Evidence map›Paper›PMID 40092299›Full record

ArticleFrontiers in neuroinformatics2025

Impact of interferon-β and dimethyl fumarate on nonlinear dynamical characteristics of electroencephalogram signatures in patients with multiple sclerosis.

Christopher Ivan Hernandez, Natalia Afek, Magda Gawłowska, Paweł Oświęcimka, Magdalena Fafrowicz, Agnieszka Slowik, Marcin Wnuk, Monika Marona, Klaudia Nowak, Kamila Zur-Wyrozumska and 4 more

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Article in Frontiers in neuroinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Christopher Ivan HernandezComputational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States.
Natalia AfekDoctoral School in the Social Sciences, Jagiellonian University, Kraków, Poland.
Magda GawłowskaDepartment of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków, Poland.
Paweł OświęcimkaComplex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, Kraków, Poland.
Magdalena FafrowiczDepartment of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków, Poland.
Agnieszka SlowikDepartment of Neurology, Jagiellonian University Medical College, Kraków, Poland.
Marcin WnukDepartment of Neurology, Jagiellonian University Medical College, Kraków, Poland.
Monika MaronaDepartment of Neurology, Jagiellonian University Medical College, Kraków, Poland.
Klaudia NowakDepartment of Neurology, Jagiellonian University Medical College, Kraków, Poland.
Kamila Zur-WyrozumskaCentre for Innovative Medical Education, Jagiellonian University Medical College, Kraków, Poland.
Mary Jean AmonDepartment of Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States.
P A HancockDepartment of Psychology, University of Central Florida, Orlando, FL, United States.
Tadeusz MarekFaculty of Psychology, SWPS University, Katowice, Poland.
Waldemar KarwowskiComputational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 ( 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.

Indexed as

complexityelectroencephalogramHiguchi’s fractal dimensionmultiple sclerosisnonlinear dynamicssample entropy

Identifiers

PMID40092299
PMCPMC11906706

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.