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Multiple Sclerosis l: Introduction

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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Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

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Temporally Defined Brain Network Activation Associated With Slowed Information Processing Speed in Multiple

Olivier Burta^1,2, Fahimeh Akbarian^1,2, Chiara Rossi^1,2

¹AIMS Lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.

Human Brain Mapping

|February 18, 2026

Summary

Information processing speed (IPS) deficits in multiple sclerosis (MS) involve sensory, cognitive, and motor speed. Reduced prefrontal activation was observed in people with MS (PwMS), impacting task performance.

Keywords:

MEG task TDE‐HMM dynamic functional networks information processing speed multiple sclerosis symbol digit modalities test

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Published on: June 13, 2025

Area of Science:

Neuroscience
Cognitive Science
Biomedical Engineering

Background:

Information processing speed (IPS) is a key cognitive deficit in multiple sclerosis (MS).
Previous research linked IPS to frontal brain regions but lacked temporal resolution.
Understanding the neural basis of IPS is crucial for developing targeted MS therapies.

Purpose of the Study:

To investigate the neural underpinnings of IPS deficits in MS using advanced neuroimaging analysis.
To identify distinct brain states associated with different aspects of IPS.
To explore the relationship between brain activity, task performance, and clinical outcomes in MS.

Main Methods:

Utilized magnetoencephalography (MEG) data from healthy controls and people with MS (PwMS) performing the Symbol Digit Modalities Test (SDMT).
Employed a data-driven time delay embedded-hidden Markov model (TDE-HMM) to analyze neural activity.
Identified spectrally defined, task-specific brain states with distinct temporal and spatial characteristics.

Main Results:

The TDE-HMM identified five task-relevant brain states contributing to IPS: sensory, cognitive, and motor speed.
PwMS exhibited reduced prefrontal activation compared to controls.
Peak neural activity in prefrontal, frontoparietal, and occipital networks correlated with reaction time and SDMT performance.

Conclusions:

IPS in MS is supported by a network involving sensory (occipital), cognitive (prefrontal/frontoparietal), and motor (sensorimotor) speed.
Neural alterations in PwMS, particularly in prefrontal regions, are associated with cognitive deficits.
Findings provide a foundation for future therapeutic strategies aimed at improving IPS in MS.