An application of a hierarchical diffusion model on ambulatory data from Huntington's patients
Huntington's disease is a debilitating neurodegenerative illness involving motor and cognitive impairments throughout its progression, eventually leading to death. Diagnosis is based on motor symptoms; however, the cognitive symptoms are more debilitating. Assessing the disease's consequences on cognition can hint at what processes should be targeted for cognitive-behavioral treatment. We present an application of a hierarchical diffusion model (Ratlciff et al., 2016; Vandekerchkove et al., 2010) to an ambulatory assessment with manifest (HD) and premanifest (PM) Huntington's patients and compare their performance, as assessed by the model, to performance from controls on a numerosity task (McLaren et al., 2020). We found a gradation of impairment across the groups in the mean drift rate, such that: (1) HD always had a lower drift rate than controls; (2) HD had lower drift rates than PM in the "easy" condition, but they had essentially equivalent rates in the "difficult" condition; (3) PM had lower drift rates than controls in the "difficult" condition, but they had essentially equivalent rates in the "easy" condition. These results held even after regressing on age for all groups, and were not observed when analyzing average response times or correct/incorrect response percentages. Our Bayesian approach also allowed us to assess which parameters were most reliably estimated with ambulatory data through the Gelman-Rubin statistic. Overall, we found that the hierarchical diffusion model provided novel insights into the progression of Huntington's disease, with our Bayesian model providing a powerful method of assessment and group separation even with in-home, ambulatory data on mobile phones.