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Objective To explore the scope of available research and to identify research gaps on in-vehicle interventions for drowsiness that utilize driver monitoring systems (DMS). Background DMS are gaining popularity as a countermeasure against drowsiness. However, how these systems can be best utilized to guide driver attention is unclear. Methods A scoping review was conducted in adherence to PRISMA guidelines. Five electronic databases (ACM Digital Library, Scopus, IEEE Xplore, TRID, and SAE Mobilus) were systematically searched in April 2022. Original studies examining in-vehicle drowsiness interventions that use DMS in a driving context (e.g., driving simulator and driver interviews) passed the screening. Data on study details, state detection methods, and interventions were extracted. Results Twenty studies qualified for inclusion. Majority of interventions involved warnings ( n = 16) with an auditory component ( n = 14). Feedback displays ( n = 4) and automation takeover ( n = 4) were also investigated. Multistage interventions ( n = 12) first cautioned the driver, then urged them to take an action, or initiated an automation takeover. Overall, interventions had a positive impact on sleepiness levels, driving performance, and user evaluations. Whether interventions effective for one type of sleepiness (e.g., passive vs. active fatigue) will perform well for another type is unclear. Conclusion Literature mainly focused on developing sensors and improving the accuracy of DMS, but not on the driver interactions with these technologies. More intervention studies are needed in general and for investigating their long-term effects. Application We list gaps and limitations in the DMS literature to guide researchers and practitioners in designing and evaluating effective safety systems for drowsy driving.

Objective This study aimed to explore the relationship between system interface elements' design features and interaction performance in simulated vehicle vibration environments. Background Touch screens have been widely used in vehicle information systems, but few studies have focused on the decline of touchscreen interaction performance and task load increase when driving on unpaved roads. Method The interaction performance (reaction time and task accuracy rate) with vibration frequencies below 3 Hz (1.5, 2.0, and 2.5 Hz) and different interface design elements was investigated employing a touch screen computer and E-prime software. Results The results indicate that vehicle vibration (below 3 Hz) can significantly reduce interaction performance with a vehicle information system interface. Conclusion An appropriate increase in the physical size of the interface design features (visual stimulus materials and touch buttons) can help to mitigate this negative effect of vibration. Application The results and findings of this study can be utilized for the design of information system interfaces as it relates to the vibration scenario of unpaved roads.

Yuanqiang Zhu,1,* Fang Ren,1,* Yuanju Zhu,2,* Xiao Zhang,1 Wenming Liu,3 Xing Tang,1 Yuting Qiao,3 Yanhui Cai,3 Mingwen Zheng1 1Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, People's Republic of China; 2Institute of Medicinal Chemistry, School of Pharmacy, Shandong University, Jinan, Shandong, People's Republic of China; 3Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, People's Republic of China*These authors contributed equally to this workCorrespondence: Yuanqiang Zhu; Mingwen ZhengDepartment of Radiology, Xijing Hospital, Fourth Military Medical University, No. 127 West Changle Road, Xi'an 710032, People's Republic of ChinaTel/Fax +86-29-8477-5415Email Zhu_YQ_fmmu@163.com; zhengmw2007@163.comBackground: It is well known that circadian rhythms and sleep homeostasis contribute to a pronounced trough in sleepiness and behavioral performance at night. However, the underlying neuroimaging mechanisms remain unclear. How brain-function connectivity is modulated during sleep deprivation (SD) has been rarely examined.Methods: By increasing the number of scanning sessions during SD, the current study used voxel-mirrored homotopic connectivity (VMHC) to investigate dynamic changes in interhemispheric communication during one night of SD. Every 2 hours from 10 pm to 06 am (session 1, 10 pm; session 2, 12 am; session 3, 2 am; session 4, 4 am; session 5, 6 am), functional magnetic resonance–imaging data and Stanford Sleepiness Scale (SSS) scores were collected from 36 healthy participants with intermediate chronotype. Dynamic changes in SSS scores and VMHC were determined using one-way repeated-measure ANOVA with the false discovery–rate method to correct for multiple comparisons.Results: Significant time effects for VMHC were found mainly in the bilateral thalamus, bilateral superior temporal gyrus, and bilateral precentral gyrus. SSS scores and VMHC in these areas were both found to be monotonously increased during SD. Furthermore, significant positive associations were found between SSS valu and VMHC values in the left superior temporal and right superior gyri.Conclusion: These findings might represent the dynamic modulation of circadian rhythm merely or the interaction effects of both circadian rhythm and sleep homeostasis on interhemispheric connectivity within the thalamus, default-mode network, and sensorimotor network. Our study provides more comprehensive information on how SD regulates brain connectivity between hemispheres and adds new evidence of neuroimaging correlates of increased sleepiness after SD.Keywords: sleep deprivation, resting-state functional MRI, voxel-mirrored homotopic connectivity, Stanford Sleepiness Scale, dynamic changes

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 Zhu_YQ_fmmu@163.com 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 zhengmw2007@163.comBackground: 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