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Altered network recruitment in multiple sclerosis patients during resting state.
Annika Lüttjohann1, Saskia Räuber2, Melanie Korsen2
1Institute of Physiology I, University of Münster, 48149, Münster, Germany.
Multiple Sclerosis and Related Disorders
|March 8, 2026
Summary
Electroencephalography (EEG) in Multiple Sclerosis (MS) patients shows altered brain activity, particularly in beta frequency bands. This desynchronized EEG activity correlates with clinical markers, suggesting compensatory brain network changes.
Area of Science:
- Neuroscience
- Neurology
- Biomedical Engineering
Background:
- Electroencephalography (EEG) records neuronal activity for neurological disorder research.
- Time-frequency analysis (TFA) reveals hidden EEG data, often using predefined frequency bands.
- Cluster-based permutation statistics offer unbiased comparison of complex neurophysiological data.
Purpose of the Study:
- To investigate differences in resting-state EEG spectral power between Multiple Sclerosis (MS) patients and controls.
- To correlate EEG findings with clinical markers of MS severity, including RNFL thickness and evoked potentials.
Main Methods:
- Resting-state surface EEG data from 51 MS and 51 control patients were analyzed using TFA.
- Cluster-based permutation statistics were employed for comparative analysis.
- EEG results were correlated with optical coherence tomography (OCT) for RNFL thickness, evoked potentials (EPs), and neuropsychological data.
Main Results:
- MS patients exhibited increased power in low and high beta frequency bands compared to controls.
- High beta band spectral power at O1 showed a negative correlation with RNFL thickness.
- Observed differences in beta power were dependent on the evoked potential scores in MS patients.
Conclusions:
- MS patients display more desynchronized EEG activity, linked to clinical disease indicators.
- Findings suggest altered brain network recruitment, potentially as a compensatory mechanism in MS.
- These network changes may underlie the clinical impairments observed in Multiple Sclerosis.
Keywords:
Cluster-based permutation statisticsElectroencephalographyMultiple sclerosisTime-frequency analysisMore Related Videos
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