Sub‐second transient activated patterns to sad expressions in major depressive disorders discovered via hidden Markov model

Abstract

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.

Hampered gamma oscillations induced by sad emotion underlying suicide attempt in major depressive disorder

“…Compared with previous published reports, 8,[42][43][44] our exploratory study has several strengths in both methodology and neurobiology. First, the current study overcame the limited spatial resolution of existing EEG research and localized low-gamma oscillations in cortex regions with advanced source-reconstructed analysis.…”

“…3b). Similarly, in the low gamma band (35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45), HMM could also reconstruct neural patterns underlying the processing of visual stimuli and emotional information, which was respectively characterized by hyper-activation in visual state3 between 0 and 250 ms, and increased power in fronto-parietal state4 from 250 to 500 ms (with a peak around 300 ms) (Fig. 3c,d).…”

Attenuated alpha–gamma coupling in emotional dual pathways with right‐Amygdala predicting ineffective antidepressant response

“…However, recent studies have shown that there was the association between aberrant gamma oscillations in the visual cortex and emerging psychosis. 46 Depressed patients showed abnormal dynamics in visual networks during the similar emotional task 49 ; thus, future study could further explore the relation between impaired visual areas and emotional dysfunction. Second, as the current study aimed at exploring dual‐routed pathways in the early emotional processing stage, analyses were performed in limited brain regions rather than large‐scale brain networks, which might also be associated with antidepressant response via previous literatures.…”

Global and local feature fusion via long and short-term memory mechanism for dance emotion recognition in robot

“…Mohanty and Palo ( 2020 ) proposed to extract prosodic and spectral parameters of audio, and then used probabilistic neural network (PNN) and hidden markov model (HMM) to extract prosodic and spectral parameters of audio. HMM processed these two kinds of parameter features (Dai et al, 2021 ).…”