Deep Learning-Based Assessment of Brain Connectivity Related to Obstructive Sleep Apnea and Daytime Sleepiness.
Deep Learning-Based Assessment of Brain Connectivity Related to Obstructive Sleep Apnea and Daytime Sleepiness.
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DOI：
10.2147/nss.s327110
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发表时间：
2021
影响因子：
3.4
通讯作者：
Shin C
中科院分区：
文献类型：
--
作者：
Lee MH;Lee SK;Thomas RJ;Yoon JE;Yun CH;Shin C
阻塞性睡眠呼吸暂停（OSA）与大脑区域之间的成对连接改变有关，这可能解释认知障碍和白天嗜睡。通过采用深度学习方法，我们研究了与OSA严重程度和白天嗜睡相关的大脑连接。横断面设计将深度学习模型应用于从553名受试者（年龄，59.2 ± 7.4岁;男性，35.6%）获得的结构脑网络。用Pearson相关系数（R）和估计值与实际值之间的绝对误差小于标准差（PAE<SD）的概率评价模型性能。此外，我们研究了性别对OSA和日间嗜睡的深度学习输出的影响，并检查了OSA和非OSA组之间与日间嗜睡相关的大脑连接的差异。在整个组和亚组的测试数据集中，我们实现了有意义的R（高达0.74）和PAE<SD（高达0.92）。运动区、额叶和边缘区以及默认模式网络是预测OSA严重程度和日间嗜睡的重要连接的突出枢纽。性别影响与OSA严重程度以及白天嗜睡相关的大脑连接。与白天嗜睡相关的大脑连通性也因有无OSA而不同。深度学习方法可以评估大脑网络特征与OSA严重程度和白天嗜睡的关联，并指定相关的大脑连接。
Obstructive sleep apnea (OSA) is associated with altered pairwise connections between brain regions, which might explain cognitive impairment and daytime sleepiness. By adopting a deep learning method, we investigated brain connectivity related to the severity of OSA and daytime sleepiness. A cross-sectional design applied a deep learning model on structural brain networks obtained from 553 subjects (age, 59.2 ± 7.4 years; men, 35.6%). The model performance was evaluated with the Pearson’s correlation coefficient (R) and probability of absolute error less than standard deviation (PAE<SD) between the estimated and the actual scores. In addition, we investigated sex effects on deep learning outputs for OSA and daytime sleepiness and examined the differences in brain connectivity related to daytime sleepiness between OSA and non-OSA groups. We achieved a meaningful R (up to 0.74) and PAE<SD (up to 0.92) in a test dataset of whole group and subgroups. Motor, frontal and limbic areas, and default mode network were the prominent hubs of important connectivity to predict OSA severity and daytime sleepiness. Sex affected brain connectivity relevant to OSA severity as well as daytime sleepiness. Brain connectivity associated with daytime sleepiness also differed by the presence vs absence of OSA. A deep learning method can assess the association of brain network characteristics with OSA severity and daytime sleepiness and specify the relevant brain connectivity.