Li Li, Wu Yalan, Wu Jiaojiao, Li Bin, Hua Rui, Shi Feng, Chen Lizhou, Wu Yeke
Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Department of Research and Development, United Imaging Intelligence, Shanghai, China.
Front Neurosci. 2025 Apr 28;19:1555054. doi: 10.3389/fnins.2025.1555054. eCollection 2025.
Long-term mobile phone use (LTMPU) has been linked to sleep disorders, mood disorders, and cognitive impairment, with MRI-detected enlarged perivascular spaces (EPVSs) as potential imaging markers. This study investigated computational MRI-visible EPVSs and their association with sleep disturbance, dementia, and mental distress in young adults with LTMPU.
This retrospective study included 82 LTMPU patients who underwent MRI scans and assessments using six clinical scales: Montreal Cognitive Assessment (MoCA), Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), Hamilton Anxiety (HAM-A), and Hamilton Depression (HAM-D). Deep learning algorithms segmented EPVSs lesions, extracting quantitative metrics (count, volume, mean length, and mean curvature) across 17 brain subregions. Correlation analyses explored relationships between EPVSs indicators and clinical measurements. The BrainNet Viewer tool highlighted significant brain subregions and EPVSs traits linked to dementia, sleep disturbance, and mental distress.
Correlation analyses identified 23 significant indicator pairs (FDR-adjusted < 0.05), including associations between nine EPVSs characteristics and MoCA scores: four with the PSQI, one with the ISI, three with the ESS, four with the HAM-A, and two with the HAM-D. Regression analyses revealed seven significant EPVSs features, with three linked to cognitive impairment: mean EPVSs length in the left basal ganglia and mean length/curvature in the left temporal lobe. Also, the mean EPVSs length in the left frontal lobe could indicate insomnia, sleepiness, and anxiety.
Computational EPVSs metrics offer insights into the EPVSs pathophysiology and its links to mood disorders, sleep disturbances, and cognitive impairment in LTMPU patients. These findings also highlight potential connections between EPVSs, excessive daytime sleepiness, and anxiety, contributing to a comprehensive understanding of these multifaceted conditions.
长期使用手机（LTMPU）与睡眠障碍、情绪障碍和认知障碍有关，磁共振成像（MRI）检测到的血管周围间隙扩大（EPVSs）是潜在的影像学标志物。本研究调查了在LTMPU的年轻成年人中，MRI可见的EPVSs及其与睡眠障碍、痴呆和精神困扰的关联。
这项回顾性研究纳入了82例LTMPU患者，他们接受了MRI扫描，并使用六个临床量表进行评估：蒙特利尔认知评估量表（MoCA）、匹兹堡睡眠质量指数（PSQI）、失眠严重程度指数（ISI）、爱泼华嗜睡量表（ESS）、汉密尔顿焦虑量表（HAM-A）和汉密尔顿抑郁量表（HAM-D）。深度学习算法对EPVSs病变进行分割，提取17个脑区的定量指标（数量、体积、平均长度和平均曲率）。相关性分析探讨了EPVSs指标与临床测量之间的关系。BrainNet Viewer工具突出显示了与痴呆、睡眠障碍和精神困扰相关的重要脑区和EPVSs特征。
相关性分析确定了23对显著的指标对（经错误发现率校正后P<0.05），包括九个EPVSs特征与MoCA评分之间的关联：四个与PSQI相关，一个与ISI相关，三个与ESS相关，四个与HAM-A相关，两个与HAM-D相关。回归分析揭示了七个显著的EPVSs特征，其中三个与认知障碍有关：左侧基底节区的EPVSs平均长度以及左侧颞叶的平均长度/曲率。此外，左侧额叶的EPVSs平均长度可能表明存在失眠、嗜睡和焦虑。
计算得到的EPVSs指标为深入了解LTMPU患者的EPVSs病理生理学及其与情绪障碍、睡眠障碍和认知障碍的联系提供了线索。这些发现还突出了EPVSs、白天过度嗜睡和焦虑之间的潜在联系，有助于全面理解这些多方面的情况。
