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Perrault AA, Kebets V, Kuek NMY, Cross NE, Tesfaye R, Pomares FB, Li J, Chee MW, Dang-Vu TT, Yeo BT. A multidimensional investigation of sleep and biopsychosocial profiles with associated neural signatures. RESEARCH SQUARE 2024:rs.3.rs-4078779. [PMID: 38659875 PMCID: PMC11042395 DOI: 10.21203/rs.3.rs-4078779/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.
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Affiliation(s)
- Aurore A. Perrault
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ilede-Montréal, QC, Canada
- Sleep & Circadian Research Group, Woolcock Institute of Medical Research, Macquarie University, Sydney, NSW, Australia
| | - Valeria Kebets
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
- McGill University, Montreal, QC, Canada
| | - Nicole M. Y. Kuek
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Nathan E. Cross
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ilede-Montréal, QC, Canada
- School of Psychology, University of Sydney, NSW, Australia
| | | | - Florence B. Pomares
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ilede-Montréal, QC, Canada
| | - Jingwei Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Center Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
| | - Michael W.L. Chee
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Thien Thanh Dang-Vu
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ilede-Montréal, QC, Canada
| | - B.T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachussetts General Hospital, Charlestown, MA, USA
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Huang X, Gao L, Xiao J, Li L, Shan X, Chen H, Chai X, Duan X. Family Environment Modulates Linkage of Transdiagnostic Psychiatric Phenotypes and Dissociable Brain Features in the Developing Brain. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00081-8. [PMID: 38537777 DOI: 10.1016/j.bpsc.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 02/26/2024] [Accepted: 03/16/2024] [Indexed: 06/04/2024]
Abstract
BACKGROUND Family environment has long been known for shaping brain function and psychiatric phenotypes, especially during childhood and adolescence. Accumulating neuroimaging evidence suggests that across different psychiatric disorders, common phenotypes may share common neural bases, indicating latent brain-behavior relationships beyond diagnostic categories. However, the influence of family environment on the brain-behavior relationship from a transdiagnostic perspective remains unknown. METHODS We included a community-based sample of 699 participants (ages 5-22 years) and applied partial least squares regression analysis to determine latent brain-behavior relationships from whole-brain functional connectivity and comprehensive phenotypic measures. Comparisons were made between diagnostic and nondiagnostic groups to help interpret the latent brain-behavior relationships. A moderation model was introduced to examine the potential moderating role of family factors in the estimated brain-behavior associations. RESULTS Four significant latent brain-behavior pairs were identified that reflected the relationship of dissociable brain network and general behavioral problems, cognitive and language skills, externalizing problems, and social dysfunction, respectively. The group comparisons exhibited interpretable variations across different diagnostic groups. A warm family environment was found to moderate the brain-behavior relationship of core symptoms in internalizing disorders. However, in neurodevelopmental disorders, family factors were not found to moderate the brain-behavior relationship of core symptoms, but they were found to affect the brain-behavior relationship in other domains. CONCLUSIONS Our findings leveraged a transdiagnostic analysis to investigate the moderating effects of family factors on brain-behavior associations, emphasizing the different roles that family factors play during this developmental period across distinct diagnostic groups.
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Affiliation(s)
- Xinyue Huang
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Leying Gao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Jinming Xiao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Lei Li
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xiaolong Shan
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Huafu Chen
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xiaoqian Chai
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.
| | - Xujun Duan
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
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3
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Perrault AA, Kebets V, Kuek NMY, Cross NE, Tesfaye R, Pomares FB, Li J, Chee MW, Dang-Vu TT, Thomas Yeo B. A multidimensional investigation of sleep and biopsychosocialprofiles with associated neural signatures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.15.580583. [PMID: 38559143 PMCID: PMC10979931 DOI: 10.1101/2024.02.15.580583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.
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Affiliation(s)
- Aurore A. Perrault
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ile-de-Montréal, QC, Canada
- Sleep & Circadian Research Group, Woolcock Institute of Medical Research, Macquarie University, Sydney, NSW, Australia
| | - Valeria Kebets
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
- McGill University, Montreal, QC, Canada
| | - Nicole M. Y. Kuek
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Nathan E. Cross
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ile-de-Montréal, QC, Canada
- School of Psychology, University of Sydney, NSW, Australia
| | | | - Florence B. Pomares
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ile-de-Montréal, QC, Canada
| | - Jingwei Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Center Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
| | - Michael W.L. Chee
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Thien Thanh Dang-Vu
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l’Ile-de-Montréal, QC, Canada
| | - B.T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachussetts General Hospital, Charlestown, MA, USA
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Shen X, Zhou X, Liao HP, McDonnell D, Wang JL. Uncovering the symptom relationship between anxiety, depression, and internet addiction among left-behind children: A large-scale purposive sampling network analysis. J Psychiatr Res 2024; 171:43-51. [PMID: 38244332 DOI: 10.1016/j.jpsychires.2024.01.025] [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: 04/26/2023] [Revised: 11/02/2023] [Accepted: 01/15/2024] [Indexed: 01/22/2024]
Abstract
Facing long-term separation from their parents, left-behind children are at risk of the co-occurrence of internalizing and externalizing problems. Although previous research has gained substantial information examining the relationship between anxiety, depression, and internet addiction at the aggregate level of variables, little is known about the heterogeneity and interactions between these components at the symptom level with a large-scale purposive sample. Adopting the network approach, two network pathways, depression and anxiety, and associations between these variables and internet addiction were constructed. Our sample included 5367 left-behind children (Mage = 13.57; SDage = 1.37; 50.07% females). Relevant bridging, central symptoms, and network stability were identified. Two relatively stable networks were obtained. For the network of anxiety and depression, sleep problems and tachycardia were vital bridging symptoms. Central symptoms, including tachycardia, restlessness, fatigue, and emptiness, were symptoms of depression. For the network of symptoms of anxiety, depression, and internet addiction, the bridging symptoms remained the same, and the central symptoms included tachycardia, restlessness, loss of control, and emptiness. By identifying relevant bridging and central symptoms, those with higher levels of these symptoms could be regarded as intervention targets, providing a reference for the current issue of valuing diagnosis over prevention in left-behind children.
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Affiliation(s)
- Xi Shen
- Center for Mental Health Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xinqi Zhou
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Hai-Ping Liao
- Center for Mental Health Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Dean McDonnell
- Department of Humanities, South East Technological University, Carlow, R93 V960, Ireland
| | - Jin-Liang Wang
- Center for Mental Health Education, Faculty of Psychology, Southwest University, Chongqing, China.
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5
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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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6
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Ko C, Kang S, Hong SB, Park YR. Protocol for the development of joint attention-based subclassification of autism spectrum disorder and validation using multi-modal data. BMC Psychiatry 2023; 23:589. [PMID: 37582781 PMCID: PMC10426216 DOI: 10.1186/s12888-023-04978-4] [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: 06/02/2023] [Accepted: 06/22/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND Heterogeneity in clinical manifestation and underlying neuro-biological mechanisms are major obstacles to providing personalized interventions for individuals with autism spectrum disorder (ASD). Despite various efforts to unify disparate data modalities and machine learning techniques for subclassification, replicable ASD clusters remain elusive. Our study aims to introduce a novel method, utilizing the objective behavioral biomarker of gaze patterns during joint attention, to subclassify ASD. We will assess whether behavior-based subgrouping yields clinically, genetically, and neurologically distinct ASD groups. METHODS We propose a study involving 60 individuals with ASD recruited from a specialized psychiatric clinic to perform joint attention tasks. Through the examination of gaze patterns in social contexts, we will conduct a semi-supervised clustering analysis, yielding two primary clusters: good gaze response group and poor gaze response group. Subsequent comparison will occur across these clusters, scrutinizing neuroanatomical structure and connectivity using structural as well as functional brain imaging studies, genetic predisposition through single nucleotide polymorphism data, and assorted socio-demographic and clinical information. CONCLUSIONS The aim of the study is to investigate the discriminative properties and the validity of the joint attention-based subclassification of ASD using multi-modality data. TRIAL REGISTRATION Clinical trial, KCT0008530, Registered 16 June 2023, https://cris.nih.go.kr/cris/index/index.do .
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Affiliation(s)
- Chanyoung Ko
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
| | - Soyeon Kang
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University Hospital, Seoul, South Korea
| | - Soon-Beom Hong
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University Hospital, Seoul, South Korea.
- Department of Psychiatry and Institute of Human Behavioral Medicine, Seoul National University College of Medicine, Seoul, South Korea.
| | - Yu Rang Park
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea.
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7
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Moreau CA, Kumar K, Harvey A, Huguet G, Urchs SGW, Schultz LM, Sharmarke H, Jizi K, Martin CO, Younis N, Tamer P, Martineau JL, Orban P, Silva AI, Hall J, van den Bree MBM, Owen MJ, Linden DEJ, Lippé S, Bearden CE, Almasy L, Glahn DC, Thompson PM, Bourgeron T, Bellec P, Jacquemont S. Brain functional connectivity mirrors genetic pleiotropy in psychiatric conditions. Brain 2023; 146:1686-1696. [PMID: 36059063 PMCID: PMC10319760 DOI: 10.1093/brain/awac315] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 07/13/2022] [Accepted: 08/11/2022] [Indexed: 02/03/2023] Open
Abstract
Pleiotropy occurs when a genetic variant influences more than one trait. This is a key property of the genomic architecture of psychiatric disorders and has been observed for rare and common genomic variants. It is reasonable to hypothesize that the microscale genetic overlap (pleiotropy) across psychiatric conditions and cognitive traits may lead to similar overlaps at the macroscale brain level such as large-scale brain functional networks. We took advantage of brain connectivity, measured by resting-state functional MRI to measure the effects of pleiotropy on large-scale brain networks, a putative step from genes to behaviour. We processed nine resting-state functional MRI datasets including 32 726 individuals and computed connectome-wide profiles of seven neuropsychiatric copy-number-variants, five polygenic scores, neuroticism and fluid intelligence as well as four idiopathic psychiatric conditions. Nine out of 19 pairs of conditions and traits showed significant functional connectivity correlations (rFunctional connectivity), which could be explained by previously published levels of genomic (rGenetic) and transcriptomic (rTranscriptomic) correlations with moderate to high concordance: rGenetic-rFunctional connectivity = 0.71 [0.40-0.87] and rTranscriptomic-rFunctional connectivity = 0.83 [0.52; 0.94]. Extending this analysis to functional connectivity profiles associated with rare and common genetic risk showed that 30 out of 136 pairs of connectivity profiles were correlated above chance. These similarities between genetic risks and psychiatric disorders at the connectivity level were mainly driven by the overconnectivity of the thalamus and the somatomotor networks. Our findings suggest a substantial genetic component for shared connectivity profiles across conditions and traits, opening avenues to delineate general mechanisms-amenable to intervention-across psychiatric conditions and genetic risks.
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Affiliation(s)
- Clara A Moreau
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université Paris Cité, Paris, France
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
| | - Kuldeep Kumar
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Annabelle Harvey
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
| | - Guillaume Huguet
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Sebastian G W Urchs
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Laura M Schultz
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Hanad Sharmarke
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
| | - Khadije Jizi
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | | | - Nadine Younis
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Petra Tamer
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Jean-Louis Martineau
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Pierre Orban
- Centre de Recherche de l’Institut Universitaire en Santé Mentale de Montréal, UdeM, Montréal, QC H1N 3V2, Canada
- Département de Psychiatrie et d’Addictologie, Université de Montréal, Pavillon Roger-Gaudry, C.P. 6128, Succursale Centre-ville, Montréal, QC H3C 3J7, Canada
| | - Ana Isabel Silva
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Jeremy Hall
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Marianne B M van den Bree
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Michael J Owen
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - David E J Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Sarah Lippé
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Carrie E Bearden
- Integrative Center for Neurogenetics, Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA 90095, USA
- Department of Psychiatry, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Biobehavioral Sciences and Psychology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Laura Almasy
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - David C Glahn
- Department of Psychiatry, Harvard Medical School, Cambridge, MA 02115, USA
- Boston Children’s Hospital, Tommy Fuss Center for Neuropsychiatric Disease Research, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, CA, USA
| | - Thomas Bourgeron
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université Paris Cité, Paris, France
| | - Pierre Bellec
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
| | - Sebastien Jacquemont
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
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8
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Drossel G, Brucar LR, Rawls E, Hendrickson TJ, Zilverstand A. Subtypes in addiction and their neurobehavioral profiles across three functional domains. Transl Psychiatry 2023; 13:127. [PMID: 37072391 PMCID: PMC10113211 DOI: 10.1038/s41398-023-02426-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/28/2023] [Accepted: 03/31/2023] [Indexed: 04/20/2023] Open
Abstract
Rates of return to use in addiction treatment remain high. We argue that the development of improved treatment options will require advanced understanding of individual heterogeneity in Substance Use Disorders (SUDs). We hypothesized that considerable individual differences exist in the three functional domains underlying addiction-approach-related behavior, executive function, and negative emotionality. We included N = 593 participants from the enhanced Nathan Kline Institute-Rockland Sample community sample (ages 18-59, 67% female) that included N = 420 Controls and N = 173 with past SUDs [54% female; N = 75 Alcohol Use Disorder (AUD) only, N = 30 Cannabis Use Disorder (CUD) only, and N = 68 Multiple SUDs]. To test our a priori hypothesis that distinct neuro-behavioral subtypes exist within individuals with past SUDs, we conducted a latent profile analysis with all available phenotypic data as input (74 subscales from 18 measures), and then characterized resting-state brain function for each discovered subtype. Three subtypes with distinct neurobehavioral profiles were recovered (p < 0.05, Cohen's D: 0.4-2.8): a "Reward type" with higher approach-related behavior (N = 69); a "Cognitive type" with lower executive function (N = 70); and a "Relief type" with high negative emotionality (N = 34). For those in the Reward type, substance use mapped onto resting-state connectivity in the Value/Reward, Ventral-Frontoparietal and Salience networks; for the Cognitive type in the Auditory, Parietal Association, Frontoparietal and Salience networks; and for the Relief type in the Parietal Association, Higher Visual and Salience networks (pFDR < 0.05). Subtypes were equally distributed amongst individuals with different primary SUDs (χ2 = 4.71, p = 0.32) and gender (χ2 = 3.44, p = 0.18). Results support functionally derived subtypes, demonstrating considerable individual heterogeneity in the multi-dimensional impairments in addiction. This confirms the need for mechanism-based subtyping to inform the development of personalized addiction medicine approaches.
