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Huntington’s disease; Reinforcement learning; Ventral striatum; Gray matter density
Abstract:
Huntington’s disease (HD) is an autosomal dominant neurodegenerative condition characterized by a triad of movement disorder, neuropsychiatric symptoms and cognitive deficits. The striatum is particularly vulnerable to the effects of mutant huntingtin, and cell loss can already be found in presymptomatic stages. Since the striatum is well known for its role in reinforcement learning, we hypothesized to find altered behavioral and neural responses in HD patients in a probabilistic reinforcement learning task performed during functional magnetic resonance imaging. We studied 24 HD patients without central nervous system (CNS)-active medication and 25 healthy controls. Twenty HD patients and 24 healthy controls were able to complete the task. Computational modeling was used to calculate prediction error values and estimate individual parameters. We observed that gray matter density and prediction error signals during the learning task were related to disease stage. HD patients in advanced disease stages appear to use a less complex strategy in the reversal learning task. In contrast, HD patients in early disease stages show intact encoding of learning signals in the degenerating left ventral striatum. This effect appears to be lost with disease progression.
