Sub‐second transient activated patterns to sad expressions in major depressive disorders discovered via hidden Markov model - Dai, Zhongpeng; Zhang, Siqi; Wang, Xinyi; Wang, Huan; Zhou, Hongliang; Tian, Shui; Chen, Zhilu; Lu, Qing; Yao, Zhijian (Journal of neuroscience research, 2021)
The pathological mechanisms of major depressive disorders (MDDs) is associated with the overexpression of negative emotions, and the fast transient‐activated patterns underlying overrepresentation in depression still remain to be revealed to date. We hypothesized that the aberrant spatiotemporal attributes of the process of sad expressions are related to the neuropathology of MDD and help to detect the depression severity. We enrolled a total of 96 subjects including 47 patients with MDD and 49 healthy controls (HCs), and recorded their magnetoencephalography data under a sad expression recognition task. A hidden Markov model (HMM) was applied to separate the whole neural activity into several brain states, then to characterize the dynamics. To find the disrupted temporal–spatial characteristics, power estimations and fractional occupancy (FO) of each state were estimated and contrasted between MDDs and HCs. Three states were found over the period of emotional stimuli processing procedure. The early visual stage (0–270 ms) was mainly manifested by state 1, and the emotional information processing stage (270–600 ms) was manifested by state 2, while the state 3 remained a steady proportion across the whole period. MDDs activated statistically more in limbic system during state 2 (p = 0.0045) and less in frontoparietal control network during state 3 (p = 5.38 × 10–5) relative to HCs. Hamilton Depression Rating Scale scores were significantly correlated with the predicted disorder severity using FO values (p = 0.0062, r = 0.3933). Relative to HCs, MDDs perceived the sad contents quickly and spent more time overexpressing the negative emotions. These phenomena indicated MDD patients might easily indulge in negative emotion and neglect other things. Furthermore, temporal descriptors built by HMM could be potential biomarkers for identifying the severity of depression disorders. We explored the comprehensive processing framework of negative emotional stimuli in patients with major depressive disorders. With a well‐established hidden Markov model, the whole processing was segmented into three dynamic states via brain functions, and temporal–spatial dynamic dysfunction was found during different task stages in depression.