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Affiliation(s)
- Gunner Drossel
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Leyla R Brucar
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Eric Rawls
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Hendrickson
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Anna Zilverstand
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
- Medical Discovery Team on Addiction, University of Minnesota, Minneapolis, MN, USA.
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9
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Brucar LR, Feczko E, Fair DA, Zilverstand A. Current Approaches in Computational Psychiatry for the Data-Driven Identification of Brain-Based Subtypes. Biol Psychiatry 2023; 93:704-716. [PMID: 36841702 PMCID: PMC10038896 DOI: 10.1016/j.biopsych.2022.12.020] [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: 08/15/2022] [Revised: 12/15/2022] [Accepted: 12/19/2022] [Indexed: 12/31/2022]
Abstract
The ability of our current psychiatric nosology to accurately delineate clinical populations and inform effective treatment plans has reached a critical point with only moderately successful interventions and high relapse rates. These challenges continue to motivate the search for approaches to better stratify clinical populations into more homogeneous delineations, to better inform diagnosis and disease evaluation, and prescribe and develop more precise treatment plans. The promise of brain-based subtyping based on neuroimaging data is that finding subgroups of individuals with a common biological signature will facilitate the development of biologically grounded, targeted treatments. This review provides a snapshot of the current state of the field in empirical brain-based subtyping studies in child, adolescent, and adult psychiatric populations published between 2019 and March 2022. We found that there is vast methodological exploration and a surprising number of new methods being created for the specific purpose of brain-based subtyping. However, this methodological exploration and advancement is not being met with rigorous validation approaches that assess both reproducibility and clinical utility of the discovered brain-based subtypes. We also found evidence for a collaboration crisis, in which methodological exploration and advancements are not clearly grounded in clinical goals. We propose several steps that we believe are crucial to address these shortcomings in the field. We conclude, and agree with the authors of the reviewed studies, that the discovery of biologically grounded subtypes would be a significant advancement for treatment development in psychiatry.
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Affiliation(s)
- Leyla R Brucar
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, Minnesota; Department of Pediatrics, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, Minnesota; Department of Pediatrics, University of Minnesota Medical School, Minneapolis, Minnesota; Institute of Child Development, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Anna Zilverstand
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, Minnesota; Medical Discovery Team on Addiction, University of Minnesota Medical School, Minneapolis, Minnesota.
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10
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More than the aggregation of its components: Unveiling the associations between anxiety, depression, and suicidal behavior in adolescents from a network perspective. J Affect Disord 2023; 326:66-72. [PMID: 36708958 DOI: 10.1016/j.jad.2023.01.081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 01/15/2023] [Accepted: 01/21/2023] [Indexed: 01/27/2023]
Abstract
BACKGROUND Facing multiple changes, adolescents are prone to have anxiety and depression concurrently, which would accompany a particularly high risk for suicide. However, most previous studies have ignored the heterogeneity of the components and used latent variable methods to explore the associations between these core variables, resulting in a lack of component-level discussions. METHOD Using a large sample of 9300 adolescents (Meanage = 13.51; SDage = 1.33; 49.82 % females), two network pathways of anxiety and depression and the associations between these variables and suicidal behavior were constructed. The central components and the stability of both networks were also identified. RESULTS Considering the network of anxiety and depression, there were two strong bridging symptoms of sleep problems and palpitation or tachycardia. The symptoms of depression showed a more vital centrality than anxiety, and the central symptoms were tachycardia, worthlessness, fatigue, and feeling of choking. For the network of suicidal behavior and symptoms of anxiety and depression, besides sleep problems, the edge linking lifetime suicide ideation and attempt and the frequency of suicide ideation in the past year was also a strong edge. Worthlessness connected symptoms of anxiety and depression with suicidal behavior. The central components were tachycardia, worthlessness, the frequency of suicidal ideation over the past year, and fatigue. Additionally, both networks had higher stability in terms of edge and centrality. CONCLUSION Based on the identified relevant strong bridging and central components, effective therapies would target these components first, which would lead to the alleviating effects on other components.
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11
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Dugré JR, Potvin S. Clarifying the role of Cortico-Cortical and Amygdalo-Cortical brain dysconnectivity associated with Conduct Problems. Neuroimage Clin 2023; 37:103346. [PMID: 36791489 PMCID: PMC9958059 DOI: 10.1016/j.nicl.2023.103346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 01/14/2023] [Accepted: 02/05/2023] [Indexed: 02/10/2023]
Abstract
A recent meta-analysis of resting-state functional connectivity studies revealed that individuals exhibiting antisocial behaviors or conduct problems may show disrupted brain connectivity in networks underpinning socio-affective and attentional processes. However, studies included in the meta-analysis generally rely on small sample sizes and substantially differ in terms of psychometric scales and neuroimaging methodologies. Therefore, we aimed to identify reliable functional brain connectivity alterations associated with severity of conduct problems using a large sample of adolescents and two measures of conduct problems. In a sample of 1416 children and adolescents, mass-univariate analyses of connectivity measures between 333 cortical parcels were conducted to examine the relationship between resting-state functional cortical-cortical connectome and the severity of conduct problems using the Child Behavior Checklist (CBCL) and the Strengths and Difficulties Questionnaire (SDQ). At a liberal threshold, results showed that the functional brain connectivity significantly associated with conduct problems largely differ between the two scales. Indeed, only 21 pairs of brain regions overlapped between the CBCL and SDQ. Permutation feature importance of these 21 brain connectivity measures revealed that connectivity between precentral/postcentral gyri and lateral prefrontal cortex (both ventral and dorsal) were the most important features in explaining variance in conduct problems. The current study highlights that psychometric measures may yield distinct functional connectivity results. Moreover, severity of conduct problems in children and adolescents was mainly associated with deficient functional connectivity of somatomotor and ventral attention networks indicating potential alterations in motor, cognitive and reward processes.
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Affiliation(s)
- Jules R Dugré
- Research Center of the Institut Universitaire en Santé Mentale de Montréal, Montreal, Canada; Department of Psychiatry and Addictology, Faculty of Medicine, University of Montreal, Montreal, Canada.
| | - Stéphane Potvin
- Research Center of the Institut Universitaire en Santé Mentale de Montréal, Montreal, Canada; Department of Psychiatry and Addictology, Faculty of Medicine, University of Montreal, Montreal, Canada.
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12
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Luo L, You W, DelBello MP, Gong Q, Li F. Recent advances in psychoradiology. Phys Med Biol 2022; 67. [PMID: 36279868 DOI: 10.1088/1361-6560/ac9d1e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/24/2022] [Indexed: 11/24/2022]
Abstract
Abstract
Psychiatry, as a field, lacks objective markers for diagnosis, progression, treatment planning, and prognosis, in part due to difficulties studying the brain in vivo, and diagnoses are based on self-reported symptoms and observation of patient behavior and cognition. Rapid advances in brain imaging techniques allow clinical investigators to noninvasively quantify brain features at the structural, functional, and molecular levels. Psychoradiology is an emerging discipline at the intersection of psychiatry and radiology. Psychoradiology applies medical imaging technologies to psychiatry and promises not only to improve insight into structural and functional brain abnormalities in patients with psychiatric disorders but also to have potential clinical utility. We searched for representative studies related to recent advances in psychoradiology through May 1, 2022, and conducted a selective review of 165 references, including 75 research articles. We summarize the novel dynamic imaging processing methods to model brain networks and present imaging genetics studies that reveal the relationship between various neuroimaging endophenotypes and genetic markers in psychiatric disorders. Furthermore, we survey recent advances in psychoradiology, with a focus on future psychiatric diagnostic approaches with dimensional analysis and a shift from group-level to individualized analysis. Finally, we examine the application of machine learning in psychoradiology studies and the potential of a novel option for brain stimulation treatment based on psychoradiological findings in precision medicine. Here, we provide a summary of recent advances in psychoradiology research, and we hope this review will help guide the practice of psychoradiology in the scientific and clinical fields.
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13
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Athreya AP, Vande Voort JL, Shekunov J, Rackley SJ, Leffler JM, McKean AJ, Romanowicz M, Kennard BD, Emslie GJ, Mayes T, Trivedi M, Wang L, Weinshilboum RM, Bobo WV, Croarkin PE. Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants. J Child Psychol Psychiatry 2022; 63:1347-1358. [PMID: 35288932 PMCID: PMC9475486 DOI: 10.1111/jcpp.13580] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/17/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. METHODS The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. RESULTS Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. CONCLUSIONS PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression.
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Affiliation(s)
- Arjun P. Athreya
- Department of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMNUSA
| | | | - Julia Shekunov
- Department of Psychiatry and PsychologyMayo ClinicRochesterMNUSA
| | | | | | | | | | - Betsy D. Kennard
- Peter O’Donnell Jr. Brain Institute and the Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Graham J. Emslie
- Peter O’Donnell Jr. Brain Institute and the Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasTXUSA,Children’s HealthChildren’s Medical CenterDallasTXUSA
| | - Taryn Mayes
- Peter O’Donnell Jr. Brain Institute and the Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Madhukar Trivedi
- Peter O’Donnell Jr. Brain Institute and the Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMNUSA
| | | | - William V. Bobo
- Department of Psychiatry and PsychologyMayo ClinicJacksonvilleFLUSA
| | - Paul E. Croarkin
- Department of Psychiatry and PsychologyMayo ClinicRochesterMNUSA
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14
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Ribeiro AP, Piquet-Pessôa M, Félix-da-Silva C, Mühlbauer JFE, de-Salles-Andrade JB, Fontenelle LF. Subjective assessments of research domain criteria constructs in addiction and compulsive disorders: a scoping review protocol. BMJ Open 2022; 12:e059232. [PMID: 36028270 PMCID: PMC9422856 DOI: 10.1136/bmjopen-2021-059232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION Obsessive-compulsive and related disorders (OCRDs) and disorders due to addictive behaviours (DABs) are prevalent conditions that share behavioural and neurobiological characteristics. The Research Domain Criteria lists a series of constructs whose dysfunctions may be present in both groups of disorders. The present study will describe the research protocol of a scoping review of the literature on self-report scales and questionnaires that tap dysfunctional constructs that underlie OCRDs and DABs. METHODS AND ANALYSIS This protocol outlines a scoping review on self-report tools and questionnaires that assess OCRDs and DABs-related constructs. The scoping review will select sources in MEDLINE, EMBASE, PsychINFO and Web of Science databases. Inclusion and exclusion criteria will be designed according to the Population, Concept, Context, Types of source framework. Two reviewers will screen independently titles, abstracts and full texts to determine the eligibility of articles. A methodological framework including six stages steps ((1) identifying a research question; (2) identifying relevant studies; (3) study selection; (4) charting the data; (5) collating, summarising and reporting the result) will be used, and the findings will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. Information extracted will be collated, and quantitative results will be presented using descriptive statistics such as percentages, tables, charts and flow diagrams as appropriate. ETHICS AND DISSEMINATION Ethical approval for conducting this scoping review is not required, as this study will involve secondary analysis of existing literature. The researchers will disseminate the study results via conference presentations and publication in a peer-reviewed journal. SCOPING REVIEW PROTOCOL REGISTRATION DOI 10.17605/OSF.IO/UJ7G5.
