Impact of Interferon-β and Dimethyl Fumarate on Nonlinear Dynamical Characteristics of Electroencephalogram Signatures in Patients with Multiple Sclerosis

Christopher Ivan Hernandez ^1* Natalia Afek ² Magda Gawłowska ³ Pawel Oswiecimka ^4,5 Magdalena Fafrowicz ³ Agnieszka Slowik ^6,7 Marcin Wnuk ^6,7 Monika Marona ^6,7 Klaudia Nowak ^6,7 Kamila Zur-Wyrozumska ^8,9 Tadeusz Marek ¹⁰ Waldemar Karwowski ^1*

• ¹ Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, College of Engineering and Computer Science, University of Central Florida, Orlando, Florida, United States
• ² Doctoral School in the Social Sciences, Jagiellonian University, Krakow, Poland
• ³ Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Krakow, Poland
• ⁴ Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, Krakow, Poland
• ⁵ Mark Kac Centre for Complex Systems Research, Jagiellonian University, Krakow, Poland
• ⁶ Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
• ⁷ Department of Neurology, University Hospital in Krakow, Krakow, Poland
• ⁸ Department of Medical Education, Jagiellonian University Medical College, Krakow, Lesser Poland, Poland
• ⁹ Department of Neurology, 5th Military Clinical Hospital, Krakow, Poland
• ¹⁰ Faculty of Psychology, SWPS University, Katowice, Silesian, Poland

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. 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 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. These findings enhance our understanding of the complex nature of MS, support treatment development, and demonstrate the effectiveness of nonlinear analysis methods.