Valeria Kebets1, Avram J Holmes2, Csaba Orban3, Siyi Tang4, Jingwei Li5, Nanbo Sun5, Ru Kong5, Russell A Poldrack6, B T Thomas Yeo7. 1. 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. 2. 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. 3. 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. 4. 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. 5. Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore. 6. Department of Psychology, Stanford University, Stanford, California. 7. 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. Electronic address: thomas.yeo@nus.edu.sg.
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.
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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