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Affiliation(s)
- Ana Paula Ribeiro
- Obsessive, Compulsive, and Anxiety Spectrum Research Program, Institute of Psychiatry of the Federal University of Rio de Janeiro (IPUB/UFRJ) and D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Marcelo Piquet-Pessôa
- Obsessive, Compulsive, and Anxiety Spectrum Research Program, Institute of Psychiatry of the Federal University of Rio de Janeiro (IPUB/UFRJ) and D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Carina Félix-da-Silva
- Obsessive, Compulsive, and Anxiety Spectrum Research Program, Institute of Psychiatry of the Federal University of Rio de Janeiro (IPUB/UFRJ) and D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Julia Fernandes Eigenheer Mühlbauer
- Obsessive, Compulsive, and Anxiety Spectrum Research Program, Institute of Psychiatry of the Federal University of Rio de Janeiro (IPUB/UFRJ) and D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Juliana B de-Salles-Andrade
- Obsessive, Compulsive, and Anxiety Spectrum Research Program, Institute of Psychiatry of the Federal University of Rio de Janeiro (IPUB/UFRJ) and D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Leonardo F Fontenelle
- Obsessive, Compulsive, and Anxiety Spectrum Research Program, Institute of Psychiatry of the Federal University of Rio de Janeiro (IPUB/UFRJ) and D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
- Department of Psychiatry, Monash University School of Clinical Sciences at Monash Health, Clayton, Victoria, Australia
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15
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Donnelly NA, Perry BI, Jones HJ, Khandaker GM. Childhood immuno-metabolic markers and risk of depression and psychosis in adulthood: A prospective birth cohort study. Psychoneuroendocrinology 2022; 139:105707. [PMID: 35286909 DOI: 10.1016/j.psyneuen.2022.105707] [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: 10/29/2021] [Revised: 02/25/2022] [Accepted: 02/25/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Metabolic and inflammatory disorders commonly co-occur with depression and psychosis, with emerging evidence implicating immuno-metabolic dysfunction in their aetiology. Previous studies have reported metabolic dysfunction and inflammation in adults with depression and psychosis. However, longitudinal studies testing the direction of association, and the effects of different dimensions of early-life immuno-metabolic dysfunction on adult psychopathology are limited. METHODS Using data from 3258 birth cohort participants we examined longitudinal associations of three metabolic hormones (leptin, adiponectin, insulin) at age 9 with risks for depression- and psychosis-spectrum outcomes at age 24. In addition, using nine immuno-metabolic biomarkers (leptin, adiponectin, insulin, interleukin-6, C-Reactive protein, low density lipoprotein, high density lipoprotein, triglycerides, and BMI), we constructed an exploratory bifactor model showing a general immuno-metabolic factor and three specific factors (adiposity, inflammation, and insulin resistance), which were also used as exposures. RESULTS Childhood leptin was associated with adult depressive episode (adjusted odds ratio (aOR)= 1.31; 95% CI, 1.02-1.71) and negative symptoms (aOR=1.15; 95% CI, 1.07-1.24), but not positive psychotic symptoms. The general immuno-metabolic factor was associated with atypical depressive symptoms (aOR=1.07; 95% CI, 1.01-1.14) and psychotic experiences (aOR=1.21; 95% CI, 1.02-1.44). The adiposity factor was associated with negative symptoms (aOR=1.07; 95% CI 1.02-1.12). Point estimates tended to be larger in women, though 95% credible intervals overlapped with those for men. In women, the inflammatory factor was associated with depressive episodes (aOR=1.27; 95% CI, 1.03-1.57). CONCLUSIONS While general immuno-metabolic dysfunction in childhood may contribute to risks for both psychotic and depressive symptoms in adulthood, childhood adiposity and inflammation appear to be particularly linked to affective (depressive and negative), but not positive psychotic symptoms.
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Affiliation(s)
- N A Donnelly
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Avon and Wiltshire Mental Health Partnership NHS Trust, UK.
| | - B I Perry
- Department of Psychiatry, University of Cambridge, UK; Cambridgeshire and Peterborough NHS Foundation Trust, UK
| | - H J Jones
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit, University of Bristol, UK
| | - G M Khandaker
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Avon and Wiltshire Mental Health Partnership NHS Trust, UK; Department of Psychiatry, University of Cambridge, UK; Cambridgeshire and Peterborough NHS Foundation Trust, UK; NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit, University of Bristol, UK
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16
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Dugré JR, Eickhoff SB, Potvin S. Meta-analytical transdiagnostic neural correlates in common pediatric psychiatric disorders. Sci Rep 2022; 12:4909. [PMID: 35318371 PMCID: PMC8941086 DOI: 10.1038/s41598-022-08909-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/09/2022] [Indexed: 01/04/2023] Open
Abstract
In the last decades, neuroimaging studies have attempted to unveil the neurobiological markers underlying pediatric psychiatric disorders. Yet, the vast majority of neuroimaging studies still focus on a single nosological category, which limit our understanding of the shared/specific neural correlates between these disorders. Therefore, we aimed to investigate the transdiagnostic neural correlates through a novel and data-driven meta-analytical method. A data-driven meta-analysis was carried out which grouped similar experiments’ topographic map together, irrespectively of nosological categories and task-characteristics. Then, activation likelihood estimation meta-analysis was performed on each group of experiments to extract spatially convergent brain regions. One hundred forty-seven experiments were retrieved (3124 cases compared to 3100 controls): 79 attention-deficit/hyperactivity disorder, 32 conduct/oppositional defiant disorder, 14 anxiety disorders, 22 major depressive disorders. Four significant groups of experiments were observed. Functional characterization suggested that these groups of aberrant brain regions may be implicated internally/externally directed processes, attentional control of affect, somato-motor and visual processes. Furthermore, despite that some differences in rates of studies involving major depressive disorders were noticed, nosological categories were evenly distributed between these four sets of regions. Our results may reflect transdiagnostic neural correlates of pediatric psychiatric disorders, but also underscore the importance of studying pediatric psychiatric disorders simultaneously rather than independently to examine differences between disorders.
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Affiliation(s)
- Jules R Dugré
- Research Center of the Institut Universitaire en Santé Mentale de Montréal, 7331 Hochelaga, Montreal, QC, H1N 3V2, Canada. .,Department of Psychiatry and Addictology, Faculty of Medicine, University of Montreal, Montreal, Canada.
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Jülich, Germany.,Institute for Systems Neuroscience, Heinrich Heine University, Düsseldorf, Germany
| | - Stéphane Potvin
- Research Center of the Institut Universitaire en Santé Mentale de Montréal, 7331 Hochelaga, Montreal, QC, H1N 3V2, Canada. .,Department of Psychiatry and Addictology, Faculty of Medicine, University of Montreal, Montreal, Canada.
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17
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Den Ouden L, Suo C, Albertella L, Greenwood LM, Lee RSC, Fontenelle LF, Parkes L, Tiego J, Chamberlain SR, Richardson K, Segrave R, Yücel M. Transdiagnostic phenotypes of compulsive behavior and associations with psychological, cognitive, and neurobiological affective processing. Transl Psychiatry 2022; 12:10. [PMID: 35013101 PMCID: PMC8748429 DOI: 10.1038/s41398-021-01773-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 12/02/2021] [Accepted: 12/16/2021] [Indexed: 01/10/2023] Open
Abstract
Compulsivity is a poorly understood transdiagnostic construct thought to underlie multiple disorders, including obsessive-compulsive disorder, addictions, and binge eating. Our current understanding of the causes of compulsive behavior remains primarily based on investigations into specific diagnostic categories or findings relying on one or two laboratory measures to explain complex phenotypic variance. This proof-of-concept study drew on a heterogeneous sample of community-based individuals (N = 45; 18-45 years; 25 female) exhibiting compulsive behavioral patterns in alcohol use, eating, cleaning, checking, or symmetry. Data-driven statistical modeling of multidimensional markers was utilized to identify homogeneous subtypes that were independent of traditional clinical phenomenology. Markers were based on well-defined measures of affective processing and included psychological assessment of compulsivity, behavioral avoidance, and stress, neurocognitive assessment of reward vs. punishment learning, and biological assessment of the cortisol awakening response. The neurobiological validity of the subtypes was assessed using functional magnetic resonance imaging. Statistical modeling identified three stable, distinct subtypes of compulsivity and affective processing, which we labeled "Compulsive Non-Avoidant", "Compulsive Reactive" and "Compulsive Stressed". They differed meaningfully on validation measures of mood, intolerance of uncertainty, and urgency. Most importantly, subtypes captured neurobiological variance on amygdala-based resting-state functional connectivity, suggesting they were valid representations of underlying neurobiology and highlighting the relevance of emotion-related brain networks in compulsive behavior. Although independent larger samples are needed to confirm the stability of subtypes, these data offer an integrated understanding of how different systems may interact in compulsive behavior and provide new considerations for guiding tailored intervention decisions.
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Affiliation(s)
- Lauren Den Ouden
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia.
| | - Chao Suo
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Lucy Albertella
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Lisa-Marie Greenwood
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
- Research School of Psychology, ANU College of Health and Medicine, The Australian National University, Canberra, Australia
| | - Rico S C Lee
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Leonardo F Fontenelle
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
- D'Or Institute for Research and Education and Anxiety, Obsessive, Compulsive Research Program, Institute of Psychiatry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Linden Parkes
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jeggan Tiego
- Neural Systems and Behaviour Lab, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Samuel R Chamberlain
- Department of Psychiatry, University of Southampton, Southampton, UK
- Southern Health NHS Foundation Trust, Southampton, UK
| | - Karyn Richardson
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Rebecca Segrave
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
| | - Murat Yücel
- BrainPark, The Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Clayton, Australia
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Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer. PERSONALITY NEUROSCIENCE 2021; 4:e6. [PMID: 34909565 PMCID: PMC8640675 DOI: 10.1017/pen.2021.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 03/30/2021] [Accepted: 04/12/2021] [Indexed: 12/13/2022]
Abstract
By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.
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19
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Pelin H, Ising M, Stein F, Meinert S, Meller T, Brosch K, Winter NR, Krug A, Leenings R, Lemke H, Nenadić I, Heilmann-Heimbach S, Forstner AJ, Nöthen MM, Opel N, Repple J, Pfarr J, Ringwald K, Schmitt S, Thiel K, Waltemate L, Winter A, Streit F, Witt S, Rietschel M, Dannlowski U, Kircher T, Hahn T, Müller-Myhsok B, Andlauer TFM. Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning. Neuropsychopharmacology 2021; 46:1895-1905. [PMID: 34127797 PMCID: PMC8429672 DOI: 10.1038/s41386-021-01051-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/24/2021] [Accepted: 05/28/2021] [Indexed: 02/07/2023]
Abstract
Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.
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Affiliation(s)
- Helena Pelin
- Max Planck Institute of Psychiatry, Munich, Germany.
- International Max Planck Research School for Translational Psychiatry, Munich, Germany.
| | - Marcus Ising
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Nils R Winter
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Hannah Lemke
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Stefanie Heilmann-Heimbach
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- Centre for Human Genetics, University of Marburg, Marburg, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Julia Pfarr
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
| | - Kai Ringwald
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Simon Schmitt
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Lena Waltemate
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Fabian Streit
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephanie Witt
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Marcella Rietschel
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Bertram Müller-Myhsok
- Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Till F M Andlauer
- Max Planck Institute of Psychiatry, Munich, Germany.
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany.
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20
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Zhang TH, Tang XC, Xu LH, Wei YY, Hu YG, Cui HR, Tang YY, Chen T, Li CB, Zhou LL, Wang JJ. Imbalance Model of Heart Rate Variability and Pulse Wave Velocity in Psychotic and Nonpsychotic Disorders. Schizophr Bull 2021; 48:154-165. [PMID: 34313787 PMCID: PMC8781329 DOI: 10.1093/schbul/sbab080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVES Patients with psychiatric disorders have an increased risk of cardiovascular pathologies. A bidirectional feedback model between the brain and heart exists widely in both psychotic and nonpsychotic disorders. The aim of this study was to compare heart rate variability (HRV) and pulse wave velocity (PWV) functions between patients with psychotic and nonpsychotic disorders and to investigate whether subgroups defined by HRV and PWV features improve the transdiagnostic psychopathology of psychiatric classification. METHODS In total, 3448 consecutive patients who visited psychiatric or psychological health services with psychotic (N = 1839) and nonpsychotic disorders (N = 1609) and were drug-free for at least 2 weeks were selected. HRV and PWV indicators were measured via finger photoplethysmography during a 5-minute period of rest. Canonical variates were generated through HRV and PWV indicators by canonical correlation analysis (CCA). RESULTS All HRV indicators but none of the PWV indicators were significantly reduced in the psychotic group relative to those in the nonpsychotic group. After adjusting for age, gender, and body mass index, many indices of HRV were significantly reduced in the psychotic group compared with those in the nonpsychotic group. CCA analysis revealed 2 subgroups defined by distinct and relatively homogeneous patterns along HRV and PWV dimensions and comprising 19.0% (subgroup 1, n = 655) and 80.9% (subgroup 2, n = 2781) of the sample, each with distinctive features of HRV and PWV functions. CONCLUSIONS HRV functions are significantly impaired among psychiatric patients, especially in those with psychosis. Our results highlight important subgroups of psychiatric patients that have distinct features of HRV and PWV which transcend current diagnostic boundaries.
