Harutoyo HIRANO

Anticipation of pain engenders anxiety and fear, potentially shaping pain perception and governing bodily responses such as peripheral vasomotion through the sympathetic nervous system (SNS). Sympathetic innervation of vascular tone during pain perception has been quantified using a peripheral arterial stiffness index; however, its innervation role during pain anticipation remains unclear. This paper reports on a neuroimaging-based study designed to investigate the responsivity and attribution of the index at different levels of anticipatory anxiety and pain perception. The index was measured in a functional magnetic resonance imaging experiment that randomly combined three visual anticipation cues and painful stimuli of two intensities. The peripheral and cerebral responses to pain anticipation and perception were quantified to corroborate bodily responsivity, and their temporal correlation was also assessed to identify the response attribution of the index. Contrasting with the high responsivity across levels of pain sensation, a low responsivity of the index across levels of anticipatory anxiety revealed its specificity across pain experiences. Discrepancies between the effects of perception and anticipation were validated across regions and levels of brain activity, providing a brain basis for peripheral response specificity. The index was also characterized by a 1-s lag in both anticipation and perception of pain, implying top-down innervation of the periphery. Our findings suggest that the SNS responds to pain in an emotion-specific and sensation-unbiased manner, thus enabling an early assessment of individual pain perception using this index. This study integrates peripheral and cerebral hemodynamic responses toward a comprehensive understanding of bodily responses to pain.

BACKGROUND: People who were previously hospitalised with stroke may have difficulty operating a motor vehicle, and their driving aptitude needs to be evaluated to prevent traffic accidents in today's car-based society. Although the association between motor-cognitive functions and driving aptitude has been extensively studied, motor-cognitive functions required for driving have not been elucidated. METHODS: In this paper, we propose a machine-learning algorithm that introduces sparse regularization to automatically select driving aptitude-related indices from 65 input indices obtained from 10 tests of motor-cognitive function conducted on 55 participants with stroke. Indices related to driving aptitude and their required tests can be identified based on the output probability of the presence or absence of driving aptitude to provide evidence for identifying subjects who must undergo the on-road driving test. We also analyzed the importance of the indices of motor-cognitive function tests in evaluating driving aptitude to further clarify the relationship between motor-cognitive function and driving aptitude. RESULTS: The experimental results showed that the proposed method achieved predictive evaluation of the presence or absence of driving aptitude with high accuracy (area under curve 0.946) and identified a group of indices of motor-cognitive function tests that are strongly related to driving aptitude. CONCLUSIONS: The proposed method is able to effectively and accurately unravel driving-related motor-cognitive functions from a panoply of test results, allowing for autonomous evaluation of driving aptitude in post-stroke individuals. This has the potential to reduce the number of screening tests required and the corresponding clinical workload, further improving personal and public safety and the quality of life of individuals with stroke.

This study was conducted with the aim of developing a circuit system that enables the measurement of the moisture content and ion concentration with a simple circuit configuration. Our previous studies have shown that soil can be represented by an equivalent circuit of a parallel circuit of resistors and capacitors. We designed a circuit that can convert the voltage transient characteristics of the soil when a current is applied to it into a square wave and output frequency information and developed an algorithm to analyze the two types of square waves and calculate R and C. Normal operation was confirmed in the range of 10 kΩ–1 MΩ for the designed circuit, and the calculation algorithm matched within a maximum error of 5%, thus confirming the validity of the program. These successfully confirmed the changes in the water content and ionic concentration. The soil moisture content measurement succeeded in measuring a maximum error of about 10%, except at one point, and the soil ion concentration measurement succeeded in measuring a maximum error of 6.6%. A new, simple, noise-resistant moisture content and ion concentration measurement circuit system with square wave output has been realized.