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Aberrant Temporal-spatial Patterns to Sad expressions in Major Depressive Disorders via Hidden Markov Model

By Zhongpeng Dai, Siqi Zhang, Hongliang Zhou, Xinyi Wang, Huan Wang, Zhijian Yao, Qing Lu

Posted 08 Mar 2021
bioRxiv DOI: 10.1101/2021.03.07.433735

Background: The pathological mechanisms of Major depressive disorders (MDD) is associated with over-expressing of negative emotions, and the overall temporal-spatial patterns underlying over-representation in depression still remained to be revealed to date. We hypothesized that the aberrant spatio-temporal attributes of the process of sad expressions relate to MDD and help to detect depression severity. Methods: We enrolled a total of 96 subjects including 47 MDDs and 49 healthy controls (HCs), and recorded their Magnetoencephalography data under a sad expressions 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 spatial-temporal features, power estimations and fractional occupancy of each state were contrasted between MDDs and HCs. Results: Three states were found over the period of emotional stimuli processing procedure. The visual state was mainly distributed in early stage (0 - 270ms) and the limbic state in middle and late stage (270ms - 600ms) of the task, while the fronto-parietal state remained a steady proportion across the whole period. MDDs activated significantly more in limbic system during limbic state (p = 0.0045) and less in frontoparietal control network during fronto-parietal state (p = 5.38*10-5) relative to HCs. Hamilton-Depression-Rating Scale scores was significantly correlated with the predicted severity value using the state descriptors (p = 0.0062, r = 0.3933). Discussion: As human brain exhibited varied activation patterns under the negative stimuli, MDDs expressed disrupted temporal-spatial activated patterns across varied stages compared to HCs, indicting disordered regulation of brain functions. Furthermore, descriptors built by HMM could be potential biomarkers for identifying the severity of depression disorders.

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