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Affiliation(s)
- Tian Hong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - Xiao Chen Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - Li Hua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - Yan Yan Wei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - Ye Gang Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - Hui Ru Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - Ying Ying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada,Labor and Worklife Program, Harvard University, Boston, MA, USA,Niacin (Shanghai) Technology Co., Ltd., Shanghai, China
| | - Chun Bo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - Lin Lin Zhou
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - Ji Jun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, PR China,CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, PR China,Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China,To whom correspondence should be addressed; Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai 200030, China; tel: +86-21-34773065, fax: +86-21-64387986, e-mail:
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21
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Doyle CM, Lasch C, Vollman EP, Desjardins CD, Helwig NE, Jacob S, Wolff JJ, Elison JT. Phenoscreening: a developmental approach to research domain criteria-motivated sampling. J Child Psychol Psychiatry 2021; 62:884-894. [PMID: 33137226 PMCID: PMC11221542 DOI: 10.1111/jcpp.13341] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/05/2020] [Indexed: 11/29/2022]
Abstract
BACKGROUND To advance early identification efforts, we must detect and characterize neurodevelopmental sequelae of risk among population-based samples early in development. However, variability across the typical-to-atypical continuum and heterogeneity within and across early emerging psychiatric/neurodevelopmental disorders represent fundamental challenges to overcome. Identifying multidimensionally determined profiles of risk, agnostic to DSM categories, via data-driven computational approaches represents an avenue to improve early identification of risk. METHODS Factor mixture modeling (FMM) was used to identify subgroups and characterize phenotypic risk profiles, derived from multiple parent-report measures of typical and atypical behaviors common to autism spectrum disorder, in a community-based sample of 17- to 25-month-old toddlers (n = 1,570). To examine the utility of risk profile classification, a subsample of toddlers (n = 107) was assessed on a distal, independent outcome examining internalizing, externalizing, and dysregulation at approximately 30 months. RESULTS FMM results identified five asymmetrically sized subgroups. The putative high- and moderate-risk groups comprised 6% of the sample. Follow-up analyses corroborated the utility of the risk profile classification; the high-, moderate-, and low-risk groups were differentially stratified (i.e., HR > moderate-risk > LR) on outcome measures and comparison of high- and low-risk groups revealed large effect sizes for internalizing (d = 0.83), externalizing (d = 1.39), and dysregulation (d = 1.19). CONCLUSIONS This data-driven approach yielded five subgroups of toddlers, the utility of which was corroborated by later outcomes. Data-driven approaches, leveraging multiple developmentally appropriate dimensional RDoC constructs, hold promise for future efforts aimed toward early identification of at-risk-phenotypes for a variety of early emerging neurodevelopmental disorders.
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Affiliation(s)
- Colleen M. Doyle
- Institute of Child Development, University of Minnesota, Minneapolis, MN,USA
| | - Carolyn Lasch
- Institute of Child Development, University of Minnesota, Minneapolis, MN,USA
| | - Elayne P. Vollman
- Department of Comparative Human Development, University of Chicago, Chicago, IL, USA
| | | | - Nathaniel E. Helwig
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
- Department of Statistics, University of Minnesota, Minneapolis, MN, USA
| | - Suma Jacob
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Jason J. Wolff
- Department of Educational Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Jed T. Elison
- Institute of Child Development, University of Minnesota, Minneapolis, MN,USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
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22
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Rawls E, Kummerfeld E, Zilverstand A. An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis. Commun Biol 2021; 4:435. [PMID: 33790384 PMCID: PMC8012376 DOI: 10.1038/s42003-021-01955-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 02/24/2021] [Indexed: 12/24/2022] Open
Abstract
Alcohol use disorder (AUD) has high prevalence and adverse societal impacts, but our understanding of the factors driving AUD is hampered by a lack of studies that describe the complex neurobehavioral mechanisms driving AUD. We analyzed causal pathways to AUD severity using Causal Discovery Analysis (CDA) with data from the Human Connectome Project (HCP; n = 926 [54% female], 22% AUD [37% female]). We applied exploratory factor analysis to parse the wide HCP phenotypic space (100 measures) into 18 underlying domains, and we assessed functional connectivity within 12 resting-state brain networks. We then employed data-driven CDA to generate a causal model relating phenotypic factors, fMRI network connectivity, and AUD symptom severity, which highlighted a limited set of causes of AUD. The model proposed a hierarchy with causal influence propagating from brain connectivity to cognition (fluid/crystalized cognition, language/math ability, & working memory) to social (agreeableness/social support) to affective/psychiatric function (negative affect, low conscientiousness/attention, externalizing symptoms) and ultimately AUD severity. Our data-driven model confirmed hypothesized influences of cognitive and affective factors on AUD, while underscoring that addiction models need to be expanded to highlight the importance of social factors, amongst others.
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Affiliation(s)
- Eric Rawls
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
| | - Erich Kummerfeld
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Anna Zilverstand
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
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23
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Vanes LD, Dolan RJ. Transdiagnostic neuroimaging markers of psychiatric risk: A narrative review. NEUROIMAGE-CLINICAL 2021; 30:102634. [PMID: 33780864 PMCID: PMC8022867 DOI: 10.1016/j.nicl.2021.102634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/03/2021] [Accepted: 03/12/2021] [Indexed: 02/07/2023]
Abstract
We review the literature on neural correlates of a general psychopathology factor General psychopathology relates to structural and functional neurodevelopment Disrupted network connectivity maturation may underlie psychiatric vulnerability
Several decades of neuroimaging research in psychiatry have shed light on structural and functional neural abnormalities associated with individual psychiatric disorders. However, there is increasing evidence for substantial overlap in the patterns of neural dysfunction seen across disorders, suggesting that risk for psychiatric illness may be shared across diagnostic boundaries. Gaining insights on the existence of shared neural mechanisms which may transdiagnostically underlie psychopathology is important for psychiatric research in order to tease apart the unique and common aspects of different disorders, but also clinically, so as to help identify individuals early on who may be biologically vulnerable to psychiatric disorder in general. In this narrative review, we first evaluate recent studies investigating the functional and structural neural correlates of a general psychopathology factor, which is thought to reflect the shared variance across common mental health symptoms and therefore index psychiatric vulnerability. We then link insights from this research to existing meta-analytic evidence for shared patterns of neural dysfunction across categorical psychiatric disorders. We conclude by providing an integrative account of vulnerability to mental illness, whereby delayed or disrupted maturation of large-scale networks (particularly default-mode, executive, and sensorimotor networks), and more generally between-network connectivity, results in a compromised ability to integrate and switch between internally and externally focused tasks.
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Affiliation(s)
- Lucy D Vanes
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, King's College London, United Kingdom.
| | - Raymond J Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
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Hart KL, Perlis RH, McCoy TH. Mapping of Transdiagnostic Neuropsychiatric Phenotypes Across Patients in Two General Hospitals. J Acad Consult Liaison Psychiatry 2021; 62:430-439. [PMID: 34210402 DOI: 10.1016/j.jaclp.2021.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND Multidimensional transdiagnostic phenotyping systems are increasingly important to neuropsychiatric phenotyping, particularly in translational research settings. The relationship the National Institute of Mental Health's Research Domain Criteria multidimensional approach to psychopathology and nonpsychiatric diagnoses has not been studied at scale but is relevant to those caring for neuropsychiatric illness in medical and surgical settings. METHODS We applied the CQH Dimensional Phenotyper natural language processing tool to estimate National Institute of Mental Health's Research Domain Criteria domain-associated symptoms of individuals admitted to nonpsychiatric wards at each of 2 large academic general hospitals over an 8-year period. We compared patterns in individual domain symptom burden, as well as a new pooled unidimensional measure, by primary medical and surgical diagnosis. RESULTS Analysis included 227,243 patients from hospital 1 of whom 68,793 (30.3%) had a prior psychiatric history and 220,213 patients from hospital 2 of whom 50,818 (23.1%) had a prior psychiatric history. The distribution of Research Domain Criteria symptom burdens over primary diagnosis was similar across hospital sites and differed significantly across primary medical or surgical diagnosis. The effect of primary medical or surgical diagnosis was larger than that of prior psychiatric history on Research Domain Criteria symptom burden. CONCLUSION Research Domain Criteria-based neuropsychiatric symptom burden estimated from general hospital patients' clinical documentation is more strongly associated with the primary hospital medical or surgical diagnosis than it is with the presence of a previous psychiatric history. The bidirectional role of psychiatric and somatic illness warrants further study through the lens of transdiagnostic phenotyping.
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Affiliation(s)
- Kamber L Hart
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA
| | - Roy H Perlis
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA
| | - Thomas H McCoy
- Center for Quantitative Health, Massachusetts General Hospital, Boston, MA.
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25
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Just a phase? Mapping the transition of behavioural problems from childhood to adolescence. Soc Psychiatry Psychiatr Epidemiol 2021; 56:821-836. [PMID: 33569649 PMCID: PMC8068698 DOI: 10.1007/s00127-020-02014-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 12/08/2020] [Indexed: 02/02/2023]
Abstract
PURPOSE Young people change substantially between childhood and adolescence. Yet, the current description of behavioural problems does not incorporate any reference to the developmental context. In the current analysis, we aimed to identify common transitions of behavioural problems between childhood and adolescence. METHOD We followed 6744 individuals over 6 years as they transitioned from childhood (age 10) into adolescence (age 16). At each stage, we used a data-driven hierarchical clustering method to identify common profiles of behavioural problems, map transitions between profiles and identify factors that predict specific transitions. RESULTS Common profiles of behavioural problems matched known comorbidity patterns but crucially showed that the presentation of behavioural problems changes markedly between childhood and adolescence. While problems with hyperactivity/impulsivity, motor control and conduct were prominent in childhood, adolescents showed profiles of problems related to emotional control, anxiety and inattention. Transitions were associated with socio-economic status and cognitive performance in childhood CONCLUSION: We show that understanding behavioural difficulties and mental ill-health must take into account the developmental context in which the problems occur, and we establish key risk factors for specific negative transitions as children become adolescents.
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Tu PC, Chen MH, Chang WC, Kao ZK, Hsu JW, Lin WC, Li CT, Su TP, Bai YM. Identification of common neural substrates with connectomic abnormalities in four major psychiatric disorders: A connectome-wide association study. Eur Psychiatry 2020; 64:e8. [PMID: 33267917 PMCID: PMC8057470 DOI: 10.1192/j.eurpsy.2020.106] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Background Recent imaging studies of large datasets suggested that psychiatric disorders have common biological substrates. This study aimed to identify all the common neural substrates with connectomic abnormalities across four major psychiatric disorders by using the data-driven connectome-wide association method of multivariate distance matrix regression (MDMR). Methods This study analyzed a resting functional magnetic resonance imaging dataset of 100 patients with schizophrenia, 100 patients with bipolar I disorder, 100 patients with bipolar II disorder, 100 patients with major depressive disorder, and 100 healthy controls (HCs). We calculated a voxel-wise 4,330 × 4,330 matrix of whole-brain functional connectivity (FC) with 8-mm isotropic resolution for each participant and then performed MDMR to identify structures where the overall multivariate pattern of FC was significantly different between each patient group and the HC group. A conjunction analysis was performed to identify common neural regions with FC abnormalities across these four psychiatric disorders. Results The conjunction of the MDMR maps revealed that the four groups of patients shared connectomic abnormalities in distributed cortical and subcortical structures, which included bilateral thalamus, cerebellum, frontal pole, supramarginal gyrus, postcentral gyrus, lingual gyrus, lateral occipital cortex, and parahippocampus. The follow-up analysis based on pair-wise FC of these regions demonstrated that these psychiatric disorders also shared similar patterns of FC abnormalities characterized by sensory/subcortical hyperconnectivity, association/subcortical hypoconnectivity, and sensory/association hyperconnectivity. Conclusions These findings suggest that major psychiatric disorders share common connectomic abnormalities in distributed cortical and subcortical regions and provide crucial support for the common network hypothesis of major psychiatric disorders.
