Brain states analysis of EEG predicts multiple sclerosis and mirrors disease duration and burden

Abstract: Background: Any treatment of multiple sclerosis should preserve mental function, considering how cognitive deterioration interferes with quality of life. However, mental assessment is still realized with neuro-psychological tests without monitoring cognition on neurobiological grounds whereas the ongoing neural activity is readily observable and readable. Objectives: The proposed method deciphers electrical brain states which as multi-dimensional cognetoms quantitatively discriminate normal from pathological patterns in an EEG. Methods: Baseline recordings from a prior EEG study of 93 subjects, 37 with MS, were analyzed. Spectral bands served to compute cognetoms and categorize subsequent feature combination sets. Results: A significant correlation arose between brain states predictors, clinical data and disease duration. Using cognetoms and spectral bands, a cross-sectional comparison separated patients from controls with a precision of 82% while using bands alone arrived at 64%. Conclusions: Brain states analysis successfully distinguishes controls from patients with MS. The congruity with disease duration is a neurobiological indicator for disease accumulation over time. Our results imply that data-driven comparisons of EEG data may complement customary diagnostic methods in neurology and psychiatry. However, thinking ahead for quantitative monitoring of disease time course and treatment efficacy, we hope to have established the analytic principles applicable to longitudinal clinical studies.