Early-Stage Alzheimer’s Disease Affects Fast But Not Slow Adaptive Processes in Motor Learning

Katrin Sutter, Leonie Oostwoud Wijdenes, Robert J. van Beers, Jurgen A.H.R. Claassen, Roy P.C. Kessels, W. Pieter Medendorp*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Alzheimer’s disease (AD) is characterized by an initial decline in declarative memory, while nondeclarative memory processing remains relatively intact. Error-based motor adaptation is traditionally seen as a form of nondeclarative memory, but recent findings suggest that it involves both fast, declarative, and slow, nondeclarative adaptive processes. If the declarative memory system shares resources with the fast process in motor adaptation, it can be hypothesized that the fast, but not the slow, process is disturbed in AD patients. To test this, we studied 20 early-stage AD patients and 21 age-matched controls of both sexes using a reach adaptation paradigm that relies on spontaneous recovery after sequential exposure to opposing force fields. Adaptation was measured using error clamps and expressed as an adaptation index (AI). Although patients with AD showed slightly lower adaptation to the force field than the controls, both groups demonstrated effects of spontaneous recovery. The time course of the AI was fitted by a hierarchical Bayesian two-state model in which each dynamic state is characterized by a retention and learning rate. Compared to controls, the retention rate of the fast process was the only parameter that was significantly different (lower) in the AD patients, confirming that the memory of the declarative, fast process is disturbed by AD. The slow adaptive process was virtually unaffected. Since the slow process learns only weakly from an error, our results provide neurocomputational evidence for the clinical practice of errorless learning of everyday tasks in people with dementia.

Original languageEnglish
Article numberENEURO.0108-24.2024
Pages (from-to)1-13
Number of pages13
JournaleNeuro
Volume11
Issue number6
Early online date31 May 2024
DOIs
Publication statusPublished - Jun 2024

Bibliographical note

Publisher Copyright:
© 2024, Society for Neuroscience. All rights reserved.

Keywords

  • declarative memory
  • dual-rate learning
  • force-field adaptation
  • hierarchical Bayesian modeling
  • reaching

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