Insomnia disorder and hyperarousal: evidence from resting-state and sleeping EEG

Background: Previous research has revealed various aspects of resting-state EEG for depression and insomnia. However, the EEG characteristics of depressed patients with co-morbid insomniac are rarely studied, especially EEG microstates that capture the dynamic activities of the large-scale brain network. Methods:To fill the research gaps, this study collected resting-state EEG data from 32 sub-clinical depressions with co-morbid insomnia (CI), 31 comorbid-free depressions (CFD), and 32 healthy controls (HC). Four topographic maps were generated from clean EEG data after clustering and rearrangement. Temporal characteristics were obtained for statistical analysis, including cross-group variance analysis (ANOVA) and intra-group correlation analysis. Results: The global clustering of all individuals in the EEG microstate analysis revealed the four previously discovered categories of microstates (A, B, C, and D). The occurrence of microstate B was found to be lower in CI than in CFD. The correlation analysis showed that the total PSQI score was negatively correlated with the occurrence of microstate C in CI (r=-0.354, p<.05). Conversely, there was a positive correlation between SDS scores and the duration of microstate C in CFD (r=0.359, p<.05). Conclusion: The spatiotemporal dynamics of the brain network can vary due to abnormalities in the visual network corresponding to microstate B in patients with depression and insomnia.. Further investigation is needed for microstate change can be related to high arousal and emotional problems in people suffering from depression and insomnia. Microstates may therefore become crucial neurobiological predictors to forecast the likelihood of future cases of depression and insomnia.

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Real-Time fMRI Neurofeedback Training Changes Brain Degree Centrality and Improves Sleep in Chronic Insomnia Disorder: A Resting-State fMRI Study

Zou

et al. 2022

Front. Mol. Neurosci.

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BackgroundChronic insomnia disorder (CID) is considered a major public health problem worldwide. Therefore, innovative and effective technical methods for studying the pathogenesis and clinical comprehensive treatment of CID are urgently needed.MethodsReal-time fMRI neurofeedback (rtfMRI-NF), a new intervention, was used to train 28 patients with CID to regulate their amygdala activity for three sessions in 6 weeks. Resting-state fMRI data were collected before and after training. Then, voxel-based degree centrality (DC) method was used to explore the effect of rtfMRI-NF training. For regions with altered DC, we determined the specific connections to other regions that most strongly contributed to altered functional networks based on DC. Furthermore, the relationships between the DC value of the altered regions and changes in clinical variables were determined.ResultsPatients with CID showed increased DC in the right postcentral gyrus, Rolandic operculum, insula, and superior parietal gyrus and decreased DC in the right supramarginal gyrus, inferior parietal gyrus, angular gyrus, middle occipital gyrus, and middle temporal gyrus. Seed-based functional connectivity analyses based on the altered DC regions showed more details about the altered functional networks. Clinical scores in Pittsburgh sleep quality index, insomnia severity index (ISI), Beck depression inventory, and Hamilton anxiety scale decreased. Furthermore, a remarkable positive correlation was found between the changed ISI score and DC values of the right insula.ConclusionsThis study confirmed that amygdala-based rtfMRI-NF training altered the intrinsic functional hubs, which reshaped the abnormal functional connections caused by insomnia and improved the sleep of patients with CID. These findings contribute to our understanding of the neurobiological mechanism of rtfMRI-NF in insomnia treatment. However, additional double-blinded controlled clinical trials with larger sample sizes need to be conducted to confirm the effect of rtfMRI-NF from this initial study.

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Insomnia disorder and hyperarousal: evidence from resting-state and sleeping EEG

Cited by 3 publications

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Hybrid spiking neural network for sleep electroencephalogram signals

Hybrid spiking neural network for sleep electroencephalogram signals

Resting-State EEG Reveals Abnormal Microstates Characteristics of Depression with Insomnia

Real-Time fMRI Neurofeedback Training Changes Brain Degree Centrality and Improves Sleep in Chronic Insomnia Disorder: A Resting-State fMRI Study

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