Frontiers in Neuroinformatics最新文献

{"title":"Modeling of whole brain sleep electroencephalogram using deep oscillatory neural network.","authors":"Sayan Ghosh, Dipayan Biswas, N R Rohan, Sujith Vijayan, V Srinivasa Chakravarthy","doi":"10.3389/fninf.2025.1513374","DOIUrl":"https://doi.org/10.3389/fninf.2025.1513374","url":null,"abstract":"<p><p>This study presents a general trainable network of Hopf oscillators to model high-dimensional electroencephalogram (EEG) signals across different sleep stages. The proposed architecture consists of two main components: a layer of interconnected oscillators and a complex-valued feed-forward network designed with and without a hidden layer. Incorporating a hidden layer in the feed-forward network leads to lower reconstruction errors than the simpler version without it. Our model reconstructs EEG signals across all five sleep stages and predicts the subsequent 5 s of EEG activity. The predicted data closely aligns with the empirical EEG regarding mean absolute error, power spectral similarity, and complexity measures. We propose three models, each representing a stage of increasing complexity from initial training to architectures with and without hidden layers. In these models, the oscillators initially lack spatial localization. However, we introduce spatial constraints in the final two models by superimposing spherical shells and rectangular geometries onto the oscillator network. Overall, the proposed model represents a step toward constructing a large-scale, biologically inspired model of brain dynamics.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1513374"},"PeriodicalIF":2.5,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Net2Brain: a toolbox to compare artificial vision models with human brain responses.","authors":"Domenic Bersch, Martina G Vilas, Sari Saba-Sadiya, Timothy Schaumlöffel, Kshitij Dwivedi, Christina Sartzetaki, Radoslaw M Cichy, Gemma Roig","doi":"10.3389/fninf.2025.1515873","DOIUrl":"10.3389/fninf.2025.1515873","url":null,"abstract":"<p><p>In cognitive neuroscience, the integration of deep neural networks (DNNs) with traditional neuroscientific analyses has significantly advanced our understanding of both biological neural processes and the functioning of DNNs. However, challenges remain in effectively comparing the representational spaces of artificial models and brain data, particularly due to the growing variety of models and the specific demands of neuroimaging research. To address these challenges, we present Net2Brain, a Python-based toolbox that provides an end-to-end pipeline for incorporating DNNs into neuroscience research, encompassing dataset download, a large selection of models, feature extraction, evaluation, and visualization. Net2Brain provides functionalities in four key areas. First, it offers access to over 600 DNNs trained on diverse tasks across multiple modalities, including vision, language, audio, and multimodal data, organized through a carefully structured taxonomy. Second, it provides a streamlined API for downloading and handling popular neuroscience datasets, such as the NSD and THINGS dataset, allowing researchers to easily access corresponding brain data. Third, Net2Brain facilitates a wide range of analysis options, including feature extraction, representational similarity analysis (RSA), and linear encoding, while also supporting advanced techniques like variance partitioning and searchlight analysis. Finally, the toolbox integrates seamlessly with other established open source libraries, enhancing interoperability and promoting collaborative research. By simplifying model selection, data processing, and evaluation, Net2Brain empowers researchers to conduct more robust, flexible, and reproducible investigations of the relationships between artificial and biological neural representations.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1515873"},"PeriodicalIF":2.5,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089098/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144110310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in deep learning for early diagnosis of Alzheimer's disease using multimodal neuroimaging: challenges and future directions.","authors":"Muhammad Liaquat Raza, Syed Tawassul Hassan, Subia Jamil, Noorulain Hyder, Kinza Batool, Sajidah Walji, Muhammad Khizar Abbas","doi":"10.3389/fninf.2025.1557177","DOIUrl":"10.3389/fninf.2025.1557177","url":null,"abstract":"<p><strong>Introduction: </strong>Alzheimer's disease is a progressive neurodegenerative disorder challenging early diagnosis and treatment. Recent advancements in deep learning algorithms applied to multimodal brain imaging offer promising solutions for improving diagnostic accuracy and predicting disease progression.</p><p><strong>Method: </strong>This narrative review synthesizes current literature on deep learning applications in Alzheimer's disease diagnosis using multimodal neuroimaging. The review process involved a comprehensive search of relevant databases (PubMed, Embase, Google Scholar and ClinicalTrials.gov), selection of pertinent studies, and critical analysis of findings. We employed a best-evidence approach, prioritizing high-quality studies and identifying consistent patterns across the literature.