Digitally generated Trail Making Test data: Analysis using hidden Markov modeling
Mengtian Du, Stacy L. Andersen, Stephanie Cosentino, Robert M. Boudreau, Thomas T. Perls, Paola Sebastiani
Abstract
The Trail Making Test (TMT) is a neuropsychological test used to assess cognitive dysfunction. The TMT consists of two parts: TMT-A requires connecting numbers 1 to 25 sequentially; TMT-B requires connecting numbers 1 to 12 and letters A to L sequentially, alternating between numbers and letters. We propose using a digitally recorded version of TMT to capture cognitive or physical functions underlying test performance. We analyzed digital versions of TMT-A and -B to derive time metrics and used Bayesian hidden Markov models to extract additional metrics. We correlated these derived metrics with cognitive and physical function scores using regression. On both TMT-A and -B, digital metrics associated with graphomotor processing test scores and gait speed. Digital metrics on TMT-B were additionally associated with episodic memory test scores and grip strength. These metrics provide additional information of cognitive state and can differentiate cognitive and physical factors affecting test performance.