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.