Multiple sclerosis (MS) is an important and incurable disease starting in 1/1000 young adults. Cognitive deterioration in MS (CDiMS) is a frequent and unpredictable complication with high impact on work and daily living.

Finding a highly accurate biomarker for CDiMS would be game changing for patients adjusting their life-plans, neurologists advising their patients on optimal treatment strategies, and researchers developing new compounds to treat the disease.

This CDiMS biomarker should be more objective and reliable than standard neuropsychological tests, less sensitive to confounding effects of physical disability than neuropsychological testing, easy to acquire, sensitive to interventions.

The solution to this challenge may not lie in how the MS-affected brain looks like, or in which neurochemicals it produces, but rather in how it functions, and this can be examined using high resolution electromagnetic signals from the brains of MS patients and controls.

To do this, new basic research is needed:

1)Taking into account that the brain responds to every cognitive stimulus in a different way, since the standard analysis techniques discard the information contained in this inter-trial variability
2)Leveraging new methods for deep learning that allow us to very accurately extract information from brain signals

My group has already collected a unique dataset for this work, and I have established an international network with experts in the field to succeed in this project.
Effective start/end date1/10/1930/04/24

    Flemish discipline codes

  • Cognitive neuroscience

ID: 47533468