Fronto-striatal gray matter contributions to discrimination learning in Parkinson's disease

Claire O'Callaghan, Ahmed A. Moustafa, Sanne De Wit, James M. Shine, Trevor W. Robbins, Simon J. G. Lewis, Michael Hornberger

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Discrimination learning deficits in Parkinson's disease (PD) have been well-established. Using both behavioral 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 gray matter loss. To address this gap in the literature, the current study explored the relationship between fronto-striatal gray matter atrophy and learning in PD. We employed a discrimination learning task and computational modeling in order to assess learning rates in non-demented PD patients. Behaviorally, 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 gray 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.

Original languageEnglish
Article number180
JournalFrontiers in Computational Neuroscience
Volume7
DOIs
Publication statusPublished - 12 Dec 2013

Keywords

  • Computational modeling
  • Discrimination learning
  • Fronto-striatal
  • Goal-directed learning
  • Parkinson's disease
  • Voxel-based morphometry

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