title: Studying depression using imaging and machine learning methods.
abstract: Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.
authors.affiliation: Department of Bioengineering, University of Pittsburgh, PA, USA.
authors.forename: Meenal J
authors.initials: MJ
authors.lastname: Patel
authors.affiliation: University of Pittsburgh School of Medicine, PA, USA.
authors.forename: Alexander
authors.initials: A
authors.lastname: Khalaf
authors.affiliation: Department of Bioengineering, University of Pittsburgh, PA, USA; University of Pittsburgh School of Medicine, PA, USA; Department of Psychiatry, University of Pittsburgh School of Medicine, PA, USA.
authors.forename: Howard J
authors.initials: HJ
authors.lastname: Aizenstein
mesh_terms: D001921:Brain / Q000473:pathology* / Q000503:physiopathology*; D003865:Major Depressive Disorder / Q000175:diagnosis* / Q000628:therapy*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods
keywords: Depression; Machine learning; Prediction; Review; Treatment