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Multiple Sclerosis l: Introduction01:19

Multiple Sclerosis l: Introduction

Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...

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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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Fatigue in Multiple Sclerosis: A Resting-State EEG Microstate Study.

Sara Baldini1, Arianna Sartori2, Lucrezia Rossi2

  • 1Department of Medicine, Surgery and Health Sciences, Neurology Unit, Cattinara University Hospital ASUGI, University of Trieste, Trieste, Italy. sara.baldini@gmail.com.

Brain Topography
|June 7, 2024
PubMed
Summary

Fatigue in Multiple Sclerosis (MS) is linked to altered brain network activity. This study found changes in salience and visual networks in fatigued MS patients, suggesting maladaptive functioning contributes to MS fatigue.

Keywords:
EEG MicrostatesFatigueFrequency BandsMultiple Sclerosis

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Area of Science:

  • Neuroscience
  • Clinical Neurology
  • Biomedical Engineering

Background:

  • Fatigue is a prevalent and debilitating symptom in Multiple Sclerosis (MS), affecting up to 80% of patients.
  • The precise neural mechanisms underlying MS-related fatigue remain incompletely understood.
  • Investigating large-scale brain network dynamics offers a promising avenue for elucidating fatigue's origins.

Purpose of the Study:

  • To explore the functional connectivity of spontaneous large-scale brain networks associated with fatigue in people with MS (PwMS).
  • To utilize the electroencephalography (EEG) microstate approach with spectral decomposition to analyze network functioning.
  • To correlate EEG microstate findings with clinical and behavioral measures of fatigue.

Main Methods:

  • Recruited 43 relapsing-remitting MS patients and 24 healthy controls (HCs).
  • Administered the Modified Fatigue Impact Scale (MFIS) to assess fatigue levels.
  • Conducted 15-minute resting-state, high-density EEG recordings for microstate analysis across different frequency bands.

Main Results:

  • Identified six EEG microstate templates across all participants and frequencies.
  • Fatigued MS patients (F-MS) exhibited a significant decrease in microstate F (salience network) in broadband and beta bands compared to healthy controls.
  • F-MS showed a significant increase in microstate B (visual network) in broadband and beta bands compared to HCs.
  • Cognitive fatigue correlated with altered delta band microstate B and beta band microstate F, while physical fatigue correlated with reduced beta band microstate F occurrence.

Conclusions:

  • Elevated fatigue levels in MS may be associated with maladaptive functioning of the salience and visual networks.
  • EEG microstate analysis provides insights into the neural underpinnings of MS fatigue.
  • These findings highlight potential targets for therapeutic interventions aimed at alleviating fatigue in MS.