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Brain Sciences
Special Issues
Machine Learning and Artificial Intelligence Algorithms in Neuroscience
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Machine Learning and Artificial Intelligence Algorithms in Neuroscience

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 930

Special Issue Editor

Dr. Mohsen Ahmadi

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
Interests: artificial intelligence; machine learning; medical image processing; healthcare; cancer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in Machine Learning (ML) and Artificial Intelligence (AI) are transforming neuroscience, offering powerful tools to analyze brain function, diagnose neurological disorders, and develop innovative treatments. AI-driven approaches significantly enhance neuroimaging analysis, brain–computer interfaces (BCIs), and cognitive neuroscience research, enabling personalized medicine and real-time decision-making in clinical applications. The ability of AI to process large-scale neuroimaging data, decode complex neural signals, and model cognitive functions has opened new possibilities in early disease detection, neurorehabilitation, and neuroproteins.

This Special Issue seeks to explore state-of-the-art methodologies that integrate AI/ML with neuroscience, addressing challenges in neuroimaging processing, neural signal decoding, cognitive modeling, neurodegenerative disease prediction, and neuro-inspired AI architectures. The application of deep learning, reinforcement learning, and explainable AI in neuroscience research continues to refine diagnostic accuracy and treatment strategies, making AI a vital tool in understanding and mimicking brain functions.

Dr. Mohsen Ahmadi
Guest Editor

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

  • machine learning
  • artificial intelligence
  • computational neuroscience
  • deep learning
  • neuroimaging
  • brain–computer interfaces
  • neural signal processing
  • cognitive modeling
  • neurodegenerative diseases
  • explainable AI

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Published Papers (1 paper)

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Research

23 pages, 5700 KiB  
Article
Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification
by Ahmad Muhammad, Qi Jin, Osman Elwasila and Yonis Gulzar
Brain Sci. 2025, 15(6), 612; https://doi.org/10.3390/brainsci15060612 - 6 Jun 2025
Viewed by 657
Abstract
Background/Objectives: Alzheimer’s disease (AD), a progressive neurodegenerative disorder, demands precise early diagnosis to enable timely interventions. Traditional convolutional neural networks (CNNs) and deep learning models often fail to effectively integrate localized brain changes with global connectivity patterns, limiting their efficacy in Alzheimer’s disease [...] Read more.
Background/Objectives: Alzheimer’s disease (AD), a progressive neurodegenerative disorder, demands precise early diagnosis to enable timely interventions. Traditional convolutional neural networks (CNNs) and deep learning models often fail to effectively integrate localized brain changes with global connectivity patterns, limiting their efficacy in Alzheimer’s disease (AD) classification. Methods: This research proposes a novel deep learning framework for multi-stage Alzheimer’s disease (AD) classification using T1-weighted MRI scans. The adaptive feature fusion layer, a pivotal advancement, facilitates the dynamic integration of features extracted from a ResNet50-based CNN and a vision transformer (ViT). Unlike static fusion methods, our adaptive feature fusion layer employs an attention mechanism to dynamically integrate ResNet50’s localized structural features and vision transformer (ViT) global connectivity patterns, significantly enhancing stage-specific Alzheimer’s disease classification accuracy. Results: Evaluated on the Alzheimer’s 5-Class (AD5C) dataset comprising 2380 MRI scans, the framework achieves an accuracy of 99.42% (precision: 99.55%; recall: 99.46%; F1-score: 99.50%), surpassing the prior benchmark of 98.24% by 1.18%. Ablation studies underscore the essential role of adaptive feature fusion in minimizing misclassifications, while external validation on a four-class dataset confirms robust generalizability. Conclusions: This framework enables precise early Alzheimer’s disease (AD) diagnosis by integrating multi-scale neuroimaging features, empowering clinicians to optimize patient care through timely and targeted interventions. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence Algorithms in Neuroscience)
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