# Multimodal Neuroimaging and Electrophysiological Markers in Multiple Sclerosis: An Integrative Review of fMRI, EEG, and EMG Approaches: fMRI, EEG, EMG in Multiple Sclerosis

## TL;DR

This review explores how combining fMRI, EEG, and EMG can improve understanding and treatment of multiple sclerosis by capturing brain and muscle function changes.

## Contribution

The paper integrates recent findings from 2019 to 2024 on multimodal neuroimaging and electrophysiological approaches in MS.

## Key findings

- fMRI reveals compensatory brain plasticity and connectivity breakdowns in MS progression.
- EEG detects cortical changes linked to fatigue and cognitive decline in real time.
- EMG effectively identifies neuromuscular impairments like spasticity and gait issues.

## Abstract

Multiple sclerosis (MS) is a chronic neurological disease marked by
demyelination, neurodegeneration, and widespread network dysfunction. While
conventional MRI remains central to diagnosis, advanced techniques such as
functional MRI (fMRI), electroencephalography (EEG), and electromyography (EMG)
are increasingly recognized for their ability to capture dynamic functional
changes that underlie clinical symptoms. This review explores the individual and
combined applications of fMRI, EEG, and EMG in MS, emphasizing recent clinical
findings from 2019 to 2024. fMRI provides high-resolution mapping of brain
activation and connectivity, revealing compensatory plasticity in early stages
and connectivity breakdowns associated with progression. EEG offers real-time
monitoring of cortical activity, detecting spectral slowing, network
reorganization, and neurophysiological correlates of fatigue and cognitive
decline. EMG quantifies neuromuscular output, identifying spasticity, motor unit
loss, and gait disturbances with high sensitivity. Integration of these
modalities enhances spatial and temporal resolution; however, challenges such as
data standardization and interpretive variability must be addressed to ensure
robust biomarker development. Advances in machine learning, portable EEG/EMG
systems, and big-data infrastructure are driving the translation of multimodal
monitoring into clinical practice. Real-time assessments and individualized
biomarker profiles could enable earlier diagnosis, more accurate prognosis, and
personalized rehabilitation and therapy strategies. Although technical,
interpretive, and standardization challenges remain, the convergence of fMRI,
EEG, and EMG offers a promising path toward precision medicine in MS. Multimodal
approaches not only deepen understanding of MS pathophysiology but also hold
tangible potential to transform disease monitoring, treatment decision-making,
and patient outcomes.