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Affiliation(s)
- Pei-Chi Tu
- Department of Medical Research, Taipei Veterans General Hospital, Taipei112, Taiwan.,Department of Psychiatry, Taipei Veterans General Hospital, Taipei112, Taiwan.,Institute of Philosophy of Mind and Cognition, National Yang-Ming University, Taipei, Taiwan.,Department of Psychiatry, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Mu-Hong Chen
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei112, Taiwan.,Department of Psychiatry, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan.,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Wan-Chen Chang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei112, Taiwan.,Department of Psychiatry, Taipei Veterans General Hospital, Taipei112, Taiwan.,Department of Biomedical Engineering, National Yang-Ming University, Taipei, Taiwan
| | - Zih-Kai Kao
- Department of Medical Research, Taipei Veterans General Hospital, Taipei112, Taiwan.,Department of Psychiatry, Taipei Veterans General Hospital, Taipei112, Taiwan
| | - Ju-Wei Hsu
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei112, Taiwan.,Department of Psychiatry, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Wei-Chen Lin
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei112, Taiwan.,Department of Psychiatry, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan.,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Cheng-Ta Li
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei112, Taiwan.,Department of Psychiatry, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan.,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Tung-Ping Su
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei112, Taiwan.,Department of Psychiatry, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan.,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.,Department of Psychiatry, Cheng Hsin General Hospital, Taipei, Taiwan
| | - Ya-Mei Bai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei112, Taiwan.,Department of Psychiatry, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan.,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
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27
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Sesso G, Cristofani C, Berloffa S, Cristofani P, Fantozzi P, Inguaggiato E, Narzisi A, Pfanner C, Ricci F, Tacchi A, Valente E, Viglione V, Milone A, Masi G. Autism Spectrum Disorder and Disruptive Behavior Disorders Comorbidities Delineate Clinical Phenotypes in Attention-Deficit Hyperactivity Disorder: Novel Insights from the Assessment of Psychopathological and Neuropsychological Profiles. J Clin Med 2020; 9:jcm9123839. [PMID: 33256132 PMCID: PMC7760262 DOI: 10.3390/jcm9123839] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 11/22/2020] [Accepted: 11/24/2020] [Indexed: 11/16/2022] Open
Abstract
Although childhood-onset psychiatric disorders are often considered as distinct and separate from each other, they frequently co-occur, with partial overlapping symptomatology. Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) commonly co-occur with each other and with other mental disorders, particularly disruptive behavior disorders, oppositional defiant disorder/conduct disorder (ODD/CD). Whether these associated comorbidities represent a spectrum of distinct clinical phenotypes is matter of research. The aim of our study was to describe the clinical phenotypes of youths with ADHD with and without ASD and/or ODD/CD, based on neuropsychological and psychopathological variables. One-hundred fifty-one participants with ADHD were prospectively recruited and assigned to four clinical groups, and assessed by means of parent-reported questionnaires, the child behavior checklist and the behavior rating inventory of executive functions. The ADHD alone group presented a greater impairment in metacognitive executive functions, ADHD+ASD patients presented higher internalizing problems and deficits in Shifting tasks, and ADHD+ODD/CD subjects presented emotional-behavioral dysregulation. Moreover, ADHD+ASD+ODD/CD individuals exhibited greater internalizing and externalizing problems, and specific neuropsychological impairments in the domains of emotional regulation. Our study supports the need to implement the evaluation of the psychopathological and neuropsychological functioning profiles, and to characterize specific endophenotypes for a finely customized establishment of treatment strategies.
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Affiliation(s)
- Gianluca Sesso
- Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy;
- IRCCS Stella Maris, Scientific Institute of Child Neurology and Psychiatry, Calambrone, 56128 Pisa, Italy; (C.C.); (S.B.); (P.C.); (P.F.); (E.I.); (A.N.); (C.P.); (F.R.); (A.T.); (E.V.); (V.V.); (G.M.)
| | - Chiara Cristofani
- IRCCS Stella Maris, Scientific Institute of Child Neurology and Psychiatry, Calambrone, 56128 Pisa, Italy; (C.C.); (S.B.); (P.C.); (P.F.); (E.I.); (A.N.); (C.P.); (F.R.); (A.T.); (E.V.); (V.V.); (G.M.)
| | - Stefano Berloffa
- IRCCS Stella Maris, Scientific Institute of Child Neurology and Psychiatry, Calambrone, 56128 Pisa, Italy; (C.C.); (S.B.); (P.C.); (P.F.); (E.I.); (A.N.); (C.P.); (F.R.); (A.T.); (E.V.); (V.V.); (G.M.)
| | - Paola Cristofani
- IRCCS Stella Maris, Scientific Institute of Child Neurology and Psychiatry, Calambrone, 56128 Pisa, Italy; (C.C.); (S.B.); (P.C.); (P.F.); (E.I.); (A.N.); (C.P.); (F.R.); (A.T.); (E.V.); (V.V.); (G.M.)
| | - Pamela Fantozzi
- IRCCS Stella Maris, Scientific Institute of Child Neurology and Psychiatry, Calambrone, 56128 Pisa, Italy; (C.C.); (S.B.); (P.C.); (P.F.); (E.I.); (A.N.); (C.P.); (F.R.); (A.T.); (E.V.); (V.V.); (G.M.)
| | - Emanuela Inguaggiato
- IRCCS Stella Maris, Scientific Institute of Child Neurology and Psychiatry, Calambrone, 56128 Pisa, Italy; (C.C.); (S.B.); (P.C.); (P.F.); (E.I.); (A.N.); (C.P.); (F.R.); (A.T.); (E.V.); (V.V.); (G.M.)
| | - Antonio Narzisi
- IRCCS Stella Maris, Scientific Institute of Child Neurology and Psychiatry, Calambrone, 56128 Pisa, Italy; (C.C.); (S.B.); (P.C.); (P.F.); (E.I.); (A.N.); (C.P.); (F.R.); (A.T.); (E.V.); (V.V.); (G.M.)
| | - Chiara Pfanner
- IRCCS Stella Maris, Scientific Institute of Child Neurology and Psychiatry, Calambrone, 56128 Pisa, Italy; (C.C.); (S.B.); (P.C.); (P.F.); (E.I.); (A.N.); (C.P.); (F.R.); (A.T.); (E.V.); (V.V.); (G.M.)
| | - Federica Ricci
- IRCCS Stella Maris, Scientific Institute of Child Neurology and Psychiatry, Calambrone, 56128 Pisa, Italy; (C.C.); (S.B.); (P.C.); (P.F.); (E.I.); (A.N.); (C.P.); (F.R.); (A.T.); (E.V.); (V.V.); (G.M.)
| | - Annalisa Tacchi
- IRCCS Stella Maris, Scientific Institute of Child Neurology and Psychiatry, Calambrone, 56128 Pisa, Italy; (C.C.); (S.B.); (P.C.); (P.F.); (E.I.); (A.N.); (C.P.); (F.R.); (A.T.); (E.V.); (V.V.); (G.M.)
| | - Elena Valente
- IRCCS Stella Maris, Scientific Institute of Child Neurology and Psychiatry, Calambrone, 56128 Pisa, Italy; (C.C.); (S.B.); (P.C.); (P.F.); (E.I.); (A.N.); (C.P.); (F.R.); (A.T.); (E.V.); (V.V.); (G.M.)
| | - Valentina Viglione
- IRCCS Stella Maris, Scientific Institute of Child Neurology and Psychiatry, Calambrone, 56128 Pisa, Italy; (C.C.); (S.B.); (P.C.); (P.F.); (E.I.); (A.N.); (C.P.); (F.R.); (A.T.); (E.V.); (V.V.); (G.M.)
| | - Annarita Milone
- IRCCS Stella Maris, Scientific Institute of Child Neurology and Psychiatry, Calambrone, 56128 Pisa, Italy; (C.C.); (S.B.); (P.C.); (P.F.); (E.I.); (A.N.); (C.P.); (F.R.); (A.T.); (E.V.); (V.V.); (G.M.)
- Correspondence: ; Tel.: +39-050-886306
| | - Gabriele Masi
- IRCCS Stella Maris, Scientific Institute of Child Neurology and Psychiatry, Calambrone, 56128 Pisa, Italy; (C.C.); (S.B.); (P.C.); (P.F.); (E.I.); (A.N.); (C.P.); (F.R.); (A.T.); (E.V.); (V.V.); (G.M.)
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28
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Tymofiyeva O, Zhou VX, Lee CM, Xu D, Hess CP, Yang TT. MRI Insights Into Adolescent Neurocircuitry-A Vision for the Future. Front Hum Neurosci 2020; 14:237. [PMID: 32733218 PMCID: PMC7359264 DOI: 10.3389/fnhum.2020.00237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 05/29/2020] [Indexed: 11/13/2022] Open
Abstract
Adolescence is the time of onset of many psychiatric disorders. Half of pediatric patients present with comorbid psychiatric disorders that complicate both their medical and psychiatric care. Currently, diagnosis and treatment decisions are based on symptoms. The field urgently needs brain-based diagnosis and personalized care. Neuroimaging can shed light on how aberrations in brain circuits might underlie psychiatric disorders and their development in adolescents. In this perspective article, we summarize recent MRI literature that provides insights into development of psychiatric disorders in adolescents. We specifically focus on studies of brain structural and functional connectivity. Ninety-six included studies demonstrate the potential of MRI to assess psychiatrically relevant constructs, diagnose psychiatric disorders, predict their development or predict response to treatment. Limitations of the included studies are discussed, and recommendations for future research are offered. We also present a vision for the role that neuroimaging may play in pediatrics and primary care in the future: a routine neuropsychological and neuropsychiatric imaging (NPPI) protocol for adolescent patients, which would include a 30-min brain scan, a quality control and safety read of the scan, followed by computer-based calculation of the structural and functional brain network metrics that can be compared to the normative data by the pediatrician. We also perform a cost-benefit analysis to support this vision and provide a roadmap of the steps required for this vision to be implemented.
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Affiliation(s)
- Olga Tymofiyeva
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Vivian X Zhou
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Chuan-Mei Lee
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.,Clinical Excellence Research Center, Stanford University, Stanford, CA, United States
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Christopher P Hess
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Tony T Yang
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
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29
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Kaczkurkin AN, Moore TM, Sotiras A, Xia CH, Shinohara RT, Satterthwaite TD. Approaches to Defining Common and Dissociable Neurobiological Deficits Associated With Psychopathology in Youth. Biol Psychiatry 2020; 88:51-62. [PMID: 32087950 PMCID: PMC7305976 DOI: 10.1016/j.biopsych.2019.12.015] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 11/07/2019] [Accepted: 12/11/2019] [Indexed: 01/31/2023]
Abstract
Psychiatric disorders show high rates of comorbidity and nonspecificity of presenting clinical symptoms, while demonstrating substantial heterogeneity within diagnostic categories. Notably, many of these psychiatric disorders first manifest in youth. We review progress and next steps in efforts to parse heterogeneity in psychiatric symptoms in youths by identifying abnormalities within neural circuits. To address this fundamental challenge in psychiatry, a number of methods have been proposed. We provide an overview of these methods, broadly organized into dimensional versus categorical approaches and single-view versus multiview approaches. Dimensional approaches including factor analysis and canonical correlation analysis aim to capture dimensional associations between psychopathology and brain measures across a continuous spectrum from health to disease. In contrast, categorical approaches, such as clustering and community detection, aim to identify subtypes of individuals within a class of symptoms or brain features. We highlight several studies that apply these methods to samples of youths and discuss issues to consider when using these approaches. Finally, we end by highlighting avenues for future research.
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Affiliation(s)
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Cedric Huchuan Xia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
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30
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Hong SJ, Vogelstein JT, Gozzi A, Bernhardt BC, Yeo BTT, Milham MP, Di Martino A. Toward Neurosubtypes in Autism. Biol Psychiatry 2020; 88:111-128. [PMID: 32553193 DOI: 10.1016/j.biopsych.2020.03.022] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 03/25/2020] [Accepted: 03/28/2020] [Indexed: 12/22/2022]
Abstract
There is a consensus that substantial heterogeneity underlies the neurobiology of autism spectrum disorder (ASD). As such, it has become increasingly clear that a dissection of variation at the molecular, cellular, and brain network domains is a prerequisite for identifying biomarkers. Neuroimaging has been widely used to characterize atypical brain patterns in ASD, although findings have varied across studies. This is due, at least in part, to a failure to account for neurobiological heterogeneity. Here, we summarize emerging data-driven efforts to delineate more homogeneous ASD subgroups at the level of brain structure and function-that is, neurosubtyping. We break this pursuit into key methodological steps: the selection of diagnostic samples, neuroimaging features, algorithms, and validation approaches. Although preliminary and methodologically diverse, current studies generally agree that at least 2 to 4 distinct ASD neurosubtypes may exist. Their identification improved symptom prediction and diagnostic label accuracy above and beyond group average comparisons. Yet, this nascent literature has shed light onto challenges and gaps. These include 1) the need for wider and more deeply transdiagnostic samples collected while minimizing artifacts (e.g., head motion), 2) quantitative and unbiased methods for feature selection and multimodal fusion, 3) greater emphasis on algorithms' ability to capture hybrid dimensional and categorical models of ASD, and 4) systematic independent replications and validations that integrate different units of analyses across multiple scales. Solutions aimed to address these challenges and gaps are discussed for future avenues leading toward a comprehensive understanding of the mechanisms underlying ASD heterogeneity.
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Affiliation(s)
- Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, New York
| | - Joshua T Vogelstein
- Department of Biomedical Engineering Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, Maryland
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - B T Thomas Yeo
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Department of Electrical and Computer Engineering, Center for Sleep and Cognition, Clinical Imaging Research Centre, N.1 Institute for Health, National University of Singapore; NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore; Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York
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31
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Siugzdaite R, Bathelt J, Holmes J, Astle DE. Transdiagnostic Brain Mapping in Developmental Disorders. Curr Biol 2020; 30:1245-1257.e4. [PMID: 32109389 PMCID: PMC7139199 DOI: 10.1016/j.cub.2020.01.078] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 12/09/2019] [Accepted: 01/28/2020] [Indexed: 01/21/2023]
Abstract
Childhood learning difficulties and developmental disorders are common, but progress toward understanding their underlying brain mechanisms has been slow. Structural neuroimaging, cognitive, and learning data were collected from 479 children (299 boys, ranging in age from 62 to 223 months), 337 of whom had been referred to the study on the basis of learning-related cognitive problems. Machine learning identified different cognitive profiles within the sample, and hold-out cross-validation showed that these profiles were significantly associated with children's learning ability. The same machine learning approach was applied to cortical morphology data to identify different brain profiles. Hold-out cross-validation demonstrated that these were significantly associated with children's cognitive profiles. Crucially, these mappings were not one-to-one. The same neural profile could be associated with different cognitive impairments across different children. One possibility is that the organization of some children's brains is less susceptible to local deficits. This was tested by using diffusion-weighted imaging (DWI) to construct whole-brain white-matter connectomes. A simulated attack on each child's connectome revealed that some brain networks were strongly organized around highly connected hubs. Children with these networks had only selective cognitive impairments or no cognitive impairments at all. By contrast, the same attacks had a significantly different impact on some children's networks, because their brain efficiency was less critically dependent on hubs. These children had the most widespread and severe cognitive impairments. On this basis, we propose a new framework in which the nature and mechanisms of brain-to-cognition relationships are moderated by the organizational context of the overall network.
