Depression recognition using resting-state and event-related fMRI signals

Abstract

PurposeThis paper aimed to develop a method for depression detection using blood-oxygen-level-dependent (BOLD) response estimated from event-related signals and resting-state functional magnetic resonance imaging (fMRI) signals together. Materials and MethodsThirteen patients with unipolar depression and matched healthy subjects were recruited. Resting state data of each subject were collected. Thereafter, event-related paradigm was undertaken using sad facial stimuli. The resting-state fMRI signal was deemed as the baseline of each subject's activity. Coefficient marks were designed to sort and select temporal independent components of event-related signals. Thereafter, stimulus-evoked BOLD response components inside event-related signal were extracted and taken as features to discriminate depressive patients from healthy controls. ResultsAccuracy rate for depression recognition was 77.27% with P value of .017 for whole-brain analysis and 81.82% with P value of .009 for region-of-interest analysis. The effectiveness and the superiority of the proposed method for disease recognition were demonstrated via the performance comparison with three other typical methods. ConclusionsThe proposed model was effective in depression recognition.