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

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.