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Affiliation(s)
- Roma Siugzdaite
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Rd, Cambridge CB2 7EF, UK
| | - Joe Bathelt
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Rd, Cambridge CB2 7EF, UK; Dutch Autism & ADHD Research Center, Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129-B, Amsterdam 1018 WS, the Netherlands
| | - Joni Holmes
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Rd, Cambridge CB2 7EF, UK
| | - Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Rd, Cambridge CB2 7EF, UK.
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32
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Zhang X, Braun U, Tost H, Bassett DS. Data-Driven Approaches to Neuroimaging Analysis to Enhance Psychiatric Diagnosis and Therapy. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:780-790. [PMID: 32127291 DOI: 10.1016/j.bpsc.2019.12.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2019] [Revised: 12/10/2019] [Accepted: 12/19/2019] [Indexed: 01/23/2023]
Abstract
Combining advanced neuroimaging with novel computational methods in network science and machine learning has led to increasingly meaningful descriptions of structure and function in both the normal and the abnormal brain, thereby contributing significantly to our understanding of psychiatric disorders as circuit dysfunctions. Despite its marked potential for psychiatric care, this approach has not yet extended beyond the research setting to any clinically useful applications. Here we review current developments in the study of neuroimaging data using network models and machine learning methods, with a focus on their promise in offering a framework for clinical translation. We discuss 3 potential contributions of these methods to psychiatric care: 1) a better understanding of psychopathology beyond current diagnostic boundaries; 2) individualized prediction of treatment response and prognosis; and 3) formal theories to guide the development of novel interventions. Finally, we highlight current obstacles and sketch a forward-looking perspective of how the application of machine learning and network modeling methods should proceed to accelerate their potential transformation of clinically useful tools.
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Affiliation(s)
- Xiaolong Zhang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Urs Braun
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany; Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania; Santa Fe Institute, Santa Fe, New Mexico.
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33
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Kebets V, Holmes AJ, Orban C, Tang S, Li J, Sun N, Kong R, Poldrack RA, Yeo BTT. Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology. Biol Psychiatry 2019; 86:779-791. [PMID: 31515054 DOI: 10.1016/j.biopsych.2019.06.013] [Citation(s) in RCA: 131] [Impact Index Per Article: 26.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 05/15/2019] [Accepted: 06/05/2019] [Indexed: 12/26/2022]
Abstract
BACKGROUND There is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which might miss possible links between psychopathology, cognitive processes, and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thereby neglecting the potential influence of huge swaths of the brain. METHODS A multivariate data-driven approach (partial least squares) was used to identify latent components linking a large set of clinical, cognitive, and personality measures to whole-brain resting-state functional connectivity patterns across 224 participants. The participants were either healthy (n = 110) or diagnosed with bipolar disorder (n = 40), attention-deficit/hyperactivity disorder (n = 37), schizophrenia (n = 29), or schizoaffective disorder (n = 8). In contrast to traditional case-control analyses, the diagnostic categories were not used in the partial least squares analysis but were helpful for interpreting the components. RESULTS Our analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction, and impulsivity. Each component was associated with a unique whole-brain resting-state functional connectivity signature and was shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensory-motor network and its connectivity with subcortical structures and cortical executive networks. CONCLUSIONS We identified three distinct dimensions with dissociable (but overlapping) whole-brain resting-state functional connectivity signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span diagnostic categories. Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks.
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Affiliation(s)
- Valeria Kebets
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts; Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
| | - Csaba Orban
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; Neuropsychopharmacology Unit, Centre for Psychiatry, Imperial College London, London, United Kingdom
| | - Siyi Tang
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; Department of Electrical Engineering, Stanford University, Stanford, California
| | - Jingwei Li
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | - Nanbo Sun
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | - Ru Kong
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | | | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore; Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.
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34
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Ivleva EI, Turkozer HB, Sweeney JA. Imaging-Based Subtyping for Psychiatric Syndromes. Neuroimaging Clin N Am 2019; 30:35-44. [PMID: 31759570 DOI: 10.1016/j.nic.2019.09.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Despite considerable research evidence demonstrating significant neurobiological alterations in psychiatric disorders, incorporating neuroimaging approaches into clinical practice remains challenging. There is an urgent need for biologically validated psychiatric disease constructs that can inform diagnostic algorithms and targeted treatment development. In this article, we present a conceptual review of the most robust and impactful findings from studies that use neuroimaging methods in efforts to define distinct disease subtypes, while emphasizing cross-diagnostic and dimensional approaches. In addition, we discuss current challenges in psychoradiology and outline potential future strategies for clinically applicable translation.
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Affiliation(s)
- Elena I Ivleva
- Department of Psychiatry, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, NC5, Dallas, TX 75390, USA.
| | - Halide B Turkozer
- Department of Psychiatry, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, NC5, Dallas, TX 75390, USA
| | - John A Sweeney
- Department of Psychiatry, University of Cincinnati, 2600 Clifton Avenue, Cincinnati, OH 45221, USA
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35
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Hong SJ, Valk SL, Di Martino A, Milham MP, Bernhardt BC. Multidimensional Neuroanatomical Subtyping of Autism Spectrum Disorder. Cereb Cortex 2019; 28:3578-3588. [PMID: 28968847 DOI: 10.1093/cercor/bhx229] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 08/23/2017] [Indexed: 12/15/2022] Open
Abstract
Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders with multiple biological etiologies and highly variable symptoms. Using a novel analytical framework that integrates cortex-wide MRI markers of vertical (i.e., thickness, tissue contrast) and horizontal (i.e., surface area, geodesic distance) cortical organization, we could show that a large multi-centric cohort of individuals with ASD falls into 3 distinctive anatomical subtypes (ASD-I: cortical thickening, increased surface area, tissue blurring; ASD-II: cortical thinning, decreased distance; ASD-III: increased distance). Bootstrap analysis indicated a high consistency of these biotypes across thousands of simulations, while analysis of behavioral phenotypes and resting-state fMRI showed differential symptom load (i.e., Autism Diagnostic Observation Schedule; ADOS) and instrinsic connectivity anomalies in communication and social-cognition networks. Notably, subtyping improved supervised learning approaches predicting ADOS score in single subjects, with significantly increased performance compared to a subtype-blind approach. The existence of different subtypes may reconcile previous results so far not converging on a consistent pattern of anatomical anomalies in autism, and possibly relate the presence of diverging corticogenic and maturational anomalies. The high accuracy for symptom severity prediction indicates benefits of MRI biotyping for personalized diagnostics and may guide the development of targeted therapeutic strategies.
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Affiliation(s)
- Seok-Jun Hong
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, Canada
| | - Sofie L Valk
- Department of Social Neuroscience, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, Leipzig, Germany
| | - Adriana Di Martino
- Department of Child and Adolescent Psychiatry, Child Study Center at NYU Langone Health, 1 Park Avenue, New York, NY, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY, USA.,Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140 Old Orangeburg Rd, Orangeburg, New York, NY, USA
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, Canada
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Ross CA, Margolis RL. Research Domain Criteria: Strengths, Weaknesses, and Potential Alternatives for Future Psychiatric Research. MOLECULAR NEUROPSYCHIATRY 2019; 5:218-236. [PMID: 31768375 DOI: 10.1159/000501797] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 06/27/2019] [Indexed: 01/07/2023]
Abstract
The Research Domain Criteria (RDoC) paradigm was launched 10 years ago as a superior approach for investigation of mental illness. RDoC conceptualizes normal human behavior, emotion, and cognition as dimensional, with mental illnesses as dimensional extremes. We suggest that RDoC may have value for understanding normal human psychology and some conditions plausibly construed as extremes of normal variation. By contrast, for the most serious of mental illnesses, including dementia, autism, schizophrenia, and bipolar disorder, we argue that RDoC is conceptually flawed. RDoC conflates variation along dimensional axes of normal function with quantitative measurements of disease phenotypes and with the occurrence of diseases in overlapping clusters or spectra. This moves away from the disease model of major mental illness. Further, RDoC imposes a top-down approach to research. We argue that progress in major mental illness research will be more rapid with a bottom-up approach, starting with the discovery of etiological factors, proceeding to investigation of pathogenic pathways, including use of cell and animal models, and leading to a refined nosology and novel, targeted treatments.
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Affiliation(s)
- Christopher A Ross
- Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Pharmacology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Russell L Margolis
- Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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37
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Wang J, Sun W, Tang X, Xu L, Wei Y, Cui H, Tang Y, Hui L, Jia Q, Zhu H, Wang J, Zhang T. Transdiagnostic Dimensions towards Personality Pathology and Childhood Traumatic Experience in a Clinical Sample: Subtype Classification by a Cross-sectional Analysis. Sci Rep 2019; 9:11248. [PMID: 31375755 PMCID: PMC6677786 DOI: 10.1038/s41598-019-47754-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 07/23/2019] [Indexed: 12/23/2022] Open
Abstract
Psychiatric disorders are highly heterogeneous syndromes often explained by underlying and internalized personality disorder(PD) traits that are affected by externalized childhood trauma experiences(CTE). The present study investigated the differential subtype model by examining the association between PD traits and CTE in a clinical sample with transdiagnostic psychopathology. Outpatients(n = 2090) presenting for psychiatric treatment completed self-reported measures of PD traits(Personality Diagnostic Questionnaire) and the childhood adversity(Child Trauma Questionnaire). Canonical variates were generated by canonical correlation analysis(CCA) and then used for hierarchical cluster analysis to produce subtypes. A support vector machine(SVM) model was used and validated using a linear kernel to assess the utility of the extracted subtypes of outpatients in clinical diagnosis classifications. The CCA determined two linear combinations: emotional abuse related dissociality PD traits(antisocial and paranoid PD) and emotional neglect related sociality PD traits(schizoid, passive-aggressive, depressive, histrionic, and avoidant PD). A cluster analysis revealed three subtypes defined by distinct and relatively homogeneous patterns along two dimensions, and comprising 17.5%(cluster-1, n = 365), 34.8%(cluster-2, n = 727), and 47.8%(cluster-3, n = 998) of the sample, each with distinctive features of PD traits and CTE. These subtypes suggest more distinct PD trait correlates of CTE manifestations than were captured by clinical phenomenological diagnostic definitions. Our results highlight important subtypes of psychiatric patients that highlight PD traits and CTE that transcend current diagnostic boundaries. The three different subtypes reflect significant differences in PD and CTE characteristics and lend support to efforts to develop PD and childhood trauma targeted psychotherapy that extends to clinical diagnosis-based interventions.
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Affiliation(s)
- JunJie Wang
- Institute of Mental Health, Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow Unversity, Soochow Unversity, Suzhou, Jiangsu, 215137, China
| | - Wei Sun
- Department of Neurosurgery, Pu Nan Hospital, Shanghai, 200125, China
| | - XiaoChen Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 200030, P.R. China
| | - LiHua Xu
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 200030, P.R. China
| | - YanYan Wei
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 200030, P.R. China
| | - HuiRu Cui
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 200030, P.R. China
| | - YingYing Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 200030, P.R. China
| | - Li Hui
- Institute of Mental Health, Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow Unversity, Soochow Unversity, Suzhou, Jiangsu, 215137, China
| | - QiuFang Jia
- Institute of Mental Health, Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow Unversity, Soochow Unversity, Suzhou, Jiangsu, 215137, China
| | - Hongliang Zhu
- Institute of Mental Health, Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow Unversity, Soochow Unversity, Suzhou, Jiangsu, 215137, China.
| | - JiJun Wang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 200030, P.R. China. .,Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai, P.R. China. .,Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.
| | - TianHong Zhang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 200030, P.R. China.
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McElroy E, Patalay P. In search of disorders: internalizing symptom networks in a large clinical sample. J Child Psychol Psychiatry 2019; 60:897-906. [PMID: 30900257 PMCID: PMC6767473 DOI: 10.1111/jcpp.13044] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/10/2019] [Indexed: 12/01/2022]
Abstract
BACKGROUND The co-occurrence of internalizing disorders is a common form of psychiatric comorbidity, raising questions about the boundaries between these diagnostic categories. We employ network psychometrics in order to: (a) determine whether internalizing symptoms cluster in a manner reflecting DSM diagnostic criteria, (b) gauge how distinct these diagnostic clusters are and (c) examine whether this network structure changes from childhood to early and then late adolescence. METHOD Symptom-level data were obtained for service users in publicly funded mental health services in England between 2011 and 2015 (N = 37,162). A symptom network (i.e. Gaussian graphical model) was estimated, and a community detection algorithm was used to explore the clustering of symptoms. RESULTS The estimated network was densely connected and characterized by a multitude of weak associations between symptoms. Six communities of symptoms were identified; however, they were weakly demarcated. Two of these communities corresponded to social phobia and panic disorder, and four did not clearly correspond with DSM diagnostic categories. The network structure was largely consistent by sex and across three age groups (8-11, 12-14 and 15-18 years). Symptom connectivity in the two older age groups was significantly greater compared to the youngest group and there were differences in centrality across the age groups, highlighting the age-specific relevance of certain symptoms. CONCLUSIONS These findings clearly demonstrate the interconnected nature of internalizing symptoms, challenging the view that such pathology takes the form of distinct disorders.
