity) symptoms. Currently, little is known about their interrelationships and their relative contributions to disease heterogeneity. Our goal is to resolve affective and cognitive symptom heterogeneity related to internalized and externalized symptom dimensions by determining subtypes of children with OCD symptoms, and to identify any corresponding neural differences. METHODS A total of 1269 children with OCD symptoms screened using the Child Behavior Checklist Obsessive-Compulsive Symptom scale and 3987 matched control subjects were obtained from the Adolescent Brain Cognitive Development (ABCD) Study. Consensus hierarchical clustering was used to cluster children with OCD symptoms into distinct subtypes. Ten neurocognitive task scores and 20 Child Behavior Checklist syndrome scales were used to characterize cognitive/behavioral differences. Gray matter volume, fractional anisotropy of major white matter fiber tracts, and functional connectivity among networks were used in case-control studies. RESULTS We identified two subgroups with contrasting patterns in internalized and externalized dimensions. Group 1 showed compulsive thoughts and repeated acts but relatively low anxiety symptoms, whereas group 2 exhibited higher anxiety and perfectionism and relatively low repetitive behavior. Only group 1 had significant cognitive impairments and gray matter volume reductions in the bilateral inferior parietal lobe, precentral gyrus, and precuneus gyrus, and had white matter tract fractional anisotropy reductions in the corticostriatal fasciculus. CONCLUSIONS Children with OCD symptoms are heterogeneous at the level of symptom clustering and its underlying neural basis. Two subgroups represent distinct patterns of externalizing and internalizing symptoms, suggesting that anxiety is not its major predisposing factor. These results may have implications for the nosology and treatment of preteenage OCD.
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Affiliation(s)
- Xinran Wu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Gechang Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Kai Zhang
- School of Computer Science and Technology, East China Normal University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Shanghai Center for Mathematical Sciences, Shanghai, China; Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
| | - Barbara J Sahakian
- Departments of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
| | - Trevor W Robbins
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Department of Psychology, University of Cambridge, Cambridge, United Kingdom; Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
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25
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Heterogeneity in caregiving-related early adversity: Creating stable dimensions and subtypes. Dev Psychopathol 2022; 34:621-634. [PMID: 35314012 PMCID: PMC9492894 DOI: 10.1017/s0954579421001668] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Early psychosocial adversities exist at many levels, including caregiving-related, extrafamilial, and sociodemographic, which despite their high interrelatedness may have unique impacts on development. In this paper, we focus on caregiving-related early adversities (crEAs) and parse the heterogeneity of crEAs via data reduction techniques that identify experiential cooccurrences. Using network science, we characterized crEA cooccurrences to represent the comorbidity of crEA experiences across a sample of school-age children (n = 258; 6-12 years old) with a history of crEAs. crEA dimensions (variable level) and crEA subtypes (subject level) were identified using parallel factor analysis/principal component analysis and graph-based Louvain community detection. Bagging enhancement with cross-validation provided estimates of robustness. These data-driven dimensions/subtypes showed evidence of stability, transcended traditional sociolegally defined groups, were more homogenous than sociolegally defined groups, and reduced statistical correlations with sociodemographic factors. Finally, random forests showed both unique and common predictive importance of the crEA dimensions/subtypes for childhood mental health symptoms and academic skills. These data-driven outcomes provide additional tools and recommendations for crEA data reduction to inform precision medicine efforts in this area.
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26
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Zhao K, Zheng Q, Dyrba M, Rittman T, Li A, Che T, Chen P, Sun Y, Kang X, Li Q, Liu B, Liu Y, Li S. Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2104538. [PMID: 35098696 PMCID: PMC9036024 DOI: 10.1002/advs.202104538] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/30/2021] [Indexed: 05/28/2023]
Abstract
Individuals with mild cognitive impairment (MCI) of different subtypes show distinct alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by employing a regional radiomics similarity network (R2SN). The second aim is to characterize the abnormality patterns associated with the clinical manifestations of each subtype. An individual-level R2SN is constructed for N = 605 normal controls (NCs), N = 766 MCI patients, and N = 283 Alzheimer's disease (AD) patients. MCI patients' R2SN profiles are clustered into two subtypes using nonnegative matrix factorization. The patterns of brain alterations, gene expression, and the risk of cognitive decline in each subtype are evaluated. MCI patients are clustered into "similar to the pattern of NCs" (N-CI, N = 252) and "similar to the pattern of AD" (A-CI, N = 514) subgroups. Significant differences are observed between the subtypes with respect to the following: 1) clinical measures; 2) multimodal neuroimaging; 3) the proportion of progression to dementia (61.54% for A-CI and 21.77% for N-CI) within three years; 4) enriched genes for potassium-ion transport and synaptic transmission. Stratification into the two subtypes provides new insight for risk assessment and precise early intervention for MCI patients.
