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

Aug 10, 2023Journal of affective disorders

Brain activity in emotion-related areas may predict anxiety levels in chronic insomnia patients

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Abstract

CID-A patients showed heightened functional connectivity strength in the right dorsomedial prefrontal cortex compared to other groups.

Chronic insomnia disorder with comorbid anxiety symptoms (CID-A) is associated with altered brain function in specific areas related to negative emotions.
The right dorsomedial prefrontal cortex is identified as a key region that exhibits increased connectivity in CID-A patients.
Functional connectivity features related to the dorsomedial prefrontal cortex may help predict anxiety symptoms in patients with chronic insomnia.
The findings indicate a potential model linking the negative affective network to the brain changes observed in CID-A.

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BACKGROUND: 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.

METHODS: 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).

RESULTS: 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.

LIMITATION: Modifications in brain functionality might vary across insomnia subtypes.

CONCLUSION: 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.

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

Abstract

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