1. Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder
- Author
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Schwarz, E., Doan, N. T., Pergola, G., Westlye, L. T., Kaufmann, T., Wolfers, T., Brecheisen, R., Quarto, T., Ing, A. J., Di Carlo, P., Gurholt, T. P., Harms, R. L., Noirhomme, Q., Moberget, T., Agartz, I., Andreassen, O. A., Bellani, M., Bertolino, A., Blasi, G., Brambilla, P., Buitelaar, J. K., Cervenka, S., Flyckt, L., Frangou, S., Franke, B., Hall, J., Heslenfeld, D. J., Kirsch, P., Mcintosh, A. M., Nothen, M. M., Papassotiropoulos, A., de Quervain, D. J. -F., Rietschel, M., Schumann, G., Tost, H., Witt, S. H., Zink, M., Meyer-Lindenberg, A., Bettella, F., Brandt, C. L., Clarke, T. -K., Coynel, D., Degenhardt, F., Djurovic, S., Eisenacher, S., Fastenrath, M., Fatouros-Bergman, H., Forstner, A. J., Frank, J., Gambi, F., Gelao, B., Geschwind, L., Di Giannantonio, M., Di Giorgio, A., Hartman, C. A., Heilmann-Heimbach, S., Herms, S., Hoekstra, P. J., Hoffmann, P., Hoogman, M., Jonsson, E. G., Loos, E., Maggioni, E., Oosterlaan, J., Papalino, M., Rampino, A., Romaniuk, L., Selvaggi, P., Sepede, G., Sonderby, I. E., Spalek, K., Sussmann, J. E., Thompson, P. M., Vasquez, A. A., Vogler, C., Whalley, H., Farde, L., Engberg, G., Erhardt, S., Schwieler, L., Collste, K., Victorsson, P., Malmqvist, A., Hedberg, M., Orhan, F., Cognitive Psychology, IBBA, Behavioural Sciences, Elvira Brattico / Principal Investigator, Department of Psychology and Logopedics, Cognitive Brain Research Unit, Faculty of Medicine, University of Helsinki, General Paediatrics, ARD - Amsterdam Reproduction and Development, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Clinical Cognitive Neuropsychiatry Research Program (CCNP), Multiscale Imaging of Brain Connectivity, RS: FPN CN 11, Vision, and RS: FPN CN 1
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0301 basic medicine ,Male ,Multivariate statistics ,Bipolar Disorder ,SEGMENTATION ,3124 Neurology and psychiatry ,Machine Learning ,0302 clinical medicine ,DEFICITS ,Gray Matter ,Psychiatry ,RISK ,medicine.diagnostic_test ,220 Statistical Imaging Neuroscience ,LIKELIHOOD ESTIMATION ,Middle Aged ,MRI SCANS ,Magnetic Resonance Imaging ,Justice and Strong Institutions ,3. Good health ,Psychiatry and Mental health ,medicine.anatomical_structure ,bipolar disorders ,Schizophrenia ,Female ,brain structural patterns ,MRI ,Adult ,SDG 16 - Peace ,Adolescent ,Brain Structure and Function ,Grey matter ,Psykiatri ,CLASSIFICATION ,Article ,lcsh:RC321-571 ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,Young Adult ,Text mining ,medicine ,Humans ,Bipolar disorder ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,Biological Psychiatry ,METAANALYSIS ,schizophrenia ,grey matter alterations ,Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7] ,business.industry ,1ST-EPISODE ,SDG 16 - Peace, Justice and Strong Institutions ,Magnetic resonance imaging ,medicine.disease ,030104 developmental biology ,Sample size determination ,Attention Deficit Disorder with Hyperactivity ,Case-Control Studies ,VOLUME ,business ,Neuroscience ,030217 neurology & neurosurgery - Abstract
Contains fulltext : 202693.pdf (Publisher’s version ) (Open Access) Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.
- Published
- 2019