Sleep medicine · 2025
Older cerebral small vessel disease and sleep disturbance: A diffusion tensor imaging study.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Gu Yumeng, Feng Wenjun, Tian Xiaolin, Wang Xiaowen, Li Xin
Аннотация
OBJECTIVE: This study examined the relationship between diffusion tensor imaging indicators and brain network characteristics in patients with cerebral small vessel disease (CSVD) with (CSVD + S) and without (CSVD-S) sleep disturbance. We explored the feasibility of using these imaging biomarkers to investigate the pathophysiological mechanisms underlying sleep disturbance in patients with CSVD.
METHODS: A total of 146 patients with CSVD and 84 healthy controls were included. Sleep quality was assessed using polysomnography and the Pittsburgh Sleep Quality Index. Tract-based spatial statistics and graph theory were applied to compare white matter lesions and brain network characteristics, which were then used for backpropagation artificial neural network (BPANN) analysis.
RESULTS: Compared with the control group, the CSVD + S group showed a decrease in total sleep time and sleep efficiency, as well as higher values for sleep onset latency, wake after sleep onset, and non-rapid eye movement sleep stage 1. Both the CSVD + S and CSVD-S groups exhibited reduced fractional anisotropy and structural connectivity strength. However, the CSVD + S group showed increased mean diffusivity in affected fiber bundles (the anterior thalamic radiation, frontal occipital fasciculus, and superior longitudinal fasciculus) in key brain regions, such as the thalamus and frontal lobe, that regulate sleep and wakefulness. In addition, the CSVD + S group showed significant impairments in global, node, and small-world attributes. The BPANN model successfully predicted sleep disturbance in patients with CSVD.
CONCLUSION: Our findings support the possibility that white matter abnormalities in subcortical neural circuits and microstructural and functional changes in brain connections underly CSVD sleep disturbance.
METHODS: A total of 146 patients with CSVD and 84 healthy controls were included. Sleep quality was assessed using polysomnography and the Pittsburgh Sleep Quality Index. Tract-based spatial statistics and graph theory were applied to compare white matter lesions and brain network characteristics, which were then used for backpropagation artificial neural network (BPANN) analysis.
RESULTS: Compared with the control group, the CSVD + S group showed a decrease in total sleep time and sleep efficiency, as well as higher values for sleep onset latency, wake after sleep onset, and non-rapid eye movement sleep stage 1. Both the CSVD + S and CSVD-S groups exhibited reduced fractional anisotropy and structural connectivity strength. However, the CSVD + S group showed increased mean diffusivity in affected fiber bundles (the anterior thalamic radiation, frontal occipital fasciculus, and superior longitudinal fasciculus) in key brain regions, such as the thalamus and frontal lobe, that regulate sleep and wakefulness. In addition, the CSVD + S group showed significant impairments in global, node, and small-world attributes. The BPANN model successfully predicted sleep disturbance in patients with CSVD.
CONCLUSION: Our findings support the possibility that white matter abnormalities in subcortical neural circuits and microstructural and functional changes in brain connections underly CSVD sleep disturbance.