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Brain Sciences
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Perspectives of Artificial Intelligence (AI) in Aging Neuroscience
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Perspectives of Artificial Intelligence (AI) in Aging 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: 31 August 2025 | Viewed by 1408

Special Issue Editors

Dr. Christos Frantzidis

E-Mail Website
Guest Editor
School of Computer Science, University of Lincoln, Lincoln PC LN6 7TS, UK
Interests: aging neuroscience; artificial gravity training; brain aging markers; brain language mapping; cognition; computerized cognitive training; functional connectivity; network neuroscience; neuroplasticity; sleep disorders
Dr. Aristea Ladas

E-Mail Website
Guest Editor
CITY College, University of York Europe Campus, Thessaloniki, Greece
Interests: brain networks; consciousness; creativity; cognitive functions; neuroplasticity; aging neuroscience

Special Issue Information

Dear Colleagues,

Over the past decade, the application of artificial intelligence in medicine and neuroscience has undergone exponential growth. Advances in big data acquisition and deep learning models have revolutionized aging neuroscience methodologies, particularly in the following areas: (1) the early detection and diagnosis of neurodegenerative conditions, (2) data fusion and processing in multi-modal neuroscientific data, (3) AI-powered cognitive and behavioral modeling, (4) drug discovery and development, and (5) chatbot applications and robotic assistance for independent living.

The aim of this Special Issue is to provide a comprehensive roadmap highlighting perspectives and challenges regarding AI methodologies in aging neuroscience. We welcome articles of all types that offer valuable insights into the following domains:

  • Identification of subtle patterns in neuroscientific data indicative of the onset of neurodegeneration;
  • AI tools for more accurate functional and structural mapping of age-related physiological and pathological changes;
  • Machine learning techniques for improving predictions of chronological aging;
  • AI-powered outcome measures for evaluating interventions and drugs aimed at preventing or delaying the onset of neurodegeneration;
  • Chatbots, robotic systems, and generative AI applications that promote active and healthy aging;
  • Natural Language Processing models serving as coaching or recommendation systems in both physiological and pathological aging.

Dr. Christos Frantzidis
Dr. Aristea Ladas
Guest Editors

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

  • aging neuroscience
  • artificial intelligence
  • brain aging
  • big data acquisition
  • chatbots
  • deep learning
  • generative AI
  • robotic assistance
  • virtual coaches for active aging

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Published Papers (1 paper)

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Research

18 pages, 839 KiB  
Article
From Narratives to Diagnosis: A Machine Learning Framework for Classifying Sleep Disorders in Aging Populations: The sleepCare Platform
by Christos A. Frantzidis
Brain Sci. 2025, 15(7), 667; https://doi.org/10.3390/brainsci15070667 - 20 Jun 2025
Viewed by 654
Abstract
Background/Objectives: Sleep disorders are prevalent among aging populations and are often linked to cognitive decline, chronic conditions, and reduced quality of life. Traditional diagnostic methods, such as polysomnography, are resource-intensive and limited in accessibility. Meanwhile, individuals frequently describe their sleep experiences through [...] Read more.
Background/Objectives: Sleep disorders are prevalent among aging populations and are often linked to cognitive decline, chronic conditions, and reduced quality of life. Traditional diagnostic methods, such as polysomnography, are resource-intensive and limited in accessibility. Meanwhile, individuals frequently describe their sleep experiences through unstructured narratives in clinical notes, online forums, and telehealth platforms. This study proposes a machine learning pipeline (sleepCare) that classifies sleep-related narratives into clinically meaningful categories, including stress-related, neurodegenerative, and breathing-related disorders. The proposed framework employs natural language processing (NLP) and machine learning techniques to support remote applications and real-time patient monitoring, offering a scalable solution for the early identification of sleep disturbances. Methods: The sleepCare consists of a three-tiered classification pipeline to analyze narrative sleep reports. First, a baseline model used a Multinomial Naïve Bayes classifier with n-gram features from a Bag-of-Words representation. Next, a Support Vector Machine (SVM) was trained on GloVe-based word embeddings to capture semantic context. Finally, a transformer-based model (BERT) was fine-tuned to extract contextual embeddings, using the [CLS] token as input for SVM classification. Each model was evaluated using stratified train-test splits and 10-fold cross-validation. Hyperparameter tuning via GridSearchCV optimized performance. The dataset contained 475 labeled sleep narratives, classified into five etiological categories relevant for clinical interpretation. Results: The transformer-based model utilizing BERT embeddings and an optimized Support Vector Machine classifier achieved an overall accuracy of 81% on the test set. Class-wise F1-scores ranged from 0.72 to 0.91, with the highest performance observed in classifying normal or improved sleep (F1 = 0.91). The macro average F1-score was 0.78, indicating balanced performance across all categories. GridSearchCV identified the optimal SVM parameters (C = 4, kernel = ‘rbf’, gamma = 0.01, degree = 2, class_weight = ‘balanced’). The confusion matrix revealed robust classification with limited misclassifications, particularly between overlapping symptom categories such as stress-related and neurodegenerative sleep disturbances. Conclusions: Unlike generic large language model applications, our approach emphasizes the personalized identification of sleep symptomatology through targeted classification of the narrative input. By integrating structured learning with contextual embeddings, the framework offers a clinically meaningful, scalable solution for early detection and differentiation of sleep disorders in diverse, real-world, and remote settings. Full article
(This article belongs to the Special Issue Perspectives of Artificial Intelligence (AI) in Aging Neuroscience)
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