title: Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.
abstract: Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to address this question. Using a recently identified neural signature of guilt-selective functional disconnection, the classification algorithm was able to distinguish remitted MD from control participants with 78.3% accuracy. This demonstrates the high potential of our fMRI signature as a biomarker of MD vulnerability.
authors.affiliation: Center for Mathematics, Computation, and Cognition, Universidade Federal do ABC, Bangu, Santo André 09020-040, Brazil; Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro 22281-100, Brazil.
authors.forename: João R
authors.initials: JR
authors.lastname: Sato
authors.affiliation: Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro 22281-100, Brazil.
authors.forename: Jorge
authors.initials: J
authors.lastname: Moll
authors.affiliation: The University of Manchester & Manchester Academic Health Sciences Centre, School of Psychological Sciences, Neuroscience and Aphasia Research Unit, Manchester M13 9PL, UK.
authors.forename: Sophie
authors.initials: S
authors.lastname: Green
authors.affiliation: The University of Manchester & Manchester Academic Health Sciences Centre, Institute of Brain, Behaviour and Mental Health, Neuroscience & Psychiatry Unit, Manchester M13 9PL, UK.
authors.forename: John F W
authors.initials: JF
authors.lastname: Deakin
authors.affiliation: Department of Electrical Engineering, Centro Universitario da FEI, Sao Bernardo do Campo 3972, Brazil.
authors.forename: Carlos E
authors.initials: CE
authors.lastname: Thomaz
authors.affiliation: Institute of Psychiatry, Psychology, and Neuroscience, King's College London, Department of Psychological Medicine, Centre for Affective Disorders, London SE5 8AZ, UK. Electronic address: roland.zahn@kcl.ac.uk.
authors.forename: Roland
authors.initials: R
authors.lastname: Zahn
mesh_terms: D000328:Adult; D000465:Algorithms; D015415:Biomarkers; D016022:Case-Control Studies; D003865:Major Depressive Disorder / Q000175:diagnosis* / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted; D017352:Echo-Planar Imaging / Q000379:methods*; D005260:Female; D006167:Guilt; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009415:Nerve Net / Q000503:physiopathology; D012016:Reference Values; D012680:Sensitivity and Specificity; D013702:Temporal Lobe / Q000503:physiopathology
chemical_list: D015415:Biomarkers
keywords: Anterior temporal lobe; Major depressive disorder; Self-blame