Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model.
Journal: ArXiv
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Affiliated Institutions:
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 , China.
Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA , 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. , Marburg, Germany.
Institute for Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany.
MOODS Team, CESP, INSERM U, Faculté de Médecine, Univ Paris-Saclay, Le Kremlin Bicêtre , 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-, Paris, France.
Institute for Translational Psychiatry, University of Münster, Münster, Germany.
Melbourne Neuropsychiatry Centre, Department of Psychiatry, the University of Melbourne, Parkville, Victoria, Australia.
Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam Public Health Research Institute, Amsterdam, 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.
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 Minnesota, Minneapolis, MN, USA.
Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK.
Departments of Psychiatry and Pediatrics, University of Calgary, Calgary, AB, Canada.
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, 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.
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.
Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Scotland, UK.
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.
Clinical Affective Neuroimaging Laboratory, Leibniz Institute for Neurobiology, Magdeburg, Germany.
Section on the Neurobiology and Treatment of Mood Disorders, National Institute of Mental Health, Bethesda, MD, USA.
Abstract summary
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 7,012 participants from 30 sites (N=2,772 MDD and N=4,240 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.
Authors & Co-authors:
Goya-Maldonado Roberto R
Erwin-Grabner Tracy T
Zeng Ling-Li LL
Ching Christopher R K CRK
Aleman Andre A
Amod Alyssa R AR
Basgoze Zeynep Z
Benedetti Francesco F
Besteher Bianca B
Brosch Katharina K
Bülow Robin R
Colle Romain R
Connolly Colm G CG
Corruble Emmanuelle E
Couvy-Duchesne Baptiste B
Cullen Kathryn K
Dannlowski Udo U
Davey Christopher G CG
Dols Annemiek A
Ernsting Jan J
Evans Jennifer W JW
Fisch Lukas L
Fuentes-Claramonte Paola P
Gonul Ali Saffet AS
Gotlib Ian H IH
Grabe Hans J HJ
Groenewold Nynke A NA
Grotegerd Dominik D
Hahn Tim T
Hamilton J Paul JP
Han Laura K M LKM
Harrison Ben J BJ
Ho Tiffany C TC
Jahanshad Neda N
Jamieson Alec J AJ
Karuk Andriana A
Kircher Tilo T
Klimes-Dougan Bonnie B
Koopowitz Sheri-Michelle SM
Lancaster Thomas T
Leenings Ramona R
Li Meng M
Linden David E J DEJ
MacMaster Frank P FP
Mehler David M A DMA
Meinert Susanne S
Melloni Elisa E
Mueller Bryon A BA
Mwangi Benson B
Nenadić Igor I
Ojha Amar A
Okamoto Yasumasa Y
Oudega Mardien L ML
Penninx Brenda W J H BWJH
Poletti Sara S
Pomarol-Clotet Edith E
Portella Maria J MJ
Pozzi Elena E
Radua Joaquim J
Rodríguez-Cano Elena E
Sacchet Matthew D MD
Salvador Raymond R
Schrantee Anouk A
Sim Kang K
Soares Jair C JC
Solanes Aleix A
Stein Dan J DJ
Stein Frederike F
Stolicyn Aleks A
Thomopoulos Sophia I SI
Toenders Yara J YJ
Uyar-Demir Aslihan A
Vieta Eduard E
Vives-Gilabert Yolanda Y
Völzke Henry H
Walter Martin M
Whalley Heather C HC
Whittle Sarah S
Winter Nils N
Wittfeld Katharina K
Wright Margaret J MJ
Wu Mon-Ju MJ
Yang Tony T TT
Zarate Carlos C
Veltman Dick J DJ
Schmaal Lianne L
Thompson Paul M PM
Study Outcome
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