Transcriptional signatures of cortical structural changes in chronic insomnia disorder.

Chronic insomnia disorder (CID) is a multidimensional disease that may influence various levels of brain organization, spanning the macroscopic structural connectome to microscopic gene expression. However, the connection between genomic variations and morphological alterations in CID remains unclear. Here, we investigated brain structural changes in CID patients at the whole-brain level and whether these link to transcriptional characteristics. Brain structural data from 104 CID patients and 102 matched healthy controls (HC) were acquired to examine cortical structural alterations using morphometric similarity (MS) analysis. Partial least squares (PLS) regression and transcriptome data from the Allen Human Brain Atlas were used to extract genomes related to MS changes. Gene-category enrichment analysis (GCEA) was used to identify potential molecular mechanisms behind the observed structural changes. We found that CID patients exhibited MS reductions in the parietal and limbic regions, along with enhancements in the temporal and frontal regions compared to HCs (pFDR < .05). Subsequently, PLS and GCEA revealed that these MS alterations were spatially correlated with a set of genes, especially those significantly correlated with excitatory and inhibitory neurons and chronic neuroinflammation. This neuroimaging-transcriptomic study bridges the gap between cortical structural changes and the molecular mechanisms in CID patients, providing novel insight into the pathophysiology of insomnia and targeted treatments.

Deciphering the impairment of perimenopausal insomnia on visual search from a neurocognitive processing perspective.

STUDY OBJECTIVES: Perimenopausal insomnia (PMI) is associated with observable performance impairments in visual search tasks. This study examines how various cognitive processing stages contribute to search performance delays in PMI compared to healthy controls (HCs). METHODS: We recruited 76 participants diagnosed with PMI and 63 HCs. Event-related potentials (ERPs) were recorded as participants engaged in a visual search task, reporting the orientation of a color popout target within an array of ellipses. We analyzed group differences in behavioral performance and ERP components across cognitive processing stages. RESULTS: Compared to HCs, PMI patients exhibited behavioral response delays, although accuracy was not different between groups. Electrophysiological analyses revealed group differences across several ERP components. Firstly, the N1 component's amplitude increased bilaterally, suggesting enhanced visual sensory processing. Secondly, a slower and smaller N2pc indicated reduced attentional orienting. Thirdly, a decreased SPCN amplitude pointed to deficits in target discrimination. Fourthly, an increased amplitude of the stimulus-locked LRP, with unchanged latency, suggested heightened neural inputs for maintaining motor initiation speed. Fifthly, prolonged response-locked LRP latency indicated slower motor execution. Finally, these changes in ERP components, along with significant correlations between LRP components and insomnia symptoms, suggest potential neural biomarkers for PMI. CONCLUSIONS: Our findings provide high-temporal-resolution insights into the neurocognitive disruptions associated with PMI, highlighting how sleep disturbances affect cognitive processing in visual tasks. These insights enhance our understanding of PMI and contribute to discussions on neural mechanisms driving behavioral performance in various conditions.

Electroencephalography connectome changes in chronic insomnia disorder are correlated with neurochemical signatures.

STUDY OBJECTIVES: This study aimed to investigate the alterations in resting-state electroencephalography (EEG) global brain connectivity (GBC) in patients with chronic insomnia disorder (CID) and to explore the correlation between macroscale connectomic variances and microscale neurotransmitter distributions. METHODS: We acquired 64-channel EEG from 35 female CID patients and 34 healthy females. EEG signals were source-localized using individual brain anatomy and orthogonalized to mitigate volume conduction. Correlation coefficients between band-limited source-space power envelopes of the DK 68 atlas were computed and averaged across regions to determine specific GBC values. A support vector machine (SVM) classifier utilizing GBC features was employed to differentiate CID patients from controls. We further used Neurosynth and a 3D atlas of neurotransmitter receptors/transporters to assess the cognitive functions and neurotransmitter landscape associated with CID cortical abnormality maps, respectively. RESULTS: CID patients exhibited elevated GBC within the medial prefrontal cortex and limbic cortex, particularly at the gamma carrier frequency, compared to controls (pFDR < .05). GBC patterns were found to effectively distinguish CID patients from controls with a precision of 90.8% in the SVM model. The cortical abnormality maps were significantly correlated with meta-analytic terms like "cognitive control" and "emotion regulation." Notably, GBC patterns were associated with neurotransmitter profiles (pspin < .05), with neurotransmitter systems such as norepinephrine, dopamine, and serotonin making significant contributions. CONCLUSIONS: This work characterizes the EEG connectomic profile of CID, facilitating the cost-effective clinical translation of EEG-derived markers. Additionally, the linkage between GBC patterns and neurotransmitter distribution offers promising avenues for developing targeted treatment strategies for CID.

Larger hypothalamic subfield volumes in patients with chronic insomnia disorder and relationships to levels of corticotropin-releasing hormone.

The hypothalamus is a well-established core structure in the sleep-wake cycle. While previous studies have not consistently found whole hypothalamus volume changes in chronic insomnia disorder (CID), differences may exist at the smaller substructural level of the hypothalamic nuclei. The study aimed to investigate the differences in total and subfield hypothalamic volumes, between CID patients and healthy controls (HCs) in vivo, through an advanced deep learning-based automated segmentation tool. A total of 150 patients with CID and 155 demographically matched HCs underwent T1-weighted structural magnetic resonance scanning. We utilized FreeSurfer v7.2 for automated segmentation of the hypothalamus and its five nuclei. Additionally, correlation and causal mediation analyses were performed to investigate the association between hypothalamic volume changes, insomnia symptom severity, and hypothalamus-pituitary-adrenal (HPA) axis-related blood biomarkers. CID patients exhibited larger volumes in the right anterior inferior, left anterior superior, and left posterior subunits of the hypothalamus compared to HCs. Moreover, we observed a positive association between blood corticotropin-releasing hormone (CRH) levels and insomnia severity, with anterior inferior hypothalamus (a-iHyp) hypertrophy mediating this relationship. In conclusion, we found significant volume increases in several hypothalamic subfield regions in CID patients, highlighting the central role of the HPA axis in the pathophysiology of insomnia.