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Brain Sciences
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Artificial Intelligence in Neurological Disorders
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Artificial Intelligence in Neurological Disorders

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Computational Neuroscience, Neuroinformatics, and Neurocomputing".

Deadline for manuscript submissions: 24 December 2025 | Viewed by 824

Special Issue Editors

Dr. Rahul Kashyap

E-Mail Website
Guest Editor
1. Department of Research, WellSpan Health, York, PA 17403, USA
2. Drexel University College of Medicine, Philadelphia, PA 19104, USA
3. Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, USA
Interests: artificial intelligence; neurological disorders; machine learning; deep learning neuroimaging; Alzheimer's disease; Parkinson's disease; predictive analytics; personalized medicine; ethical AI
Dr. Pallavi Dinesh Shirsat

E-Mail Website
Guest Editor Assistant
Minden Nephrology and Hypertension, Minden, LA 71055, USA
Interests: artificial intelligence; machine learning; acute kidney injury; diabetes; diabetic neuropathy; dialysis; hypertension

Special Issue Information

Dear Colleagues,

This Special Issue, titled “Artificial Intelligence in Neurological Disorders”, explores the transformative role of AI in diagnosing, managing, and treating neurological conditions. It focuses on the integration of machine learning (ML), deep learning (DL), and other AI methodologies into clinical neurology. Key topics include AI-driven diagnostic tools for the early detection of disorders such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis, along with predictive models of disease progression. Advances in neuroimaging analysis, personalized treatment strategies, and rehabilitation technologies are also highlighted.

This Special Issue emphasizes the potential of AI to enhance diagnostic accuracy and reduce clinical workload by interpreting complex neurological data. Ethical considerations, including bias, data privacy, and the need for explainable AI, are also discussed. Case studies demonstrate real-world applications of AI for improving patient outcomes, while challenges such as data standardization and regulatory hurdles are addressed. Overall, this Special Issue underscores AI's promise in revolutionizing neurological healthcare, urging multidisciplinary collaboration for its successful implementation.

Dr. Rahul Kashyap
Guest Editor

Dr. Pallavi Dinesh Shirsat
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • neurological disorders
  • machine learning
  • deep learning
  • neuroimaging
  • Alzheimer’s disease
  • Parkinson’s disease
  • predictive analytics
  • personalized medicine
  • ethical AI

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Published Papers (1 paper)

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Research

16 pages, 1643 KB  
Article
P300 Spatiotemporal Prior-Based Transformer-CNN for Auxiliary Diagnosis of PTSD
by Lize Tan, Hao Fang, Peng Ding, Fan Wang, Yuanyuan Wei and Yunfa Fu
Brain Sci. 2025, 15(10), 1124; https://doi.org/10.3390/brainsci15101124 - 19 Oct 2025
Viewed by 300
Abstract
Objectives: To address the challenges of subjectivity, misdiagnosis and underdiagnosis in post-traumatic stress disorder (PTSD), this study proposes an objective auxiliary diagnostic method based on P300 signals. Existing studies largely rely on conventional P300 features, lacking the systematic integration of event-related potential (ERP) [...] Read more.
Objectives: To address the challenges of subjectivity, misdiagnosis and underdiagnosis in post-traumatic stress disorder (PTSD), this study proposes an objective auxiliary diagnostic method based on P300 signals. Existing studies largely rely on conventional P300 features, lacking the systematic integration of event-related potential (ERP) priors and facing limitations in spatiotemporal feature modeling. Methods: Using common spatiotemporal pattern (CSTP) analysis and quantitative evaluation, we revealed significant spatiotemporal differences in P300 signals between PTSD patients and healthy controls. ERP prior information was then extracted and integrated into a hybrid architecture combining transformer encoders and a convolutional neural network (CNN), enabling joint modeling of long-range temporal dependencies and local spatial patterns. Results: The proposed P300 spatiotemporal transformer-CNN (P300-STTCNet) achieved a classification accuracy of 93.37% in distinguishing PTSD from healthy controls, markedly outperforming traditional approaches. Conclusions: Significant spatiotemporal differences in P300 signals exist between PTSD and healthy control groups. The P300-STTCNet model effectively captures PTSD-related spatiotemporal features, demonstrating strong potential for electroencephalogram-based objective auxiliary diagnosis. Full article
(This article belongs to the Special Issue Artificial Intelligence in Neurological Disorders)
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