Predicting antidepressant responsiveness in major depressive disorder patients via electroencephalography gamma-band dynamic functional connectivity in response to salient auditory stimuli.

BACKGROUND

Heterogeneous pathophysiological characteristics in patients with major depressive disorder (MDD) lead to individually differentiated sensitivities to antidepressants. Based on the hypothesis that gamma-band dynamic fluctuations in cortical functional connectivity (FC) in response to salient stimuli are linked to pathophysiological characteristics, we conducted a classification analysis for antidepressant responsiveness prediction.

METHODS

Biosignals and psychological measures were acquired from 47 patients with MDD prior to treatment. After 8 weeks of antidepressant therapy, patients were divided into non-remitted MDD (nrMDD; aged 42.55 ± 11.52 years; n = 20) and remitted MDD (rMDD; aged 47.22 ± 11.59 years; n = 27) groups based on their depressive symptom reduction. Electroencephalography (EEG) signals were acquired during the duration-variant auditory mismatch negativity paradigm. From the deviant condition, gamma-band weighted phase-lag index-based dynamic fluctuations were evaluated using a template generated from 21 demography-matched healthy control (aged 43.81 ± 14.10 years) data.

RESULTS

Using these dynamic functional connectivity (dFC) features, a machine learning-based classification analysis was performed for nrMDD and rMDD. Using leave-one-out cross-validation, the linear discriminant analysis classifier achieved the best accuracy (82.98%) for classifying nrMDD and rMDD. Further simple effect analyses identified three core dFC features for nrMDD: (i) relatively intact time-dependent FC between the left frontal and right temporal regions; (ii) disrupted right frontoparietal FC; and (iii) disrupted left fronto-temporal FC. These dFC features commonly exhibit transient hyperconnections in patients with nrMDD.

CONCLUSIONS

We demonstrated that gamma-band dFC responses to salient stimuli could serve as potential biomarkers for antidepressant responsiveness prediction in patients with MDD.