An enriched granger causal model allowing variable static anatomical constraints

Increased functional connectivity within the salience network in patients with insomnia

BACKGROUND

Insomnia is a common sleep disorder with significant negative impacts on emotional states; however, the underlying mechanism of insomnia with comorbid emotional dysregulation remains largely unknown. The salience network (SN) plays an important role in both sleep and emotional regulation. The study aimed to explore the specific alterations in functional connectivity (FC) within the SN in insomnia patients.

METHODS

A total of 30 eligible patients with insomnia disorder (ID group) and 30 healthy controls (HC group) underwent resting-state functional magnetic resonance imaging (fMRI) scanning and psychometric assessments. Differences in FC within the SN were examined using seed-based region-to-region connectivity analysis.

RESULTS

Compared with healthy controls, patients with insomnia showed increased FC within the SN, mainly between the anterior cingulate cortex (ACC) and right superior frontal gyrus (SFG), the right SFG and right supramarginal gyrus (SMG), and between the right insular (INS) and left SMG (P<0.05). Additionally, significant correlations were observed between increased FC and the Hamilton Depression Rating Scale (HAMD), Pittsburgh Sleep Quality Index (PSQI), and Hamilton Anxiety Rating Scale (HAMA) scores (P<0.05, after Bonferroni correction).

CONCLUSIONS

These results suggest that increased FC within the SN may be related to poor sleep quality and negative emotions, highlighting the importance of the SN in the pathophysiological mechanisms of insomnia with comorbid emotional dysregulation.

Structurally informed resting-state effective connectivity recapitulates cortical hierarchy

Interregional brain communication is mediated by the brain's physical wiring (i.e., structural connectivity). Yet, it remains unclear whether models describing directed, functional interactions between latent neuronal populations-effective connectivity-benefit from incorporating macroscale structural connectivity. Here, we assess a hierarchical empirical Bayes method: structural connectivity-based priors constrain the inversion of group-level resting-state effective connectivity, using subject-level posteriors as input; subsequently, group-level posteriors serve as empirical priors for re-evaluating subject-level effective connectivity. This approach permits knowledge of the brain's structure to inform inference of (multilevel) effective connectivity. In 17 resting-state brain networks, we find that a positive, monotonic relationship between structural connectivity and the prior probability of group-level effective connectivity generalizes across sessions and samples. Providing further validation, we show that inter-network differences in the coupling between structural and effective connectivity recapitulate a well-known unimodal-transmodal hierarchy. Thus, our results provide support for the use of our method over structurally uninformed alternatives.

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

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.