Classification of major depressive disorder using vertex-wise brain sulcal depth, curvature, and thickness with a deep and a shallow learning model.
Authors
Affiliations (60)
Affiliations (60)
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Georg-August University, Göttingen, Germany. [email protected].
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Georg-August University, Göttingen, Germany.
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China.
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, 90274, USA.
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
- Department of Psychiatry & Mental Health, Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
- Department of Psychiatry and Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA.
- Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy.
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.
- Department of Psychiatry and Psychotherapy, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany.
- Institute for Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany.
- MOODS Team, CESP, INSERM U1018, Faculté de Médecine, Univ Paris-Saclay, Le Kremlin Bicêtre, 94275, France.
- Service Hospitalo-Universitaire de Psychiatrie de Bicêtre, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, Hôpital de Bicêtre, Le Kremlin Bicêtre, F-94275, France.
- Department of Biomedical Sciences, Florida State University, Tallahassee, FL, USA.
- Sorbonne University, Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France.
- Institute for Molecular Bioscience, the University of Queensland, St Lucia, QLD, Australia.
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, the University of Melbourne, Parkville, VIC, Australia.
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands.
- Experimental Therapeutics and Pathophysiology Branch, National Institute for Mental Health, National Institutes of Health, Bethesda, MD, USA.
- FIDMAG Germanes Hospitalàries Research Foundation, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Barcelona, Catalonia, Spain.
- SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey.
- Department of Psychology, Stanford University, Stanford, CA, USA.
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
- Centre for Youth Mental Health, the University of Melbourne, Parkville, VIC, Australia.
- Orygen, Parkville, VIC, Australia.
- Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
- Department of Psychology, University of California, Los Angeles, CA, USA.
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA.
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK.
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK.
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK.
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, 6229 ER, the Netherlands.
- Departments of Psychiatry and Pediatrics, University of Calgary, Calgary, AB, Canada.
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany.
- Institute for Translational Neuroscience, University of Münster, Münster, Germany.
- Center Of Excellence on Mood Disorders, Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences at McGovern Medical School, the University of Texas Health Science Center at Houston, Houston, TX, USA.
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA.
- Center for Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan.
- GGZ inGeest Mental Health Care, Amsterdam, the Netherlands.
- Sant Pau Mental Health Research Group, Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau, Barcelona, Catalonia, Spain.
- CIBERSAM, Madrid, Spain.
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Amsterdam University Medical Centers, location AMC, Department of Radiology and Nuclear Medicine, Amsterdam, the Netherlands.
- West Region, Institute of Mental Health, Singapore, Singapore.
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Scotland, UK.
- Developmental and Educational Psychology, Leiden University, Leiden, the Netherlands.
- Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, Rotterdam, the Netherlands.
- Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain.
- Intelligent Data Analysis Laboratory (IDAL), Department of Electronic Engineering, Universitat de València, Valencia, Spain.
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
- Queensland Brain Institute, the University of Queensland, Brisbane, QLD, Australia.
- Centre for Advanced Imaging, the University of Queensland, Brisbane, QLD, Australia.
- Section on the Neurobiology and Treatment of Mood Disorders, National Institute of Mental Health, Bethesda, MD, USA.
Abstract
Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing 7012 participants from 31 sites (N = 2772 MDD and N = 4240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible. Future studies are needed to determine whether more sophisticated integration of information from other MRI modalities such as fMRI and DWI will lead to a higher performance in this diagnostic task.