Machine Learning Based Digital Assessment of Mild Cognitive Impairment Using Hand Movements during the Trail Making Test

One of the objectives of digital neuropsychology is to apply computational methods to improve the accuracy of traditional assessments. We created and evaluated a computerized TMT (cTMT) that preserves its original structure and records high-resolution mouse trajectories. Seventy-four older adults (41 with mild cognitive impairment and 33 healthy controls) completed the cTMT and a standard diagnostic battery. We also developed NeuroTask, a Python library that extracts features from cursor time series, including reaction times, speed and acceleration metrics, trajectory deviations, and state-based measures. We compared demographic, digital, and non-digital models using nested cross-validation and non-parametric permutation tests. Demographic models provided only modest discrimination (AUC = 0.56). Digital hand features improved performance (AUC = 0.65), and combining them with demographics reached an AUC of 0.70, which approached the performance of the neuropsychological battery used to define the diagnosis (AUC = 0.76). In complementary regression analyses with digital plus demographic features, we obtained significant predictions for five of seven target scores: MMSE, Digit Symbol, TMT-A, TMT-B, and Forward Digit Span. These results indicate that fine-grained hand-movement features from the cTMT provide useful information for classifying mild cognitive impairment and for predicting multiple neuropsychological scores.