</p><p><strong>Results: </strong>Deep learning architectures, including convolutional neural networks, recurrent neural networks, and transformer-based models, have shown remarkable potential in analyzing multimodal neuroimaging data. These models can effectively process structural and functional imaging modalities, extracting relevant features and patterns associated with Alzheimer's pathology. Integration of multiple imaging modalities has demonstrated improved diagnostic accuracy compared to single-modality approaches. Deep learning models have also shown promise in predictive modeling, identifying potential biomarkers and forecasting disease progression.</p><p><strong>Discussion: </strong>While deep learning approaches show great potential, several challenges remain. Data heterogeneity, small sample sizes, and limited generalizability across diverse populations are significant hurdles. The clinical translation of these models requires careful consideration of interpretability, transparency, and ethical implications. The future of AI in neurodiagnostics for Alzheimer's disease looks promising, with potential applications in personalized treatment strategies.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1557177"},"PeriodicalIF":2.5,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12081360/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomics-driven neuro-fuzzy framework for rule generation to enhance explainability in MRI-based brain tumor segmentation.","authors":"Leondry Mayeta-Revilla, Eduardo P Cavieres, Matías Salinas, Diego Mellado, Sebastian Ponce, Francisco Torres Moyano, Steren Chabert, Marvin Querales, Julio Sotelo, Rodrigo Salas","doi":"10.3389/fninf.2025.1550432","DOIUrl":"https://doi.org/10.3389/fninf.2025.1550432","url":null,"abstract":"<p><strong>Introduction: </strong>Brain tumors are a leading cause of mortality worldwide, with early and accurate diagnosis being essential for effective treatment. Although Deep Learning (DL) models offer strong performance in tumor detection and segmentation using MRI, their black-box nature hinders clinical adoption due to a lack of interpretability.</p><p><strong>Methods: </strong>We present a hybrid AI framework that integrates a 3D U-Net Convolutional Neural Network for MRI-based tumor segmentation with radiomic feature extraction. Dimensionality reduction is performed using machine learning, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) is employed to produce interpretable decision rules. Each experiment is constrained to a small set of high-impact radiomic features to enhance clarity and reduce complexity.</p><p><strong>Results: </strong>The framework was validated on the BraTS2020 dataset, achieving an average DICE Score of 82.94% for tumor core segmentation and 76.06% for edema segmentation. Classification tasks yielded accuracies of 95.43% for binary (healthy vs. tumor) and 92.14% for multi-class (healthy vs. tumor core vs. edema) problems. A concise set of 18 fuzzy rules was generated to provide clinically interpretable outputs.</p><p><strong>Discussion: </strong>Our approach balances high diagnostic accuracy with enhanced interpretability, addressing a critical barrier in applying DL models in clinical settings. Integrating of ANFIS and radiomics supports transparent decision-making, facilitating greater trust and applicability in real-world medical diagnostics assistance.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1550432"},"PeriodicalIF":2.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12043696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143989629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large-scale EM data reveals myelinated axonal changes and altered connectivity in the corpus callosum of an autism mouse model.","authors":"Guoqiang Zhao, Ao Cheng, Jiahao Shi, Peiyao Shi, Jun Guo, Chunying Yin, Hafsh Khan, Jiachi Chen, Pengcheng Wang, Jiao Chen, Ruobing Zhang","doi":"10.3389/fninf.2025.1563799","DOIUrl":"https://doi.org/10.3389/fninf.2025.1563799","url":null,"abstract":"<p><strong>Introduction: </strong>Autism spectrum disorder (ASD) encompasses a diverse range of neurodevelopmental disorders with complex etiologies, including genetic, environmental, and neuroanatomical factors. While the exact mechanisms underlying ASD remain unclear, structural abnormalities in the brain offer valuable insights into its pathophysiology. The corpus callosum, the largest white matter tract in the brain, plays a crucial role in interhemispheric communication, and its structural abnormalities may contribute to ASD-related phenotypes.</p><p><strong>Methods: </strong>To investigate the ultrastructural alterations in the corpus callosum associated with ASD, we utilized serial scanning electron microscopy (sSEM) in mice. A dataset of the entire sagittal sections of the corpus callosum from wild-type and Shank3B mutant mice was acquired at 4 nm resolution, enabling precise comparisons of myelinated axon properties. Leveraging a fine-tuned EM-SAM model for automated segmentation, we quantitatively analyzed key metrics, including G-ratio, myelin thickness, and axonal density.</p><p><strong>Results: </strong>In the corpus callosum of Shank3B autism model mouse, we observed a significant increase in myelinated axon density, accompanied by thinner myelin sheaths compared to wild-type. Additionally, we identified abnormalities in the diameter distribution of myelinated axons and deviations in G-ratio. Notably, these ultrastructural alterations were widespread across the corpus callosum, suggesting a global disruption of myelinated axon integrity.