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.

Increased cerebrovascular reactivity in selected brain regions after extracranial-intracranial bypass improves the speed and accuracy of visual cancellation in patients with severe steno-occlusive disease: a preliminary study.

The effect of the change in cerebrovascular reactivity (CVR) in each brain area on cognitive function after extracranial-intracranial bypass (EC-IC bypass) was examined. Eighteen patients who underwent EC-IC bypass for severe unilateral steno-occlusive disease were included. Single-photon emission CT (SPECT) for evaluating CVR and the visual cancellation (VC) task were performed before and after surgery. The accuracy of VC was expressed by the arithmetic mean of the age-matched correct answer rate and the accurate answer rate, and the averages of the time (time score) and accuracy (accuracy score) of the four VC subtests were used. The speed of VC tended to be slower, whereas accuracy was maintained before surgery. The EC-IC bypass improved CVR mainly in the cerebral hemisphere on the surgical side. On bivariate analysis, when CVR increased post-operatively, accuracy improved on both surgical sides, but the time score was faster on the left and slower on the right surgical side. Stepwise multiple regression analysis showed that the number of the brain regions associated with the time score was 5 and that associated with the accuracy score was 4. In the hemodynamically ischemic brain, processing speed might be adjusted so that accuracy would be maintained based on the speed-accuracy trade-off mechanism that may become engaged separately in the left and right cerebral hemispheres when performing VC. When considering the treatment for hemodynamic ischemia, the relationship between CVR change and the speed-accuracy trade-off in each brain region should be considered.

The right hemisphere is important for driving-related cognitive function after stroke.

Considering quality of life (QOL) after stroke, car driving is one of the most important abilities for returning to the community. In this study, directed attention and sustained attention, which are thought to be crucial for driving, were examined. Identification of specific brain structure abnormalities associated with post-stroke cognitive dysfunction related to driving ability would help in determining fitness for car driving after stroke. Magnetic resonance imaging was performed in 57 post-stroke patients (51 men; mean age, 63 ± 11 years) who were assessed for attention deficit using a standardized test (the Clinical Assessment for Attention, CAT), which includes a Continuous Performance Test (CPT)-simple version (CPT-SRT), the Behavioral Inattention Test (BIT), and a driving simulator (handle task for dividing attention, and simple and selective reaction times for sustained attention). A statistical non-parametric map (SnPM) that displayed the association between lesion location and cognitive function for car driving was created. From the SnPM analysis, the overlay plots were localized to the right hemisphere during handling the hit task for bilateral sides (left hemisphere damage related to right-side neglect and right hemisphere damage related to left-side neglect) and during simple and selective reaction times (false recognition was related to damage of both hemispheres). A stepwise multiple linear regression analysis confirmed the importance of both hemispheres, especially the right hemisphere, for cognitive function and car driving ability. The present study demonstrated that the right hemisphere has a crucial role for maintaining directed attention and sustained attention, which maintain car driving ability, improving QOL for stroke survivors.

Cognitive screening test for rehabilitation using spatiotemporal data extracted from a digital trail making test part-A.

We investigated a newly developed digitized Trail Making Test using an iPad (iTMT) as a brief cognitive function screening test. We found that the iTMT part-A (iTMT-A) can estimate generalized cognitive function in rehabilitation inpatients examined using the Mini-Mental State Examination (MMSE). Forty-two hospitalized participants undergoing rehabilitation (rehab participants), 30 of whom had cerebral infarction/hemorrhage (stroke participants), performed the iTMT five times (first three times: iTMT-A; fourth: paper version of TMT-A; fifth: the inverse version of iTMT-A) and the MMSE once. Each iTMT-A trial's completion time was divided into the move and dwell times. A linear mixed model following post-hoc tests revealed that the completion time of the third and fourth iTMT-A was faster compared to that of the first iTMT-A, suggesting the presence of a learning effect. In the partial least squares (PLS) regression analysis, the coefficient of determination for estimating the MMSE score was increased by using the dwell and move times extracted from the repeated iTMT-A and the availability of TMT-B, even for subjects with low MMSE scores. These findings indicate that the dwell time of iTMT-A may be important for estimating cognitive function. The iTMT-A extracts significant factors temporally and spatially, and by incorporating the learning effect of repeated trials, it may be possible to screen cognitive and physical functions for rehabilitation patients.

Pen-point Trajectory Analysis During Trail Making Test Based on a Time Base Generator Model.

The Trail Making test (TMT) is a widely used neuropsychological test to assess the cognitive function of patients. This paper presents the analysis method of pen-point trajectory during the TMT based on a time base generator (TBG). In the proposed method, the movement segments between targets are first extracted from pen-point trajectories, which are measured during performance of the TMT on an iPad. By fitting the extracted trajectories with a TBG-based trajectory generation model, the proposed method can then calculate quantitative indices representing the shape and collapse of the velocity profile. In the experiment, we analyzed TMT data from 25 stroke patients who were classified into three groups according to their scores on the Mini-Mental State Examination (MMSE). The results revealed that most of the measured inter-target trajectories had unimodal bell-shaped velocity profiles, as seen in reaching movements. Furthermore, we found that the degree of collapse in the velocity profile shape increased significantly when the cognitive function decreased.

Relationships between motor and cognitive functions and subsequent post-stroke mood disorders revealed by machine learning analysis.

Mood disorders (e.g. depression, apathy, and anxiety) are often observed in stroke patients, exhibiting a negative impact on functional recovery associated with various physical disorders and cognitive dysfunction. Consequently, post-stroke symptoms are complex and difficult to understand. In this study, we aimed to clarify the cross-sectional relationship between mood disorders and motor/cognitive functions in stroke patients. An artificial neural network architecture was devised to predict three types of mood disorders from 36 evaluation indices obtained from functional, physical, and cognitive tests on 274 patients. The relationship between mood disorders and motor/cognitive functions were comprehensively analysed by performing input dimensionality reduction for the neural network. The receiver operating characteristic curve from the prediction exhibited a moderate to high area under the curve above 0.85. Moreover, the input dimensionality reduction retrieved the evaluation indices that are more strongly related to mood disorders. The analysis results suggest a stress threshold hypothesis, in which stroke-induced lesions promote stress vulnerability and may trigger mood disorders.