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Affiliation(s)
- Eoin McElroy
- Institute of Psychology, Health and SocietyUniversity of LiverpoolLiverpoolUK
| | - Praveetha Patalay
- Institute of Psychology, Health and SocietyUniversity of LiverpoolLiverpoolUK
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Pasion R, Martins EC, Barbosa F. Empirically supported interventions in psychology: contributions of Research Domain Criteria. ACTA ACUST UNITED AC 2019; 32:15. [PMID: 32027006 PMCID: PMC6966736 DOI: 10.1186/s41155-019-0128-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 07/05/2019] [Indexed: 11/10/2022]
Abstract
Empirically supported interventions in psychological disorders should provide (1) evidence supporting the underlying psychological mechanisms of psychopathology to target in the intervention and (2) evidence supporting the efficacy of the intervention. However, research has been dedicated in a greater extent to efficacy than to the acquisition of empirical support for the theoretical basis of therapies. Research Domain Criteria (RDoC) emerges as a new framework to provide empirically based theories about psychological mechanisms that may be targeted in intervention and tested for its efficacy. The current review aims to demonstrate the possible applications of RDoC to design empirically supported interventions for psychological disorders. Two RDoC-inspired interventions are reviewed, and the RDoC framework is broadly explored in terms of its contributions and limitations. From preliminary evidence, RDoC offers many avenues for improving evidence-based interventions in psychology, but some limitations must be anticipated to increase the RDoC applicability to naturalistic settings.
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Affiliation(s)
- Rita Pasion
- Laboratory of Neuropsychophysiology, Faculty of Psychology and Educational Sciences, University of Porto, Rua Alfredo Allen, 535, 4200-135, Porto, Portugal.
| | - Eva C Martins
- Department of Social and Behavioural Sciences (CPUP), University of Porto, Porto, Portugal.,Maia University Institute (ISMAI), Maia, Portugal
| | - Fernando Barbosa
- Laboratory of Neuropsychophysiology, Faculty of Psychology and Educational Sciences, University of Porto, Rua Alfredo Allen, 535, 4200-135, Porto, Portugal
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Ding X, Salmeron BJ, Wang J, Yang Y, Stein EA, Ross TJ. Evidence of subgroups in smokers as revealed in clinical measures and evaluated by neuroimaging data: a preliminary study. Addict Biol 2019. [PMID: 29516603 DOI: 10.1111/adb.12620] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
To date, fractionation of the nicotine addiction phenotype has been limited to that based primarily on characteristics of cigarette use, although it is widely appreciated that a variety of individual factors are associated with tobacco use disorder. Identifying subtypes of tobacco use disorder based on such factors may lead to better understanding of potential treatment targets, individualize treatments and improve outcomes. In this preliminary study, to identify potential subgroups, we applied hierarchical clustering to a broad range of assessments measuring personality, IQ and psychiatric symptoms, as well as various environmental and experiential characteristics from 102 otherwise healthy cigarette smokers. The identified subgroups were further compared on various resting-state fMRI measures from a subset (N = 65) of individuals who also underwent resting-state fMRI scanning. The clustering dendrogram indicated that smokers can be divided into three subgroups. Each subgroup had unique clinical assessment characteristics. The division yielded imaging differences between subgroups in the supplementary motor area/middle cingulate cortex and the cuneus. Regression analyses showed that amplitude of low frequency fluctuations in the supplementary motor area/middle cingulate cortex differed between groups and were negatively correlated with the Toronto Alexithymia Scale subscale Difficulty Describing Feelings.
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Affiliation(s)
- Xiaoyu Ding
- Neuroimaging Research Branch, Intramural Research ProgramNational Institute on Drug Abuse, National Institutes of Health Baltimore MD USA
| | - Betty Jo Salmeron
- Neuroimaging Research Branch, Intramural Research ProgramNational Institute on Drug Abuse, National Institutes of Health Baltimore MD USA
| | - Jamei Wang
- Department of Biomedical EngineeringCarnegie Mellon University Pittsburgh PA USA
| | - Yihong Yang
- Neuroimaging Research Branch, Intramural Research ProgramNational Institute on Drug Abuse, National Institutes of Health Baltimore MD USA
| | - Elliot A. Stein
- Neuroimaging Research Branch, Intramural Research ProgramNational Institute on Drug Abuse, National Institutes of Health Baltimore MD USA
| | - Thomas J. Ross
- Neuroimaging Research Branch, Intramural Research ProgramNational Institute on Drug Abuse, National Institutes of Health Baltimore MD USA
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41
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Quattrone D, Di Forti M, Gayer-Anderson C, Ferraro L, Jongsma HE, Tripoli G, La Cascia C, La Barbera D, Tarricone I, Berardi D, Szöke A, Arango C, Lasalvia A, Tortelli A, Llorca PM, de Haan L, Velthorst E, Bobes J, Bernardo M, Sanjuán J, Santos JL, Arrojo M, Del-Ben CM, Menezes PR, Selten JP, Jones PB, Kirkbride JB, Richards AL, O'Donovan MC, Sham PC, Vassos E, Rutten BPF, van Os J, Morgan C, Lewis CM, Murray RM, Reininghaus U. Transdiagnostic dimensions of psychopathology at first episode psychosis: findings from the multinational EU-GEI study. Psychol Med 2019; 49:1378-1391. [PMID: 30282569 PMCID: PMC6518388 DOI: 10.1017/s0033291718002131] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Revised: 07/01/2018] [Accepted: 07/24/2018] [Indexed: 12/31/2022]
Abstract
BACKGROUND The value of the nosological distinction between non-affective and affective psychosis has frequently been challenged. We aimed to investigate the transdiagnostic dimensional structure and associated characteristics of psychopathology at First Episode Psychosis (FEP). Regardless of diagnostic categories, we expected that positive symptoms occurred more frequently in ethnic minority groups and in more densely populated environments, and that negative symptoms were associated with indices of neurodevelopmental impairment. METHOD This study included 2182 FEP individuals recruited across six countries, as part of the EUropean network of national schizophrenia networks studying Gene-Environment Interactions (EU-GEI) study. Symptom ratings were analysed using multidimensional item response modelling in Mplus to estimate five theory-based models of psychosis. We used multiple regression models to examine demographic and context factors associated with symptom dimensions. RESULTS A bifactor model, composed of one general factor and five specific dimensions of positive, negative, disorganization, manic and depressive symptoms, best-represented associations among ratings of psychotic symptoms. Positive symptoms were more common in ethnic minority groups. Urbanicity was associated with a higher score on the general factor. Men presented with more negative and less depressive symptoms than women. Early age-at-first-contact with psychiatric services was associated with higher scores on negative, disorganized, and manic symptom dimensions. CONCLUSIONS Our results suggest that the bifactor model of psychopathology holds across diagnostic categories of non-affective and affective psychosis at FEP, and demographic and context determinants map onto general and specific symptom dimensions. These findings have implications for tailoring symptom-specific treatments and inform research into the mood-psychosis spectrum.
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Affiliation(s)
- Diego Quattrone
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, UK
| | - Marta Di Forti
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, UK
| | - Charlotte Gayer-Anderson
- Department of Health Service and Population Research, Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK
| | - Laura Ferraro
- Department of Experimental Biomedicine and Clinical Neuroscience, University of Palermo, Via G. La Loggia 1, 90129 Palermo, Italy
| | - Hannah E Jongsma
- Department of Psychiatry, University of Cambridge, Herchel Smith Building for Brain & Mind Sciences, Forvie Site, Robinson Way, Cambridge, CB2 0SZ, UK
| | - Giada Tripoli
- Department of Psychosis Studies, Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK
| | - Caterina La Cascia
- Department of Experimental Biomedicine and Clinical Neuroscience, University of Palermo, Via G. La Loggia 1, 90129 Palermo, Italy
| | - Daniele La Barbera
- Department of Experimental Biomedicine and Clinical Neuroscience, University of Palermo, Via G. La Loggia 1, 90129 Palermo, Italy
| | - Ilaria Tarricone
- Department of Medical and Surgical Science, Psychiatry Unit, Alma Mater Studiorum Università di Bologna, Viale Pepoli 5, 40126 Bologna, Italy
| | - Domenico Berardi
- Department of Medical and Surgical Science, Psychiatry Unit, Alma Mater Studiorum Università di Bologna, Viale Pepoli 5, 40126 Bologna, Italy
| | - Andrei Szöke
- INSERM, U955, Equipe 15, 51 Avenue de Maréchal de Lattre de Tassigny, 94010 Créteil, France
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM (CIBERSAM), C/Doctor Esquerdo 46, 28007 Madrid, Spain
| | - Antonio Lasalvia
- Section of Psychiatry, Azienda Ospedaliera Universitaria Integrata di Verona, Piazzale L.A. Scuro 10, 37134 Verona, Italy
| | - Andrea Tortelli
- Etablissement Public de Santé Maison Blanche, Paris 75020, France
| | | | - Lieuwe de Haan
- Department of Psychiatry, Early Psychosis Section, Academic Medical Centre, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands
| | - Eva Velthorst
- Department of Psychiatry, Early Psychosis Section, Academic Medical Centre, University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands
| | - Julio Bobes
- Department of Medicine, Psychiatry Area, School of Medicine, Universidad de Oviedo, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), C/Julián Clavería s/n, 33006 Oviedo, Spain
| | - Miguel Bernardo
- Barcelona Clinic Schizophrenia Unit, Neuroscience Institute, Hospital clinic, Department of Medicine, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
| | - Julio Sanjuán
- Department of Psychiatry, School of Medicine, Universidad de Valencia, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), C/Avda. Blasco Ibáñez 15, 46010 Valencia, Spain
| | - Jose Luis Santos
- Department of Psychiatry, Servicio de Psiquiatría Hospital “Virgen de la Luz”, C/Hermandad de Donantes de Sangre, 16002 Cuenca, Spain
| | - Manuel Arrojo
- Department of Psychiatry, Psychiatric Genetic Group, Instituto de Investigación Sanitaria de Santiago de Compostela, Complejo Hospitalario Universitario de Santiago de Compostela, Spain
| | - Cristina Marta Del-Ben
- Division of Psychiatry, Department of Neuroscience and Behaviour, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil
| | - Paulo Rossi Menezes
- Department of Preventative Medicine, Faculdade de Medicina FMUSP, University of São Paulo, São Paulo, Brazil
| | - Jean-Paul Selten
- Rivierduinen Institute for Mental Health Care, Sandifortdreef 19, 2333 ZZ Leiden, The Netherlands
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, South Limburg Mental Health Research and Teaching Network, Maastricht University Medical Centre, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | | | - Peter B Jones
- Department of Psychiatry, University of Cambridge, Herchel Smith Building for Brain & Mind Sciences, Forvie Site, Robinson Way, Cambridge, CB2 0SZ, UK
- CAMEO Early Intervention Service, Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, CB21 5EF, UK
| | - James B Kirkbride
- Psylife Group, Division of Psychiatry, University College London, 6th Floor, Maple House, 149 Tottenham Court Road, London W1T 7NF, UK
| | - Alexander L Richards
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff CF24 4HQ, UK
| | - Michael C O'Donovan
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff CF24 4HQ, UK
| | - Pak C Sham
- Department of Psychiatry, the University of Hong Kong, Hong Kong, China
- Centre for Genomic Sciences, Li KaShing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Evangelos Vassos
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Bart PF Rutten
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, South Limburg Mental Health Research and Teaching Network, Maastricht University Medical Centre, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Jim van Os
- Department of Psychosis Studies, Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, South Limburg Mental Health Research and Teaching Network, Maastricht University Medical Centre, P.O. Box 616, 6200 MD Maastricht, The Netherlands
- Brain Centre Rudolf Magnus, Utrecht University Medical Centre, Utrecht, The Netherlands
| | - Craig Morgan
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, UK
- Department of Health Service and Population Research, Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Robin M Murray
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, UK
- Department of Psychosis Studies, Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK
| | - Ulrich Reininghaus
- Department of Health Service and Population Research, Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, South Limburg Mental Health Research and Teaching Network, Maastricht University Medical Centre, P.O. Box 616, 6200 MD Maastricht, The Netherlands
- Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
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Connectal coding: discovering the structures linking cognitive phenotypes to individual histories. Curr Opin Neurobiol 2019; 55:199-212. [PMID: 31102987 DOI: 10.1016/j.conb.2019.04.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 04/14/2019] [Accepted: 04/16/2019] [Indexed: 01/06/2023]
Abstract
Cognitive phenotypes characterize our memories, beliefs, skills, and preferences, and arise from our ancestral, developmental, and experiential histories. These histories are written into our brain structure through the building and modification of various brain circuits. Connectal coding, by way of analogy with neural coding, is the art, study, and practice of identifying the network structures that link cognitive phenomena to individual histories. We propose a formal statistical framework for connectal coding and demonstrate its utility in several applications spanning experimental modalities and phylogeny.