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Affiliation(s)
- Kun Zhao
- Beijing Advanced Innovation Centre for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100191China
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing100876China
| | - Qiang Zheng
- School of Computer and Control EngineeringYantai UniversityYantai264005China
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE)Rostock18147Germany
| | - Timothy Rittman
- Department of Clinical NeurosciencesUniversity of CambridgeCambridge Biomedical CampusCambridgeCB2 0SZUK
| | - Ang Li
- State Key Laboratory of Brain and Cognitive Science, Institute of BiophysicsChinese Academy of SciencesBeijing100101China
| | - Tongtong Che
- Beijing Advanced Innovation Centre for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100191China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing100049China
| | - Yuqing Sun
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing100049China
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing100049China
| | - Qiongling Li
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijing100875China
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijing100875China
| | - Yong Liu
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing100876China
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Shuyu Li
- Beijing Advanced Innovation Centre for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100191China
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijing100875China
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27
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Dutt RK, Hannon K, Easley TO, Griffis JC, Zhang W, Bijsterbosch JD. Mental health in the UK Biobank: A roadmap to self-report measures and neuroimaging correlates. Hum Brain Mapp 2022; 43:816-832. [PMID: 34708477 PMCID: PMC8720192 DOI: 10.1002/hbm.25690] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 09/10/2021] [Accepted: 10/11/2021] [Indexed: 11/09/2022] Open
Abstract
The UK Biobank (UKB) is a highly promising dataset for brain biomarker research into population mental health due to its unprecedented sample size and extensive phenotypic, imaging, and biological measurements. In this study, we aimed to provide a shared foundation for UKB neuroimaging research into mental health with a focus on anxiety and depression. We compared UKB self-report measures and revealed important timing effects between scan acquisition and separate online acquisition of some mental health measures. To overcome these timing effects, we introduced and validated the Recent Depressive Symptoms (RDS-4) score which we recommend for state-dependent and longitudinal research in the UKB. We furthermore tested univariate and multivariate associations between brain imaging-derived phenotypes (IDPs) and mental health. Our results showed a significant multivariate relationship between IDPs and mental health, which was replicable. Conversely, effect sizes for individual IDPs were small. Test-retest reliability of IDPs was stronger for measures of brain structure than for measures of brain function. Taken together, these results provide benchmarks and guidelines for future UKB research into brain biomarkers of mental health.
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Affiliation(s)
- Rosie K Dutt
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Kayla Hannon
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Ty O Easley
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Joseph C Griffis
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Wei Zhang
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Janine D Bijsterbosch
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
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28
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Bondy E, Bogdan R. Understanding Anhedonia from a Genomic Perspective. Curr Top Behav Neurosci 2022; 58:61-79. [PMID: 35152374 PMCID: PMC9375777 DOI: 10.1007/7854_2021_293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Anhedonia, or the decreased ability to experience pleasure, is a cardinal symptom of major depression that commonly occurs within other forms of psychopathology. Supportive of long-held theory that anhedonia represents a genetically influenced vulnerability marker for depression, evidence from twin studies suggests that it is moderately-largely heritable. However, the genomic sources of this heritability are just beginning to be understood. In this review, we survey what is known about the genomic architecture underlying anhedonia and related constructs. We briefly review twin and initial candidate gene studies before focusing on genome-wide association study (GWAS) and polygenic efforts. As large samples are needed to reliably detect the small effects that typically characterize common genetic variants, the study of anhedonia and related phenotypes conflicts with current genomic research requirements and frameworks that prioritize sample size over precise phenotyping. This has resulted in few and underpowered studies of anhedonia-related constructs that have largely failed to reliably identify individual variants. Nonetheless, the polygenic architecture of anhedonia-related constructs identified in these studies has genetic overlap with depression and schizophrenia as well as related brain structure (e.g., striatal volume), providing important clues to etiology that may usefully guide refinement in nosology. As we await the accumulation of larger samples for more well-powered GWAS of reward-related constructs, novel analytic techniques that leverage GWAS summary statistics (e.g., genomic structural equation modeling) may currently be used to help characterize how the genomic architecture of anhedonia is shared and distinct from that underlying other constructs (e.g., depression, neuroticism, anxiety).
