Neuroimaging profiles of the negative affective network predict anxiety severity in patients with chronic insomnia disorder: A machine learning study

• Published in: Journal of affective disorders 2023-11, Vol.340, p.542-550
• Main Authors: Xu, Hao, Dou, Zeyang, Luo, Yucai, Yang, Lu, Xiao, Xiangwen, Zhao, Guangli, Lin, Wenting, Xia, Zihao, Zhang, Qi, Zeng, Fang, Yu, Siyi
• Format: Article
• Language: English
• Subjects: Anxiety; Chronic insomnia disorder; Machine learning; Negative affective network; Neuroimaging features; Predict
• ISSN: 0165-0327; 1573-2517
• DOI: 10.1016/j.jad.2023.08.016

Sleep is instrumental in safeguarding emotional well-being. While the susceptibility to both insomnia and anxiety has been demonstrated to involve intricate brain systems, the neuroimaging profile of chronic insomnia disorder with comorbid anxiety symptoms (CID-A) remains unexplored. Employing machine learning methodologies, this study aims to elucidate the distinct neural substrates underlying CID-A and to investigate whether these cerebral markers can prognosticate anxiety symptoms in patients with insomnia. Functional magnetic resonance imaging (fMRI) data were procured from a relatively large cohort (dataset 1) comprised of 47 CID-A patients, 49 CID patients without anxiety (CID-NA), and 48 good sleeper controls (GSC). Aberrant cerebral functional alterations were assessed through functional connectivity strength (FCS) and resting-state functional connectivity (rsFC). Subsequently, Support Vector Regression (SVR) models were constructed to predict anxiety symptoms in CID patients based on neuroimaging features, which were validated utilizing an external cohort (dataset 2). In comparison to CID-NA and GSC subjects, CID-A patients exhibited heightened FCS in the right dorsomedial prefrontal cortex (DMPFC), a central hub within the negative affective network. Moreover, the SVR models revealed that DMPFC-related rsFC/FCS features could be employed to predict anxiety symptoms in two independent cohorts of CID patients. Modifications in brain functionality might vary across insomnia subtypes. The present findings suggest a potential negative affective network model for the neuropathophysiology of CID accompanied by anxiety. Importantly, the negative affective network pattern may serve as a predictor for anxiety symptoms in CID patients. •Insomnia patients with and without anxiety disorders have common and unique brain function changes.•The specific abnormal neuroimaging profile of CID-A patients is in the right DMPFC, a central hub of the negative affective network.•A machine learning model using DMPFC-related features predicts anxiety severity in two insomnia patient cohorts.•Disconnected negative affective network may be targeted for stratified treatment of insomnia subtypes.