Wang Chen, Fang Peng, Li Ya, Wu Lin, Hu Tian, Yang Qi, Han Aiping, Chang Yingjuan, Tang Xing, Lv Xiuhua, Xu Ziliang, Xu Yongqiang, Li Leilei, Zheng Minwen, Zhu Yuanqiang
Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, People's Republic of China.
Department of Military Medical Psychology, Air Force Medical University, Xi'an, People's Republic of China.
Nat Sci Sleep. 2022 Apr 22;14:791-803. doi: 10.2147/NSS.S345328. eCollection 2022.
Large individual differences exist in sleep deprivation (SD) induced sustained attention deterioration. Several brain imaging studies have suggested that the activities within frontal-parietal network, cortico-thalamic connections, and inter-hemispheric connectivity might underlie the neural correlates of vulnerability/resistance to SD. However, those traditional approaches are based on average estimates of differences at the group level. Currently, a neuroimaging marker that can reliably predict this vulnerability at the individual level is lacking.
Efficient transfer of information relies on the integrity of white matter (WM) tracts in the human brain, we therefore applied machine learning approach to investigate whether the WM diffusion metrics can predict vulnerability to SD. Forty-nine participants completed the psychomotor vigilance task (PVT) both after resting wakefulness (RW) and after 24 h of sleep deprivation (SD). The number of PVT lapse (reaction time > 500 ms) was calculated for both RW condition and SD condition and participants were categorized as vulnerable (24 participants) or resistant (25 participants) to SD according to the change in the number of PVT lapses between the two conditions. Diffusion tensor imaging were acquired to extract four multitype WM features at a regional level: fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) learning approach using leave-one-out cross-validation (LOOCV) was performed to assess the discriminative power of WM features in SD-vulnerable and SD-resistant participants.
LSVM analysis achieved a correct classification rate of 83.67% (sensitivity: 87.50%; specificity: 80.00%; and area under the receiver operating characteristic curve: 0.85) for differentiating SD-vulnerable from SD-resistant participants. WM fiber tracts that contributed most to the classification model were primarily commissural pathways (superior longitudinal fasciculus), projection pathways (posterior corona radiata, anterior limb of internal capsule) and association pathways (body and genu of corpus callosum). Furthermore, we found a significantly negative correlation between changes in PVT lapses and the LSVM decision value.
These findings suggest that WM fibers connecting (1) regions within frontal-parietal attention network, (2) the thalamus to the prefrontal cortex, and (3) the left and right hemispheres contributed the most to classification accuracy.
睡眠剥夺（SD）引起的持续注意力下降存在很大的个体差异。多项脑成像研究表明，额顶叶网络内的活动、皮质-丘脑连接以及半球间连接可能是对SD易感性/抵抗力的神经关联基础。然而，这些传统方法基于群体水平差异的平均估计。目前，缺乏一种能够在个体水平可靠预测这种易感性的神经影像标志物。
信息的有效传递依赖于人脑白质（WM）束的完整性，因此我们应用机器学习方法来研究WM扩散指标是否能够预测对SD的易感性。49名参与者在静息觉醒（RW）后以及睡眠剥夺24小时（SD）后均完成了精神运动警觉任务（PVT）。计算了RW条件和SD条件下PVT失误（反应时间>500毫秒）的数量，并根据两种条件下PVT失误数量的变化将参与者分为对SD易感（24名参与者）或抗SD（25名参与者）两类。采集扩散张量成像以在区域水平提取四种多类型WM特征：各向异性分数、平均扩散率、轴向扩散率和径向扩散率。采用留一法交叉验证（LOOCV）的线性支持向量机（LSVM）学习方法来评估WM特征在SD易感和抗SD参与者中的判别能力。
LSVM分析在区分SD易感和抗SD参与者方面的正确分类率达到83.67%（敏感性：87.50%；特异性：80.00%；受试者工作特征曲线下面积：0.85）。对分类模型贡献最大的WM纤维束主要是连合通路（上纵束）、投射通路（放射冠后部、内囊前肢）和联合通路（胼胝体体部和膝部）。此外，我们发现PVT失误的变化与LSVM决策值之间存在显著的负相关。
这些发现表明，连接（1）额顶叶注意力网络内区域、（2）丘脑与前额叶皮质以及（3）左右半球的WM纤维对分类准确性贡献最大。