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Affiliation(s)
- Erin Bondy
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, St. Louis, MO, USA
| | - Ryan Bogdan
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, St. Louis, MO, USA.
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29
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Knott R, Johnson BP, Tiego J, Mellahn O, Finlay A, Kallady K, Kouspos M, Mohanakumar Sindhu VP, Hawi Z, Arnatkeviciute A, Chau T, Maron D, Mercieca EC, Furley K, Harris K, Williams K, Ure A, Fornito A, Gray K, Coghill D, Nicholson A, Phung D, Loth E, Mason L, Murphy D, Buitelaar J, Bellgrove MA. The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project design and methodologies: a dimensional approach to understanding neurobiological and genetic aetiology. Mol Autism 2021; 12:55. [PMID: 34353377 PMCID: PMC8340366 DOI: 10.1186/s13229-021-00457-3] [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: 02/03/2021] [Accepted: 07/05/2021] [Indexed: 11/20/2022] Open
Abstract
Background ASD and ADHD are prevalent neurodevelopmental disorders that frequently co-occur and have strong evidence for a degree of shared genetic aetiology. Behavioural and neurocognitive heterogeneity in ASD and ADHD has hampered attempts to map the underlying genetics and neurobiology, predict intervention response, and improve diagnostic accuracy. Moving away from categorical conceptualisations of psychopathology to a dimensional approach is anticipated to facilitate discovery of data-driven clusters and enhance our understanding of the neurobiological and genetic aetiology of these conditions. The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project is one of the first large-scale, family-based studies to take a truly transdiagnostic approach to ASD and ADHD. Using a comprehensive phenotyping protocol capturing dimensional traits central to ASD and ADHD, the MAGNET project aims to identify data-driven clusters across ADHD-ASD spectra using deep phenotyping of symptoms and behaviours; investigate the degree of familiality for different dimensional ASD-ADHD phenotypes and clusters; and map the neurocognitive, brain imaging, and genetic correlates of these data-driven symptom-based clusters. Methods The MAGNET project will recruit 1,200 families with children who are either typically developing, or who display elevated ASD, ADHD, or ASD-ADHD traits, in addition to affected and unaffected biological siblings of probands, and parents. All children will be comprehensively phenotyped for behavioural symptoms, comorbidities, neurocognitive and neuroimaging traits and genetics. Conclusion The MAGNET project will be the first large-scale family study to take a transdiagnostic approach to ASD-ADHD, utilising deep phenotyping across behavioural, neurocognitive, brain imaging and genetic measures. Supplementary Information The online version contains supplementary material available at 10.1186/s13229-021-00457-3.
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Affiliation(s)
- Rachael Knott
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia.