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43
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Bathelt J, Johnson A, Zhang M, Astle DE. The cingulum as a marker of individual differences in neurocognitive development. Sci Rep 2019; 9:2281. [PMID: 30783161 PMCID: PMC6381161 DOI: 10.1038/s41598-019-38894-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 01/11/2019] [Indexed: 01/21/2023] Open
Abstract
The canonical approach to exploring brain-behaviour relationships is to group individuals according to a phenotype of interest, and then explore the neural correlates of this grouping. A limitation of this approach is that multiple aetiological pathways could result in a similar phenotype, so the role of any one brain mechanism may be substantially underestimated. Building on advances in network analysis, we used a data-driven community-clustering algorithm to identify robust subgroups based on white-matter microstructure in childhood and adolescence (total N = 313, mean age: 11.24 years). The algorithm indicated the presence of two equal-size groups that show a critical difference in fractional anisotropy (FA) of the left and right cingulum. Applying the brain-based grouping in independent samples, we find that these different 'brain types' had profoundly different cognitive abilities with higher performance in the higher FA group. Further, a connectomics analysis indicated reduced structural connectivity in the low FA subgroup that was strongly related to reduced functional activation of the default mode network. These results provide a proof-of-concept that bottom-up brain-based groupings can be identified that relate to cognitive performance. This provides a first demonstration of a complimentary approach for investigating individual differences in brain structure and function, particularly for neurodevelopmental disorders where researchers are often faced with phenotypes that are difficult to define at the cognitive or behavioural level.
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Affiliation(s)
- Joe Bathelt
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.
| | - Amy Johnson
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Mengya Zhang
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Duncan E Astle
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
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44
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Cuthbert BN. The PRISM project: Social withdrawal from an RDoC perspective. Neurosci Biobehav Rev 2019; 97:34-37. [DOI: 10.1016/j.neubiorev.2018.08.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 08/02/2018] [Accepted: 08/10/2018] [Indexed: 11/26/2022]
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Lee Y, Ragguett RM, Mansur RB, Boutilier JJ, Rosenblat JD, Trevizol A, Brietzke E, Lin K, Pan Z, Subramaniapillai M, Chan TCY, Fus D, Park C, Musial N, Zuckerman H, Chen VCH, Ho R, Rong C, McIntyre RS. Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review. J Affect Disord 2018; 241:519-532. [PMID: 30153635 DOI: 10.1016/j.jad.2018.08.073] [Citation(s) in RCA: 145] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 07/12/2018] [Accepted: 08/12/2018] [Indexed: 02/07/2023]
Abstract
BACKGROUND No previous study has comprehensively reviewed the application of machine learning algorithms in mood disorders populations. Herein, we qualitatively and quantitatively evaluate previous studies of machine learning-devised models that predict therapeutic outcomes in mood disorders populations. METHODS We searched Ovid MEDLINE/PubMed from inception to February 8, 2018 for relevant studies that included adults with bipolar or unipolar depression; assessed therapeutic outcomes with a pharmacological, neuromodulatory, or manual-based psychotherapeutic intervention for depression; applied a machine learning algorithm; and reported predictors of therapeutic response. A random-effects meta-analysis of proportions and meta-regression analyses were conducted. RESULTS We identified 639 records: 75 full-text publications were assessed for eligibility; 26 studies (n=17,499) and 20 studies (n=6325) were included in qualitative and quantitative review, respectively. Classification algorithms were able to predict therapeutic outcomes with an overall accuracy of 0.82 (95% confidence interval [CI] of [0.77, 0.87]). Pooled estimates of classification accuracy were significantly greater (p < 0.01) in models informed by multiple data types (e.g., composite of phenomenological patient features and neuroimaging or peripheral gene expression data; pooled proportion [95% CI] = 0.93[0.86, 0.97]) when compared to models with lower-dimension data types (pooledproportion=0.68[0.62,0.74]to0.85[0.81,0.88]). LIMITATIONS Most studies were retrospective; differences in machine learning algorithms and their implementation (e.g., cross-validation, hyperparameter tuning); cannot infer importance of individual variables fed into learning algorithm. CONCLUSIONS Machine learning algorithms provide a powerful conceptual and analytic framework capable of integrating multiple data types and sources. An integrative approach may more effectively model neurobiological components as functional modules of pathophysiology embedded within the complex, social dynamics that influence the phenomenology of mental disorders.
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Affiliation(s)
- Yena Lee
- Institute of Medical Science, University of Toronto, Toronto, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Renee-Marie Ragguett
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Rodrigo B Mansur
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Justin J Boutilier
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
| | - Joshua D Rosenblat
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Alisson Trevizol
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada
| | - Elisa Brietzke
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Department of Psychiatry, Federal University of Sao Paulo, Sao Paulo, Brazil
| | - Kangguang Lin
- Laboratory of Emotion and Cognition, Department of Affective Disorders, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Department of Neuropsychology, University of Hong Kong, Hong Kong, China
| | - Zihang Pan
- Institute of Medical Science, University of Toronto, Toronto, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Mehala Subramaniapillai
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Timothy C Y Chan
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
| | - Dominika Fus
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Caroline Park
- Institute of Medical Science, University of Toronto, Toronto, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Natalie Musial
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Hannah Zuckerman
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Vincent Chin-Hung Chen
- School of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Roger Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Carola Rong
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Roger S McIntyre
- Institute of Medical Science, University of Toronto, Toronto, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Pharmacology, University of Toronto, Toronto, Canada.
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Dean DJ, Walther S, Bernard JA, Mittal VA. Motor clusters reveal differences in risk for psychosis, cognitive functioning, and thalamocortical connectivity: evidence for vulnerability subtypes. Clin Psychol Sci 2018; 6:721-734. [PMID: 30319928 PMCID: PMC6178957 DOI: 10.1177/2167702618773759] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Abnormal development of parallel cortical-striatal networks may contribute to abnormal motor, cognitive, and affective behavior prior to the onset of psychosis. Partitioning individuals at clinical high-risk (CHR) using motor behavior may provide a novel perspective on different etiological pathways or patient subtypes. A K-means cluster analysis was conducted in CHR (N=69; 42% female, mean age=18.67 years) young adults using theoretically distinct measures of motor behavior. The resulting subtypes were then compared on positive and negative symptoms at baseline, and 2-year risk of psychosis conversion. CHR participants were followed for 2 years to determine conversion to psychosis. CHR subtypes and healthy controls (N=61; 57% female, mean age=18.58 years) were compared on multiple cognitive domains and cortical-striatal connectivity. Results suggest 3 vulnerability subtypes of CHR individuals with different profiles of motor performance, symptoms, risk for conversion to psychosis, cognition, and thalamocortical connectivity. This approach may reflect a novel strategy for promoting tailored risk assessment as well as future research developing individualized medicine.
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Affiliation(s)
- Derek J. Dean
- University of Colorado Boulder, Department of Psychology and Neuroscience, Boulder, CO, USA
- University of Colorado Boulder, Center for Neuroscience, Boulder, CO, USA
| | - Sebastian Walther
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Jessica A. Bernard
- Texas A&M University, Department of Psychological and Brain Sciences, College Station, TX, USA
- Texas A&M University, Institute for Neuroscience, College Station, TX, USA
| | - Vijay A. Mittal
- Northwestern University, Department of Psychology, Evanston, IL, USA
- Northwestern University, Department of Psychiatry, Chicago IL, USA
- Northwestern University, Institute for Policy Research, Evanston, IL, USA
- Northwestern University, Medical Social Sciences, Chicago, IL, USA
- Institute for Innovations in Developmental Sciences, Evanston/Chicago, IL, USA
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Zhao X, Rangaprakash D, Yuan B, Denney TS, Katz JS, Dretsch MN, Deshpande G. Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning. FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS 2018; 4:25. [PMID: 30393630 PMCID: PMC6214192 DOI: 10.3389/fams.2018.00025] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Many brain-based disorders are traditionally diagnosed based on clinical interviews and behavioral assessments, which are recognized to be largely imperfect. Therefore, it is necessary to establish neuroimaging-based biomarkers to improve diagnostic precision. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising technique for the characterization and classification of varying disorders. However, most of these classification methods are supervised, i.e., they require a priori clinical labels to guide classification. In this study, we adopted various unsupervised clustering methods using static and dynamic rs-fMRI connectivity measures to investigate whether the clinical diagnostic grouping of different disorders is grounded in underlying neurobiological and phenotypic clusters. In order to do so, we derived a general analysis pipeline for identifying different brain-based disorders using genetic algorithm-based feature selection, and unsupervised clustering methods on four different datasets; three of them-ADNI, ADHD-200, and ABIDE-which are publicly available, and a fourth one-PTSD and PCS-which was acquired in-house. Using these datasets, the effectiveness of the proposed pipeline was verified on different disorders: Attention Deficit Hyperactivity Disorder (ADHD), Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), Post-Traumatic Stress Disorder (PTSD), and Post-Concussion Syndrome (PCS). For ADHD and AD, highest similarity was achieved between connectivity and phenotypic clusters, whereas for ASD and PTSD/PCS, highest similarity was achieved between connectivity and clinical diagnostic clusters. For multi-site data (ABIDE and ADHD-200), we report site-specific results. We also reported the effect of elimination of outlier subjects for all four datasets. Overall, our results suggest that neurobiological and phenotypic biomarkers could potentially be used as an aid by the clinician, in additional to currently available clinical diagnostic standards, to improve diagnostic precision. Data and source code used in this work is publicly available at https://github.com/xinyuzhao/identification-of-brain-based-disorders.git.
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Affiliation(s)
- Xinyu Zhao
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Quora, Inc., Mountain View, CA, United States
| | - D. Rangaprakash
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Bowen Yuan
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
| | - Thomas S. Denney
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University, University of Alabama at Birmingham, Birmingham, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Jeffrey S. Katz
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University, University of Alabama at Birmingham, Birmingham, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Michael N. Dretsch
- Human Dimension Division, HQ TRADOC, Fort Eustis, VA, United States
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University, University of Alabama at Birmingham, Birmingham, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States
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Early Cannabis Use and Neurocognitive Risk: A Prospective Functional Neuroimaging Study. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:713-725. [PMID: 30033100 DOI: 10.1016/j.bpsc.2018.05.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 04/19/2018] [Accepted: 05/09/2018] [Indexed: 11/22/2022]
Abstract
BACKGROUND Retrospective neuroimaging studies have suggested an association between early cannabis onset and later neurocognitive impairment. However, these studies have been limited in their ability to distinguish substance use risk factors from cannabis-induced effects on neurocognition. We used a prospective cohort design to test whether neurocognitive differences preceded cannabis onset (substance use risk model) and if early cannabis use was associated with poorer neurocognitive development (cannabis exposure model). METHODS Participants (N = 85) completed a visuospatial working memory task during functional magnetic resonance imaging and multiple cognitive assessments (Wechsler Intelligence Scale for Children-IV, Cambridge Neuropsychological Test Automated Battery) at 12 years of age, before any reported cannabis use (baseline), and at 15 years of age (follow-up: N = 85 cognitive assessments, n = 67 neuroimaging). By follow-up, 22 participants reported using cannabis and/or failed a Δ9-tetrahydrocannabinol urine screen (users). RESULTS At baseline, group differences supported a risk model. Those who would initiate cannabis use by 15 years of age had activation differences in frontoparietal (increased) and visual association (decreased) regions and poorer executive planning scores (Stockings of Cambridge) compared with noninitiators. Limited support was found for a cannabis exposure model. At follow-up, activation in the cuneus displayed a significant cannabis dose-response relationship, although neither cannabis dose nor cuneus activation was associated with cognitive performance. CONCLUSIONS The purported neurocognitive effects of early cannabis onset may not be due to cannabis initiation alone but also driven by limitations or late development of neurocognitive systems predictive of substance use. In addition, more prolonged cannabis exposure may be required to observe the cognitive effects of early cannabis onset.
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An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci Data 2017; 4:170181. [PMID: 29257126 PMCID: PMC5735921 DOI: 10.1038/sdata.2017.181] [Citation(s) in RCA: 271] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Accepted: 10/11/2017] [Indexed: 11/23/2022] Open
Abstract
Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5–21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n=664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).
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Goldberg JF, Rush AJ. Addressing the unmet needs of current antidepressants: does neuroscience help or hinder clinical psychopharmacology research? Expert Opin Pharmacother 2017; 18:1417-1420. [PMID: 28780896 DOI: 10.1080/14656566.2017.1363178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Joseph F Goldberg
- a Department of Psychiatry , Icahn School of Medicine at Mount Sinai , New York , NY , USA
| | - A John Rush
- b Department of Psychiatry and Behavioral Sciences , Duke-National University of Singapore , Singapore.,c Department of Psychiatry , Duke University Medical School , Durham , NC , USA
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