</p><p><strong>Discussion: </strong>This study provides novel insights into the microstructural abnormalities of the corpus callosum in ASD mouse, supporting the hypothesis that myelination deficits contribute to ASD-related communication impairments between brain hemispheres. However, given the structural focus of this study, further research integrating functional assessments is necessary to establish a direct link between these morphological changes and ASD-related neural dysfunction.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1563799"},"PeriodicalIF":2.5,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12021825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143961848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning.","authors":"Zhibin Jiang, Keli Hu, Jia Qu, Zekang Bian, Donghua Yu, Jie Zhou","doi":"10.3389/fninf.2025.1559335","DOIUrl":"https://doi.org/10.3389/fninf.2025.1559335","url":null,"abstract":"<p><strong>Introduction: </strong>Motor imagery electroencephalographic (MI-EEG) signal recognition is used in various brain-computer interface (BCI) systems. In most existing BCI systems, this identification relies on classification algorithms. However, generally, a large amount of subject-specific labeled training data is required to reliably calibrate the classification algorithm for each new subject. To address this challenge, an effective strategy is to integrate transfer learning into the construction of intelligent models, allowing knowledge to be transferred from the source domain to enhance the performance of models trained in the target domain. Although transfer learning has been implemented in EEG signal recognition, many existing methods are designed specifically for certain intelligent models, limiting their application and generalization.</p><p><strong>Methods: </strong>To broaden application and generalization, an extended-LSR-based inductive transfer learning method is proposed to facilitate transfer learning across various classical intelligent models, including neural networks, Takagi-SugenoKang (TSK) fuzzy systems, and kernel methods.</p><p><strong>Results and discussion: </strong>The proposed method not only promotes the transfer of valuable knowledge from the source domain to improve learning performance in the target domain when target domain training data are insufficient but also enhances application and generalization by incorporating multiple classic base models. The experimental results demonstrate the effectiveness of the proposed method in MI-EEG signal recognition.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1559335"},"PeriodicalIF":2.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12014663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143977845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The quest to share data.","authors":"Arthur W Toga, Sidney Taiko Sheehan, Tyler Ard","doi":"10.3389/fninf.2025.1570568","DOIUrl":"10.3389/fninf.2025.1570568","url":null,"abstract":"<p><p>Data sharing in scientific research is widely acknowledged as crucial for accelerating progress and innovation. Mandates from funders, such as the NIH's updated Data Sharing Policy, have been beneficial in promoting data sharing. However, the effectiveness of such mandates relies heavily on the motivation of data providers. Despite policy-imposed requirements, many researchers may only comply minimally, resulting in data that is inadequately reusable. Here, we discuss the multifaceted challenges of incentivizing data sharing and the complex interplay of factors involved. Our paper delves into the motivations of various stakeholders, including funders, investigators, and data users, highlighting the differences in perspectives and concerns. We discuss the role of guidelines, such as the FAIR principles, in promoting good data management practices but acknowledge the practical and ethical challenges in implementation. We also examine the impact of infrastructure on data sharing effectiveness, emphasizing the need for systems that support efficient data discovery, access, and analysis. We address disparities in resources and expertise among researchers and concerns related to data misuse and misinterpretation. Here, we advocate for a holistic approach to incentivizing data sharing beyond mere compliance with mandates. It calls for the development of reward systems, financial incentives, and supportive infrastructure to encourage researchers to share data enthusiastically and effectively. By addressing these challenges collaboratively, the scientific community can realize the full potential of data sharing to advance knowledge and innovation.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1570568"},"PeriodicalIF":2.