| | - Beth P Johnson
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Jeggan Tiego
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Olivia Mellahn
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Amy Finlay
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Kathryn Kallady
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Maria Kouspos
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Vishnu Priya Mohanakumar Sindhu
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Ziarih Hawi
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Aurina Arnatkeviciute
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Tracey Chau
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Dalia Maron
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Emily-Clare Mercieca
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Kirsten Furley
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Katrina Harris
- Department of Paediatrics, Monash University, Melbourne, VIC, 3800, Australia.,Department of Developmental Paediatrics, Monash Children's Hospital, 246 Clayton Rd, Clayton, VIC, 3168, Australia
| | - Katrina Williams
- Department of Paediatrics, Monash University, Melbourne, VIC, 3800, Australia.,Department of Developmental Paediatrics, Monash Children's Hospital, 246 Clayton Rd, Clayton, VIC, 3168, Australia.,Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, Australia.,Department of Paediatrics, Faculty of Medicine, Dentistry and Health Sciences, Royal Children's Hospital, 50 Flemington Road, Parkville, VIC, 3052, Australia
| | - Alexandra Ure
- Department of Paediatrics, Monash University, Melbourne, VIC, 3800, Australia.,Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, Australia.,Department of Mental Health, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, Australia.,Neurodevelopment and Disability Research, Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Kylie Gray
- Centre for Educational Development, Appraisal, and Research, University of Warwick, Coventry, CV4 7AL, UK.,Department of Psychiatry, School of Clinical Sciences, Monash University, 246 Clayton Rd, Melbourne, VIC, 3168, Australia
| | - David Coghill
- Department of Paediatrics, Faculty of Medicine, Dentistry and Health Sciences, Royal Children's Hospital, 50 Flemington Road, Parkville, VIC, 3052, Australia.,Department of Mental Health, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, Australia.,Neurodevelopment and Disability Research, Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, Australia
| | - Ann Nicholson
- Faculty of Information and Technology, Monash University, Melbourne, VIC, 3800, Australia
| | - Dinh Phung
- Faculty of Information and Technology, Monash University, Melbourne, VIC, 3800, Australia
| | - Eva Loth
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Luke Mason
- Centre for Brain and Cognitive Development, Birkbeck, University of London, Henry Welcome Building, Malet Street, London, WC1E 7HX, UK
| | - Declan Murphy
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Jan Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Kapittelweg 29, 6525 EN, Nijmegen, The Netherlands
| | - Mark A Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
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30
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Setoyama D, Matsushima T, Hayakawa K, Nakao T, Kanba S, Kang D, Kato TA. Blood metabolic signatures of hikikomori, pathological social withdrawal. DIALOGUES IN CLINICAL NEUROSCIENCE 2021; 23:14-28. [PMID: 35860171 PMCID: PMC9286746 DOI: 10.1080/19585969.2022.2046978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background A severe form of pathological social withdrawal, ‘hikikomori,’ has been acknowledged in Japan, spreading worldwide, and becoming a global health issue. The pathophysiology of hikikomori has not been clarified, and its biological traits remain unexplored. Methods Drug-free patients with hikikomori (n = 42) and healthy controls (n = 41) were recruited. Psychological assessments for the severity of hikikomori and depression were conducted. Blood biochemical tests and plasma metabolome analysis were performed. Based on the integrated information, machine-learning models were created to discriminate cases of hikikomori from healthy controls, predict hikikomori severity, stratify the cases, and identify metabolic signatures that contribute to each model. Results Long-chain acylcarnitine levels were remarkably higher in patients with hikikomori; bilirubin, arginine, ornithine, and serum arginase were significantly different in male patients with hikikomori. The discriminative random forest model was highly performant, exhibiting an area under the ROC curve of 0.854 (confidential interval = 0.648–1.000). To predict hikikomori severity, a partial least squares PLS-regression model was successfully created with high linearity and practical accuracy. In addition, blood serum uric acid and plasma cholesterol esters contributed to the stratification of cases. Conclusions These findings reveal the blood metabolic signatures of hikikomori, which are key to elucidating the pathophysiology of hikikomori and also useful as an index for monitoring the treatment course for rehabilitation.
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Affiliation(s)
- Daiki Setoyama
- Department of Clinical Chemistry and Laboratory Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Toshio Matsushima
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kohei Hayakawa
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Shigenobu Kanba
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Dongchon Kang
- Department of Clinical Chemistry and Laboratory Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Takahiro A. Kato
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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31
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Cheng TW, Mills KL, Miranda Dominguez O, Zeithamova D, Perrone A, Sturgeon D, Feldstein Ewing SW, Fisher PA, Pfeifer JH, Fair DA, Mackiewicz Seghete KL. Characterizing the impact of adversity, abuse, and neglect on adolescent amygdala resting-state functional connectivity. Dev Cogn Neurosci 2020; 47:100894. [PMID: 33385788 PMCID: PMC7786040 DOI: 10.1016/j.dcn.2020.100894] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 10/27/2020] [Accepted: 12/01/2020] [Indexed: 01/07/2023] Open
Abstract
Characterizing typologies of childhood adversity may inform the development of risk profiles and corresponding interventions aimed at mitigating its lifelong consequences. A neurobiological grounding of these typologies requires systematic comparisons of neural structure and function among individuals with different exposure histories. Using seed-to-whole brain analyses, this study examined associations between childhood adversity and amygdala resting-state functional connectivity (rs-fc) in adolescents aged 11–19 years across three independent studies (N = 223; 127 adversity group) in both general and dimensional models of adversity (comparing abuse and neglect). In a general model, adversity was associated with altered amygdala rs-fc with clusters within the left anterior lateral prefrontal cortex. In a dimensional model, abuse was associated with altered amygdala rs-fc within the orbitofrontal cortex, dorsal precuneus, posterior cingulate cortex, and dorsal anterior cingulate cortex/anterior mid-cingulate cortex, as well as within the dorsal attention, visual, and somatomotor networks. Neglect was associated with altered amygdala rs-fc with the hippocampus, supplementary motor cortex, temporoparietal junction, and regions within the dorsal attention network. Both general and dimensional models revealed unique regions, potentially reflecting pathways by which distinct histories of adversity may influence adolescent behavior, cognition, and psychopathology.
