Abstract Number: Esoc2026a483 Neurovascular Network Identifies Sleep Disturbance in Patients With Cerebral Small Vessel Disease
Year: 2026
Type: conference-abstract
Abstract: Background and aims Sleep disturbances are prevalent in cerebral small vessel disease (CSVD), yet their underlying mechanisms remain unclear. Dysfunction of the neurovascular network (NVN), encompassing autonomic, hemodynamic, and neuroelectrical domains, may be pivotal. We aimed to investigate whether multidimensional NVN dysfunction is associated with poor sleep in CSVD and to develop NVN-based screening models. Methods In this single-center cross-sectional study of 102 CSVD patients, we comprehensively assessed NVN functions via heart rate variability (HRV), multimodal cerebral hemodynamics (cerebral autoregulation [CA], cerebrovascular reactivity [CVR], and neurovascular coupling [NVC]), and resting-state electroencephalography (EEG). Sleep quality was evaluated using the Pittsburgh Sleep Quality Index, with a score >5 defining poor sleep. Multivariable logistic regression and receiver operating characteristic (ROC) analyses were used to build and evaluate screening models. Results Fifty-three patients (52%) had poor sleep, and exhibited significantly impaired HRV, dynamic CA, CVR, and alpha-band EEG activity, but exaggerated NVC responses. Multivariable models incorporating demographics and NVN parameters demonstrated strong screening performance. The full models, which included HRV, EEG, and all three hemodynamic sub-dimensions, achieved an AUC of 0.854 (95% CI: 0.783-0.925). Simplified models, retaining HRV, EEG, and the optimal hemodynamic sub-dimension (CA), showed robust performance (AUC = 0.839), achieving a superior complexity-performance trade-off. Both full and simplified models outperformed single-dimension ones, which incorporate parameters from only one NVN domain. Conclusions Poor sleep in CSVD is associated with multimodal NVN dysfunction across autonomic, hemodynamic, and neuroelectrical domains. Multimodal NVN-based models offer a sensitive and physiologically informed approach for identifying sleep disturbances in this population. Conflict of interest All authors have nothing to disclose.
Source: European Stroke Journal
Authors: Tong Guo, Ziyi Gao, Mengxi Zhao, Qianqian Yang, Guoliang Hu, Xiaodong Li, Yilong Wang, Ling Guan
Language: English
Topic: Sleep and related disorders
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,400
OpenAlex ID: W7160427632
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