Measurement of emotional states of zebrafish through integrated analysis of motion and respiration using bioelectric signals

Motor-cognitive functions required for driving in post-stroke individuals identified via machine-learning analysis

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.

Recording the ventilation activity of free-swimming zebrafish and its application to novel tank tests

Bioelectric signals related to ventilatory movements in fish can be detected via externally located electrodes. In this study, a technique to continuously monitor the electric ventilatory signals in free-swimming zebrafish was developed. This technique was applied to monitoring ventilation activity as a physiological measure in conjunction with various behavioral measures in a novel tank environment. It was found that in addition to ventilation rate, time domain analysis of changes in ventilation rate is useful for evaluating the emotional state of zebrafish. By integrating the physiological and behavioral measures in analyses, a 1 h novel tank test trial revealed that the habituation process involves two phases. The first phase, which lasted 10 min, involved rapid attenuation of the initial fear/anxiety response to encountering a novel environment. The second phase lasted 20 min and involved further attenuation of anxiety and an increase in exploration behavior. These data suggest that combining ventilation-related physiological measures with conventional behavioral measures enables multidimensional examination of the habituation process in a novel tank environment with more precision than is possible when relying on behavioral responses alone.