Nature and Science of Sleep (Apr 2022)
Predicting Attentional Vulnerability to Sleep Deprivation: A Multivariate Pattern Analysis of DTI Data
Abstract
Chen Wang,1,* Peng Fang,2,* Ya Li,1,* Lin Wu,2,* Tian Hu,3 Qi Yang,4 Aiping Han,5 Yingjuan Chang,1 Xing Tang,1 Xiuhua Lv,1 Ziliang Xu,1 Yongqiang Xu,1 Leilei Li,1 Minwen Zheng,1 Yuanqiang Zhu1 1Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, People’s Republic of China; 2Department of Military Medical Psychology, Air Force Medical University, Xi’an, People’s Republic of China; 3Department of Radiology, Yan’an University Affiliated Hospital, Yan’an, People’s Republic of China; 4Department of Radiology, Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine, Xianyang, People’s Republic of China; 5Imaging Diagnosis and Treatment Center, Xi’an International Medical Center Hospital, Xi’an, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuanqiang Zhu, Department of Radiology, Xijing Hospital, Fourth Military Medical University, No. 127 West Changle Road, Xi’an, 710032, People’s Republic of China, Tel/Fax +86-29-84775415, Email [email protected] Minwen Zheng, Department of Radiology, Xijing Hospital, Fourth Military Medical University, No. 127 West Changle Road, Xi’an, 710032, People’s Republic of China, Tel/Fax +86-29-84771938, Email [email protected]: 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.Methods: 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.Results: 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.Conclusion: 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.Keywords: sleep deprivation, vulnerability, diffusion tensor imaging, machine learning, psychomotor vigilance task
