Impact of interferon-β and dimethyl fumarate on nonlinear dynamical characteristics of electroencephalogram signatures in patients with multiple sclerosis
Impact of interferon-β and dimethyl fumarate on nonlinear dynamical characteristics of electroencephalogram signatures in patients with multiple sclerosis
StatusVoR
Alternative title
Authors
Hernandez, Christopher Ivan
Afek, Natalia
Gawłowska, Magda
Oświęcimka, Paweł
Fafrowicz, Magdalena
Slowik, Agnieszka
Wnuk, Marcin
Marona, Monika
Nowak, Klaudia
Zur-Wyrozumska, Kamila
Monograph
Monograph (alternative title)
Date
2025-02-28
Publisher
Journal title
Frontiers in Neuroinformatics
Issue
Volume
19
Pages
Pages
1-14
ISSN
1662-5196
ISSN of series
Access date
2025-02-28
Abstract PL
Abstract EN
Introduction: Multiple sclerosis (MS) is an intricate neurological condition that affects many individuals worldwide, and there is a considerable amount of research into understanding the pathology and treatment development. Nonlinear analysis has been increasingly utilized in analyzing electroencephalography (EEG) signals from patients with various neurological disorders, including MS, and it has been proven to be an effective tool for comprehending the complex nature exhibited by the brain.
Methods: This study seeks to investigate the impact of Interferon-β (IFN-β) and dimethyl fumarate (DMF) on MS patients using sample entropy (SampEn) and Higuchi’s fractal dimension (HFD) on collected EEG signals. The data were collected at Jagiellonian University in Krakow, Poland. In this study, a total of 175 subjects were included across the groups: IFN-β (n = 39), DMF (n = 53), and healthy controls (n = 83).
Results: The analysis indicated that each treatment group exhibited more complex EEG signals than the control group. SampEn had demonstrated significant sensitivity to the effects of each treatment compared to HFD, while HFD showed more sensitivity to changes over time, particularly in the DMF group.
Discussion: These findings enhance our understanding of the complex nature of MS, support treatment development, and demonstrate the effectiveness of nonlinear analysis methods.
Abstract other
Keywords PL
Keywords EN
electroencephalogram
complexity
nonlinear dynamics
sample entropy
Higuchi’s fractal dimension
multiple sclerosis
complexity
nonlinear dynamics
sample entropy
Higuchi’s fractal dimension
multiple sclerosis