Neural Network Activity during Visuomotor Adaptation

  • Henry Darch

Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)


The vertebrate brain can rapidly adjust voluntary movements in response to errors through a process of trial-by-trial motor adaptation. There are several regions of the brain known to influence this adaptation of voluntary movements. The cerebellum is the principal area associated with adaptive movements and is thought to enable predictions of the consequences of movements. The motor cortex has been implicated in the long-term memory and maintenance of appropriate patterns of neural activity to execute accurate movements- the motor engram. The prefrontal cortex has also been implicated in the implementation of cognitive strategies and maintaining task engagement.
Evidence to-date showing each of these areas’ influence on motor adaptation suggests a functional network that works together to appropriately modify motor actions in response to behavioural errors. The neural mechanisms by which these distant nodes communicate with one another are not yet understood.
The present study has investigated the neurophysiological changes that occur in the neural activity of the cerebellum (paravermal cortex), motor cortex and prefrontal cortex, as well as the level of network communication between them, during a visuomotor adaptation paradigm in both humans and cats. In both species, neural population activity (EEG in humans and local field potential activity in cats) showed a modulation of beta frequency oscillations in the primary motor cortex just prior to movement, specific to early stages of adaptation. No significant effects were observed in the cerebellar cortex and phase synchrony between the three brain areas was unchanged during adaptation.
Together, these data suggest changes in motor cortical activity related to adaptation of reaching movements but no detectable changes to functional connectivity between the distributed brain nodes involved in motor adaptation. Contrary to predictions related to updating internal models during adaptation, this suggests neural network connectivity remains similar throughout the motor learning process.
Date of Award19 Mar 2019
Original languageEnglish
Awarding Institution
  • The University of Bristol
SupervisorRichard Apps (Supervisor) & Iain D Gilchrist (Supervisor)


  • Motor Adaptation
  • Cerebellum
  • Neural Network

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