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

Abstract

Purpose: This 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 methods: Thirteen 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.

Results: Accuracy 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.

Conclusions: The proposed model was effective in depression recognition.