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11961638/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a multiscale neural connectivity knowledgebase of the autonomic nervous system.","authors":"Fahim T Imam, Thomas H Gillespie, Ilias Ziogas, Monique C Surles-Zeigler, Susan Tappan, Burak I Ozyurt, Jyl Boline, Bernard de Bono, Jeffrey S Grethe, Maryann E Martone","doi":"10.3389/fninf.2025.1541184","DOIUrl":"10.3389/fninf.2025.1541184","url":null,"abstract":"<p><p>The Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is a U.S. National Institutes of Health (NIH) funded effort to enhance our understanding of the neural circuitry responsible for visceral control. SPARC's mission is to identify, extract, and compile our overall existing knowledge and understanding of the autonomic nervous system (ANS) connectivity between the central nervous system and end organs. A major goal of SPARC is to use this knowledge to promote the development of the next generation of neuromodulation devices and bioelectronic medicine for nervous system diseases. As part of the SPARC program, we have been developing the SPARC Connectivity Knowledge Base of the Autonomic Nervous System (SCKAN), a dynamic resource containing information about the origins, terminations, and routing of ANS projections. The distillation of SPARC's connectivity knowledge into this knowledge base involves a rigorous curation process to capture connectivity information provided by experts, published literature, textbooks, and SPARC scientific data. SCKAN is used to automatically generate anatomical and functional connectivity maps on the SPARC portal. In this article, we present the design and functionality of SCKAN, including the detailed knowledge engineering process developed to populate the resource with high quality and accurate data. We discuss the process from both the perspective of SCKAN's ontological representation as well as its practical applications in developing information systems. We share our techniques, strategies, tools and insights for developing a practical knowledgebase of ANS connectivity that supports continual enhancement.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1541184"},"PeriodicalIF":2.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FAIR African brain data: challenges and opportunities.","authors":"Eberechi Wogu, George Ogoh, Patrick Filima, Barisua Nsaanee, Bradley Caron, Franco Pestilli, Damian Eke","doi":"10.3389/fninf.2025.1530445","DOIUrl":"10.3389/fninf.2025.1530445","url":null,"abstract":"<p><strong>Introduction: </strong>The effectiveness of research and innovation often relies on the diversity or heterogeneity of datasets that are Findable, Accessible, Interoperable and Reusable (FAIR). However, the global landscape of brain data is yet to achieve desired levels of diversity that can facilitate generalisable outputs. Brain datasets from low-and middle-income countries of Africa are still missing in the global open science ecosystem. This can mean that decades of brain research and innovation may not be generalisable to populations in Africa.</p><p><strong>Methods: </strong>This research combined experiential learning or experiential research with a survey questionnaire. The experiential research involved deriving insights from direct, hands-on experiences of collecting African Brain data in view of making it FAIR. This was a critical process of action, reflection, and learning from doing data collection. A questionnaire was then used to validate the findings from the experiential research and provide wider contexts for these findings.</p><p><strong>Results: </strong>The experiential research revealed major challenges to FAIR African brain data that can be categorised as socio-cultural, economic, technical, ethical and legal challenges. It also highlighted opportunities for growth that include capacity development, development of technical infrastructure, funding as well as policy and regulatory changes. The questionnaire then showed that the wider African neuroscience community believes that these challenges can be ranked in order of priority as follows: Technical, economic, socio-cultural and ethical and legal challenges.</p><p><strong>Conclusion: </strong>We conclude that African researchers need to work together as a community to address these challenges in a way to maximise efforts and to build a thriving FAIR brain data ecosystem that is socially acceptable, ethically responsible, technically robust and legally compliant.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1530445"},"PeriodicalIF":2.5,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11911527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of interferon-β and dimethyl fumarate on nonlinear dynamical characteristics of electroencephalogram signatures in patients with multiple sclerosis.","authors":"Christopher Ivan Hernandez, Natalia Afek, Magda Gawłowska, Paweł Oświęcimka, Magdalena Fafrowicz, Agnieszka Slowik, Marcin Wnuk, Monika Marona, Klaudia Nowak, Kamila Zur-Wyrozumska, Mary Jean Amon, P A Hancock, Tadeusz Marek, Waldemar Karwowski","doi":"10.3389/fninf.2025.1519391","DOIUrl":"https://doi.org/10.3389/fninf.2025.1519391","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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 (<i>n</i> = 39), DMF (<i>n</i> = 53), and healthy controls (<i>n</i> = 83).</p><p><strong>Results: </strong>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.</p><p><strong>Discussion: </strong>These findings enhance our understanding of the complex nature of MS, support treatment development, and demonstrate the effectiveness of nonlinear analysis methods.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1519391"},"PeriodicalIF":2.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11906706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}