MS can selectively drive and modulate activity in local neural network even at an in vivo tissue level.

No abstract

The fatigue of truck, bus, and taxi drivers has been a causal trigger for road accidents. However, the relationship between collision risk and the extent of objective fatigue has yet to be confirmed. In this study, we aimed to identify the relationship between autonomic nerve function as an objective parameter of fatigue and the extent of rear-end collision risk, which includes not only objectively risky events but also situations in which truck drivers require safety guidance from safety transport managers. Data of 33 truck driver participants (2 females, 31 males, 46.0 ± 9.1 years old, min-max: 24-65 years old) were analyzed. Drive recorder and automotive sensor data were collected over an eight-month period, and the autonomic nerve function during resting state in drivers was evaluated daily, pre-and postshift, using pulse waves and electrocardiographic waveform measurement. The rear-end collision risk Index was developed using decision tree analysis of the audiovisual drive recorder data and distance data from the front automotive sensors. The rear-end collision risk index of shift-day was positively correlated with the sympathetic nerve activity index of post-shift condition on the previous day. This suggests that fatigue-related sympathetic nerve overactivity of post-shift condition increases the rear-end collision risk in the following day. Measures, such as actively seeking rest and undertaking fatigue recovery according to the degree of sympathetic nerve activity of post-shift condition, are necessary in order to prevent truck drivers' rear-end collisions.

Objective. Single coil-based systems for magnetic stimulation are widely used for neurostimulation in neuroscience research and clinical treatment of neurological diseases. However, parallelization of magnetic stimulation with multiple coils may generate far greater potential than a single coil, and could thus expand the scope of brain area stimulation. Therefore, we examined whether a multiple coil-based system could improve the effectiveness and focality of conventional single coil-based magnetic stimulation. Approach. We designed and tested a micromagnetic stimulation (µMS) device with multiple submillimeter-sized coils as a possible substitute for one large coil. Our design concept is spatially-distributed stimulation strategy involving the small number of coils to be able to mimic desired electric field profiles. To this end, the cost function of the error between the desired and coilinduced electric fields was firstly calculated, and coil currents were repetitively estimated to achieve the smaller number of coils under a certain criterion: a minimum error with spatial sparsity. Using these approaches, we evaluated the capability of our multi-channel µMS via numerical simulations and demonstrated responsive results in animal experiments. Main results. Our approach can enhance control of neural excitation and improve the concentration of the excitation field induced by magnetic stimulation with reduced power consumption. Furthermore, in vivo electrophysiological recordings of mouse brain performed to evaluate our proposed approach for brain stimulation demonstrated experimentally that our multi-channel µMS device can yield more effective stimulation than the single-channel device. In addition, our device permitted electronic spatial adjustment of the stimulus shape and location without moving the coils. Significance. The development of new multichannel µMS-based therapeutic approaches may be useful because the µMS affects only a restricted brain area. Indeed, the small size of micro-coils and their finer focality with multichannel contribution might be suitable for chronic use, which is difficult using conventional large transcranial magnetic stimulation (TMS) with simple round or figure-eight coils. Thus, our findings support new opportunities to explore magnetic stimulation as a therapeutic approach for neurological disorders.

Increasing road crashes related to occupational drivers’ deteriorating health has become a social problem. To prevent road crashes, warnings and predictions of increased crash risk based on drivers’ conditions are important. However, in on-road driving, the relationship between drivers’ physiological condition and crash risk remains unclear due to difficulties in the simultaneous measurement of both. This study aimed to elucidate the relationship between drivers’ physiological condition assessed by autonomic nerve function (ANF) and an indicator of rear-end collision risk in on-road driving. Data from 20 male truck drivers (mean ± SD, 49.0±8.2 years; range, 35–63 years) were analyzed. Over a period of approximately three months, drivers’ working behavior data, such as automotive sensor data, and their ANF data were collected during their working shift. Using the gradient boosting decision tree method, a rear-end collision risk index was developed based on the working behavior data, which enabled continuous risk quantification. Using the developed risk index and drivers’ ANF data, effects of their physiological condition on risk were analyzed employing a logistic quantile regression method, which provides wider information on the effects of the explanatory variables, after hierarchical model selection. Our results revealed that in on-road driving, activation of sympathetic nerve activity and inhibition of parasympathetic nerve activity increased each quantile of the rear-end collision risk index. The findings suggest that acute stress-induced drivers’ fatigue increases rear-end collision risk. Hence, in on-road driving, drivers’ physiological condition monitoring and ANF-based stress warning and relief system can contribute to promoting the prevention of rear-end truck collisions.