Frontiers in Computational Neuroscience (Dec 2013)
Fronto-striatal grey matter contributions to discrimination learning in Parkinson’s disease
Abstract
Discrimination learning deficits in Parkinson’s disease (PD) have been well established. Using both behavioural patient studies and computational approaches, these deficits have typically been attributed to dopamine imbalance across the basal ganglia. However, this explanation of impaired learning in PD does not account for the possible contribution of other pathological changes that occur in the disease process, importantly including grey matter loss. To address this gap in the literature, the current study explored the relationship between fronto-striatal grey matter atrophy and learning in PD. We employed a discrimination learning task and computational modelling in order to assess learning rates in non-demented PD patients. Behaviourally, we confirmed that learning rates were reduced in patients relative to controls. Furthermore, voxel-based morphometry imaging analysis demonstrated that this learning impairment was directly related to grey matter loss in discrete fronto-striatal regions (specifically, the ventromedial prefrontal cortex, inferior frontal gyrus and nucleus accumbens). These findings suggest that dopaminergic imbalance may not be the sole determinant of discrimination learning deficits in PD, and highlight the importance of factoring in the broader pathological changes when constructing models of learning in PD.
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