title: Patient classification as an outlier detection problem: an application of the One-Class Support Vector Machine.
abstract: Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC-SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i.e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region-based features the OC-SVM classified 52% of patients as outliers. However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers.
authors: Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College London, London, UK. J.Mourao-Miranda@cs.ucl.ac.uk
authors: Janaina
authors: J
authors: Mourão-Miranda
authors: David R
authors: DR
authors: Hardoon
authors: Tim
authors: T
authors: Hahn
authors: Andre F
authors: AF
authors: Marquand
authors: Steve C R
authors: SC
authors: Williams
authors: John
authors: J
authors: Shawe-Taylor
authors: Michael
authors: M
authors: Brammer
mesh_terms: D000328:Adult; D003863:Depression / Q000145:classification / Q000175:diagnosis*; D004644:Emotions / Q000502:physiology; D005149:Facial Expression; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D060388:Support Vector Machine