Reversal learning reveals cognitive deficits and altered prediction error encoding in the ventral striatum in Huntington’s disease

Reversal learning reveals cognitive deficits and altered prediction error encoding in the ventral... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain Imaging and Behavior Springer Journals

Reversal learning reveals cognitive deficits and altered prediction error encoding in the ventral striatum in Huntington’s disease

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Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Biomedicine; Neurosciences; Neuroradiology; Neuropsychology; Psychiatry
ISSN
1931-7557
eISSN
1931-7565
D.O.I.
10.1007/s11682-016-9660-0
Publisher site
See Article on Publisher Site

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.

Journal

Brain Imaging and BehaviorSpringer Journals

Published: Dec 5, 2016

References

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