Quantitative Neuro-Assessment: MRI vs. Electrophysiology in CNS Disease Biomarker Science

Personalized neurological care in hospital settings leverages artificial intelligence (AI) to enhance the quantification of structural and functional biomarkers using magnetic resonance imaging (MRI) and electrophysiological techniques (EEG, MEG, evoked potentials). This comprehensive review examines magnetic resonance imaging (MRI) and electrophysiological techniques (EEG, MEG, evoked potentials) across multiple sclerosis (MS), spinal cord injury (SCI), Alzheimer's disease (AD), and Parkinson's disease (PD). MRI delivers superior spatial resolution (0.5-1 mm) for anatomical quantification through standardized protocols and automated tools (FreeSurfer, FSL), enabling reproducible measurement of lesion volume, cortical thinning, and microstructural integrity. Electrophysiological methods provide millisecond temporal resolution for functional assessment but face quantification challenges from signal noise and processing complexity. Our integrated analysis reveals MRI's advantage in quantification reproducibility (ICC = 0.92 vs. EEG's 0.76) and diagnostic yield for structural pathologies (70.6% vs. 0% in first seizures), while electrophysiology excels in dynamic monitoring (VEP delays in MS; beta oscillations in PD). Critically, these modalities demonstrate complementarity: Combined EEG-fMRI improves epileptogenic zone localization by 32%, and AI-driven fusion achieves 94% accuracy in AD classification. The path forward requires harmonized quantification standards, portable hybrid technologies, and validated multimodal biomarkers to advance personalized neurology.