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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: 30 July 2026 | Viewed by 3330

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 250 words) can be sent to the Editorial Office for assessment.

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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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
Cited by 1 | Viewed by 896
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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Review

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15 pages, 416 KB  
Review
Current State of the Clinical Applications of Artificial Intelligence in Stroke: A Literature Review
by Grant C. Sorkin, Nicholas M. Caffes, John P. Shank, James L. Hershey, Dana E. Knaub, Jillian C. Krebs and Muhammad H. Niazi
Brain Sci. 2026, 16(2), 173; https://doi.org/10.3390/brainsci16020173 - 31 Jan 2026
Viewed by 1242
Abstract
Background: Artificial intelligence (AI) has emerged as a transformative tool in medicine, leveraging rapid analysis of large datasets to accelerate diagnosis, enhance clinical decision-making, and improve clinical workflows. This is highly relevant in stroke care given the time-sensitive nature of the disease process. [...] Read more.
Background: Artificial intelligence (AI) has emerged as a transformative tool in medicine, leveraging rapid analysis of large datasets to accelerate diagnosis, enhance clinical decision-making, and improve clinical workflows. This is highly relevant in stroke care given the time-sensitive nature of the disease process. This review evaluates the current landscape of evidence-based medicine utilizing AI in stroke, with emphasis on its use in phases of clinical care across the stroke continuum, including pre-hospital, acute, and recovery phases. This offers a comprehensive understanding of the current state of AI in both practice and literature. Methods: A review of major databases was conducted, identifying peer-reviewed literature evaluating the use of AI and its level of evidence across the stroke continuum. Given the heterogeneity of study designs, interventions, and outcome metrics spanning multiple disciplines, findings were synthesized narratively. Results: Across all phases of care, there remain no randomized controlled trials (RCTs) evaluating patient-level outcome data using AI (Level A). In the pre-hospital phase of care, AI has been used to identify stroke symptoms and assist EMS routing/training but presently remains limited to research. AI is most studied in the acute phase of care, representing the only phase to achieve commercial application in imaging detection and telestroke assistance, supported by non-randomized evidence (Level B-NR). In the recovery phase, AI may enhance wearable technologies, tele-rehabilitation, and robotics/brain–computer interfaces, with early RCTs (Level B-R) supporting the latter two, representing the strongest evidence for AI in stroke care to date. Conclusions: Despite the potential for AI to transform all phases of care across the stroke continuum, major challenges remain, including transparency, generalizability, equity, and the need for externally validated clinical studies. Full article
(This article belongs to the Special Issue Artificial Intelligence in Neurological Disorders)
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Other

Jump to: Research, Review

40 pages, 3300 KB  
Systematic Review
Advancing Brain Tumor Diagnosis Using Deep Learning: A Systematic and Critical Review on Methodological Approaches to Glioma Segmentation and Classification Through Multiparametric MRI
by Simona Aresta, Cinzia Palmirotta, Muhammad Asim, Petronilla Battista, Gaia C. Santi, Gianvito Lagravinese, Claudia Cava, Pietro Fiore, Andrea Santamato, Paolo Vitali, Isabella Castiglioni, Gennaro D’Anna, Leonardo Rundo and Christian Salvatore
Brain Sci. 2026, 16(5), 468; https://doi.org/10.3390/brainsci16050468 - 27 Apr 2026
Viewed by 242
Abstract
Background/Objectives: Brain tumors are highly lethal cancers, with gliomas representing the most complex subtype. Magnetic resonance imaging (MRI) is the main non-invasive imaging modality. This review evaluates deep learning (DL) and artificial intelligence methods for brain tumor segmentation and classification. Methods: In this [...] Read more.
Background/Objectives: Brain tumors are highly lethal cancers, with gliomas representing the most complex subtype. Magnetic resonance imaging (MRI) is the main non-invasive imaging modality. This review evaluates deep learning (DL) and artificial intelligence methods for brain tumor segmentation and classification. Methods: In this systematic review, PubMed and Scopus were searched for articles published from 2022 to March 2025. Authors independently identified eligible studies based on predefined inclusion criteria and extracted data. The study quality and risk of bias were assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) checklist. Results: Thirty-one studies met the inclusion criteria from 310 records, with eight addressing both segmentation and classification. Most segmentation studies used publicly available multiparametric MRI datasets. Performance varied by architecture and tumor region, with whole-tumor segmentation achieving the highest Dice Similarity Coefficient (DSC). Classical U-Nets reported DSC values ranging 80–87%, while models with residual or attention mechanisms exceeded 90%. Classification focused on tumor type and glioma grading, using features learned from multiparametric MRI. Reported accuracy ranged from 91.3% to 99.4%, with sensitivity and specificity often above 95%. However, variability across tumor subregions, limited external validation, reliance on public datasets, and heterogeneous preprocessing raise concerns about robustness and real-world generalizability. Evidence on the use of explainability methods for both tasks remains limited. Conclusions: DL models for glioma segmentation and classification demonstrate promising performance. However, standardized validation protocols, multi-center datasets, and the integration of explainable artificial intelligence techniques are needed to improve transparency, robustness, and clinical applicability. Full article
(This article belongs to the Special Issue Artificial Intelligence in Neurological Disorders)
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