Cognitive structure and progression in Parkinson’s Disease: Insights from a tablet-based assessment

Cognitive impairment represents an important burden for patients with Parkinson’s disease (PwPD). Digital tools may improve accessibility and provide richer assessments than traditional paper–pencil tests; open-source generation can support the availability of comparable assessments in different cohorts. We therefore implemented a digital, tablet-based cognitive assessment (DiCo) comprising 13 commonly used tests as an open-source tool. 97 PwPD without overt cognitive impairment (43% women) completed the entire DiCo. Clustering of feature correlations and conditional dependencies indicated a predominantly mutual organization of cognitive performance in PwPD. Exploratory factor analysis identified five interrelated latent factors, most of which were derived from single tests. Factors correlated moderately with traditional neuropsychological tests and questionnaires. Machine learning identified working memory as the most predictive features of the MoCA. Latent profile analysis revealed four cognitive subgroups, mainly reflecting severity, with one group characterized by selective reflection impulsivity. Exploratory longitudinal analyses suggest partly independent trajectories of cognitive and motor progression, with a data-driven composite score detecting changes not captured by the MDS UPDRS III. Taken together, the DiCo demonstrated good feasibility in PD, and individual tests might be sufficient to substitute for MoCA or FAB in a research context.