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Affiliation(s)
- Theresa W Cheng
- Department of Psychology, University of Oregon, Eugene, OR, United States.
| | - Kathryn L Mills
- Department of Psychology, University of Oregon, Eugene, OR, United States
| | - Oscar Miranda Dominguez
- Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, United States
| | - Dagmar Zeithamova
- Department of Psychology, University of Oregon, Eugene, OR, United States
| | - Anders Perrone
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, United States
| | - Darrick Sturgeon
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, United States
| | | | - Philip A Fisher
- Department of Psychology, University of Oregon, Eugene, OR, United States
| | - Jennifer H Pfeifer
- Department of Psychology, University of Oregon, Eugene, OR, United States
| | - Damien A Fair
- Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, United States; Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, United States
| | - Kristen L Mackiewicz Seghete
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, United States; Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States
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32
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Rutherford S. The Promise of Machine Learning for Psychiatry. Biol Psychiatry 2020; 88:e53-e55. [PMID: 33153529 DOI: 10.1016/j.biopsych.2020.08.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 08/27/2020] [Indexed: 11/15/2022]
Affiliation(s)
- Saige Rutherford
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, and the Donders Institute, Radboud University Medical Center, Nijmegen, the Netherlands.
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33
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Parkes L, Satterthwaite TD, Bassett DS. Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment. Curr Opin Neurobiol 2020; 65:120-128. [PMID: 33242721 PMCID: PMC7770086 DOI: 10.1016/j.conb.2020.10.016] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 10/11/2020] [Accepted: 10/14/2020] [Indexed: 02/01/2023]
Abstract
Searching for biomarkers has been a chief pursuit of the field of psychiatry. Toward this end, studies have catalogued candidate resting-state biomarkers in nearly all forms of mental disorder. However, it is becoming increasingly clear that these biomarkers lack specificity, limiting their capacity to yield clinical impact. We discuss three avenues of research that are overcoming this limitation: (i) the adoption of transdiagnostic research designs, which involve studying and explicitly comparing multiple disorders from distinct diagnostic axes of psychiatry; (ii) dimensional models of psychopathology that map the full spectrum of symptomatology and that cut across traditional disorder boundaries; and (iii) modeling individuals' unique functional connectomes throughout development. We provide a framework for tying these subfields together that draws on tools from machine learning and network science.
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Affiliation(s)
- Linden Parkes
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA.
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Satterthwaite TD, Feczko E, Kaczkurkin AN, Fair DA. Parsing Psychiatric Heterogeneity Through Common and Unique Circuit-Level Deficits. Biol Psychiatry 2020; 88:4-5. [PMID: 32553194 PMCID: PMC8220658 DOI: 10.1016/j.biopsych.2020.04.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 04/20/2020] [Indexed: 12/13/2022]
Affiliation(s)
- Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Eric Feczko
- Departments of Behavioral Neuroscience, Psychiatry, and Advanced Imaging Research Center, Oregon Health and Science University, Portland, Oregon
| | | | - Damien A. Fair
- Departments of Behavioral Neuroscience, Psychiatry, and Advanced Imaging Research Center, Oregon Health and Science University, Portland, Oregon
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Krueger RF, DeYoung CG. The Indispensable Value of a Coherent Phenotypic Model of Psychopathology. Biol Psychiatry 2020; 88:6-8. [PMID: 32553195 PMCID: PMC10089251 DOI: 10.1016/j.biopsych.2020.04.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 04/09/2020] [Indexed: 11/24/2022]
Affiliation(s)
- Robert F Krueger
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota.
| | - Colin G DeYoung
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota
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