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Ocular Motor Study

The aim of this project is to fully characterise eye movement changes in ME/CFS on two consecutive days, identifying an ocular motor signature that is unique to the disorder.

  • Jade Bartholomew
  • Wenjie Shan
  • Michael Menden, PhD
  • Meaghan Clough, PhD
  • Natalie Thomas, PhD
  • Joanne Fielding, PhD
  • Christopher Armstrong, PhD
  • First publication being worked on.
  • Only a subtle difference between controls and patients was observed with the first set of tests.
  • New PhD student will conduct a deep analysis of the raw data to identify if important data was missed.
STUDY HYPOTHESIS AND DESCRIPTION

Ocular motor (eye movement) assessment can be used in the  diagnosis of various neurological diseases. Eye movement  requires signaling across a vast, well-defined neural network  that incorporates over 50% of the brain. Damage at any point  across this extensive network manifests as abnormalities in  eye movement. In a given disease/disorder, this manifests in a  unique eye movement signature that can be measured using  high powered eye-tracking technologies, allowing the  quantification of even the subtlest of changes. This is  especially relevant for those with ME/CFS as symptoms can  often be subtle and prone to fluctuation (i.e. tending to  worsen following exertion).

A defined ocular motor signature for ME/CFS would provide  the first, objective, quantifiable marker for this disease that  can be used to provide diagnostic certainty, provide a sensitive  measure of progression or future treatment effect, and to  inform the pathophysiological underpinnings of the disease.

STUDY DESIGN

  1. Identify fatigue signatures in ME/CFS using simple and repetitive ocular motor tasks over time.
  2. Identify cognitive control changes in ME/CFS using validated cognitive ocular motor tasks.
  3. Characterise eye movement changes due to PEM induced by cognitive exertion.
  4. Develop a diagnostic algorithm and task set that incorporates a variety of ocular motor task signatures in ME/CFS using machine learning techniques.
  5. Combine biofluid collections with 2-day ocular motor tests for metabolite and immune marker analysis.

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