Multiple sclerosis and related disorders · 2026
Brain states analysis of EEG predicts multiple sclerosis and mirrors disease duration and burden.
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Mórocz István, Jouzizadeh Mojtaba, Ghaderi Amir Hossein, Cheraghmakani Hamed, Baghbanian Seyed Mohammad, Khanbabaie Reza, Mogoutov Andrei
Аннотация
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.
OBJECTIVE: The proposed method deciphers electrical brain states which as multi-dimensional cognetoms quantitatively discriminate normal from pathological patterns in an EEG.
METHOD: Baseline recordings from a prior EEG study of 88 subjects, 36 with MS, were analyzed. Spectral bands served to compute cognetoms and categorize subsequent feature combination sets.
RESULT: The brain states predictor correlates with disease burden and duration. Using cognetoms and spectral bands, a cross-sectional comparison separated patients from controls with a precision of 85% while using bands alone arrived at 79%.
CONCLUSION: We demonstrate the efficiency of the quantitative data-driven method based on brain states analysis by contrasting EEG data of patients with MS and healthy subjects. The congruity with disease severity and duration is a neurophysiological indicator for disease accumulation over time. We discuss potential applications of the approach for the monitoring of disease time course and treatment efficacy in longitudinal clinical studies in psychiatry and neurology.
OBJECTIVE: The proposed method deciphers electrical brain states which as multi-dimensional cognetoms quantitatively discriminate normal from pathological patterns in an EEG.
METHOD: Baseline recordings from a prior EEG study of 88 subjects, 36 with MS, were analyzed. Spectral bands served to compute cognetoms and categorize subsequent feature combination sets.
RESULT: The brain states predictor correlates with disease burden and duration. Using cognetoms and spectral bands, a cross-sectional comparison separated patients from controls with a precision of 85% while using bands alone arrived at 79%.
CONCLUSION: We demonstrate the efficiency of the quantitative data-driven method based on brain states analysis by contrasting EEG data of patients with MS and healthy subjects. The congruity with disease severity and duration is a neurophysiological indicator for disease accumulation over time. We discuss potential applications of the approach for the monitoring of disease time course and treatment efficacy in longitudinal clinical studies in psychiatry and neurology.