Insomnia and depression are highly comorbid symptoms in both primary insomnia (PI) and major depressive disorder (MDD). In the current study, we aimed at exploring both the homogeneous and heterogeneous brain structure alteration in PI and MDD patients. Sixty-five MDD patients and 67 matched PI patients were recruited and underwent a structural MRI scan. The subjects were sub-divided into four groups, namely MDD patients with higher or lower insomnia, and PI patients with higher or lower severe depression. A general linear model was employed to explore the changes in cortical thickness and volume as a result of depression or insomnia, and their interaction. In addition, partial correlation analysis was conducted to detect the clinical significance of the altered brain structural regions. A main effect of depression on cortical thickness was seen in the superior parietal lobe, middle cingulate cortex, and parahippocampal gyrus, while a main effect of insomnia on cortical thickness was found in the posterior cingulate cortex. Importantly, the interaction between depression and insomnia was associated with decreased gray matter volume in the right orbitofrontal cortex, i.e., patients with co-occurring depression and insomnia showed smaller brain volume in the right orbitofrontal cortex when compared to patients with lower insomnia/depression. These findings highlighted the role of the orbitofrontal cortex in the neuropathology of the comorbidity of insomnia and depression. Our findings provide new insights into the understanding of the brain mechanism underlying comorbidity of insomnia and depression.

The ascending arousal system plays a crucial role in individuals' consciousness. Recently, advanced functional magnetic resonance imaging (fMRI) has made it possible to investigate the ascending arousal network (AAN) in vivo. However, the role of AAN in the neuropathology of human insomnia remains unclear. Our study aimed to explore alterations in AAN and its connections with cortical networks in chronic insomnia disorder (CID). Resting‐state fMRI data were acquired from 60 patients with CID and 60 good sleeper controls (GSCs). Changes in the brain's functional connectivity (FC) between the AAN and eight cortical networks were detected in patients with CID and GSCs. Multivariate pattern analysis (MVPA) was employed to differentiate CID patients from GSCs and predict clinical symptoms in patients with CID. Finally, these MVPA findings were further verified using an external data set (32 patients with CID and 33 GSCs). Compared to GSCs, patients with CID exhibited increased FC within the AAN, as well as increased FC between the AAN and default mode, cerebellar, sensorimotor, and dorsal attention networks. These AAN‐related FC patterns and the MVPA classification model could be used to differentiate CID patients from GSCs with 88% accuracy in the first cohort and 77% accuracy in the validation cohort. Moreover, the MVPA prediction models could separately predict insomnia (data set 1, R² = .34; data set 2, R² = .15) and anxiety symptoms (data set 1, R² = .35; data set 2, R² = .34) in the two independent cohorts of patients. Our findings indicated that AAN contributed to the neurobiological mechanism of insomnia and highlighted that fMRI‐based markers and machine learning techniques might facilitate the evaluation of insomnia and its comorbid mental symptoms.

Repetitive transcranial magnetic stimulation (rTMS) offers a promising approach for the treatment of insomnia; however, the precise targets and underlying neural mechanisms remain unclear. This randomized, wait-controlled trial aimed to evaluate the clinical efficacy of neuronavigated rTMS targeting the right dorsolateral prefrontal cortex (DLPFC) in chronic insomnia disorder (CID) and to identify potential neural mechanisms associated with therapeutic outcomes. Fifty patients with CID were randomized to receive 20 sessions of 1 Hz rTMS targeting the right DLPFC or to a waitlist control group. Stimulation coordinates were selected (MNI: 40,39,11) based on our previous neuroimaging meta-analysis, and were precisely localized using MRI-guided neuronavigation. Clinical assessments and resting-state fMRI were conducted before and after the intervention, respectively. Target-based functional connectivity (FC) analysis was used to map rTMS-associated network changes, while causal mediation analysis was used to examine the relationships between neural changes and clinical improvements. Compared to waitlist controls, the rTMS group showed greater improvements in insomnia and mood symptoms (all p < 0.001), with higher response rates (54.55 % vs. 9.09 %) and remission rates (68.18 % vs. 13.64 %). FC analysis showed significant group × time effects on the bilateral DLPFC, middle cingulate cortex, and right anterior cerebellar vermis. Mediation analysis indicated that FC changes in the right DLPFC mediated 24 % of the improvement in insomnia severity (Insomnia Severity Index, p = 0.048). These preliminary findings suggest that precision neuronavigated rTMS targeting the right DLPFC may alleviate insomnia symptoms, with the observed clinical improvements potentially related to the reorganization of the DLPFC network. While these results are encouraging, further research based on placebo-controlled study designs is required to confirm these effects and better understand the underlying mechanisms. This study provides preliminary evidence supporting the integration of precision targeting with neuroimaging to explore the mechanisms underlying the effects of rTMS in insomnia treatment.