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Advancing Neurological Disorders via Computational Neuroscience and Neural Networks
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Advancing Neurological Disorders via Computational Neuroscience and Neural Networks

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 2648

Special Issue Editor

Dr. Elnaz Pashaei

E-Mail Website
Guest Editor
Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
Interests: artificial intelligence; bioinformatics; computational biology; machine learning; neural networks; marker gene identification

Special Issue Information

Dear Colleagues,

Recent advances in computational techniques have transformed the field of neurological disorders, representing a breakthrough in computational neuroscience. Machine learning and neural network simulations offer powerful tools for deciphering complex neurological processes. These technologies facilitate the modeling of disease progression, the detection of biomarkers, and the analysis of neuroimaging. By integrating genetic, imaging, and clinical data, computational models are able to enhance the diagnostic accuracy and outcome of treatments for various neurological disorders, including tumors, seizures, degenerative diseases, and brain aging.

This Special Issue welcomes the submission of original research articles, reviews, and methodological studies that advance our understanding and application of computational approaches in neurological research. Researchers and practitioners are invited to contribute innovative approaches, novel findings, and technological advancements in these critical areas. The aim of this Special Issue is to foster interdisciplinary dialogue and galvanize advances in the diagnosis, treatment, and management of neurological disorders via computational insights.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Neurological diagnosis and prediction.
  • Neural network simulations in neurology.
  • Treatment optimization for neurological disorders.
  • Integrative data analysis in neurology.
  • Neurological drug discovery.
  • Neuroimaging analysis.
  • Computational models of neurological disorders.

Dr. Elnaz Pashaei
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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • neurological disorders
  • neuroimaging analysis
  • biomarker identification
  • computational neuroscience
  • machine learning
  • brain aging

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

Published Papers (2 papers)

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Research

19 pages, 4489 KiB  
Article
Biomarker Identification for Alzheimer’s Disease Using a Multi-Filter Gene Selection Approach
by Elnaz Pashaei, Elham Pashaei and Nizamettin Aydin
Int. J. Mol. Sci. 2025, 26(5), 1816; https://doi.org/10.3390/ijms26051816 - 20 Feb 2025
Viewed by 665
Abstract
There is still a lack of effective therapies for Alzheimer’s disease (AD), the leading cause of dementia and cognitive decline. Identifying reliable biomarkers and therapeutic targets is crucial for advancing AD research. In this study, we developed an aggregative multi-filter gene selection approach [...] Read more.
There is still a lack of effective therapies for Alzheimer’s disease (AD), the leading cause of dementia and cognitive decline. Identifying reliable biomarkers and therapeutic targets is crucial for advancing AD research. In this study, we developed an aggregative multi-filter gene selection approach to identify AD biomarkers. This method integrates hub gene ranking techniques, such as degree and bottleneck, with feature selection algorithms, including Random Forest and Double Input Symmetrical Relevance, and applies ranking aggregation to improve accuracy and robustness. Five publicly available AD-related microarray datasets (GSE48350, GSE36980, GSE132903, GSE118553, and GSE5281), covering diverse brain regions like the hippocampus and frontal cortex, were analyzed, yielding 803 overlapping differentially expressed genes from 464 AD and 492 normal cases. An independent dataset (GSE109887) was used for external validation. The approach identified 50 prioritized genes, achieving an AUC of 86.8 in logistic regression on the validation dataset, highlighting their predictive value. Pathway analysis revealed involvement in critical biological processes such as synaptic vesicle cycles, neurodegeneration, and cognitive function. These findings provide insights into potential therapeutic targets for AD. Full article
(This article belongs to the Special Issue Advancing Neurological Disorders via Computational Neuroscience and Neural Networks)
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24 pages, 1946 KiB  
Article
Network Diffusion-Constrained Variational Generative Models for Investigating the Molecular Dynamics of Brain Connectomes Under Neurodegeneration
by Jiajia Xie, Raghav Tandon and Cassie S. Mitchell
Int. J. Mol. Sci. 2025, 26(3), 1062; https://doi.org/10.3390/ijms26031062 - 26 Jan 2025
Viewed by 1108
Abstract
Alzheimer’s disease (AD) is a complex and progressive neurodegenerative condition with significant societal impact. Understanding the temporal dynamics of its pathology is essential for advancing therapeutic interventions. Empirical and anatomical evidence indicates that network decoupling occurs as a result of gray matter atrophy. [...] Read more.
Alzheimer’s disease (AD) is a complex and progressive neurodegenerative condition with significant societal impact. Understanding the temporal dynamics of its pathology is essential for advancing therapeutic interventions. Empirical and anatomical evidence indicates that network decoupling occurs as a result of gray matter atrophy. However, the scarcity of longitudinal clinical data presents challenges for computer-based simulations. To address this, a first-principles-based, physics-constrained Bayesian framework is proposed to model time-dependent connectome dynamics during neurodegeneration. This temporal diffusion network framework segments pathological progression into discrete time windows and optimizes connectome distributions for biomarker Bayesian regression, conceptualized as a learning problem. The framework employs a variational autoencoder-like architecture with computational enhancements to stabilize and improve training efficiency. Experimental evaluations demonstrate that the proposed temporal meta-models outperform traditional static diffusion models. The models were evaluated using both synthetic and real-world MRI and PET clinical datasets that measure amyloid beta, tau, and glucose metabolism. The framework successfully distinguishes normative aging from AD pathology. Findings provide novel support for the “decoupling” hypothesis and reveal eigenvalue-based evidence of pathological destabilization in AD. Future optimization of the model, integrated with real-world clinical data, is expected to improve applications in personalized medicine for AD and other neurodegenerative diseases. Full article
(This article belongs to the Special Issue Advancing Neurological Disorders via Computational Neuroscience and Neural Networks)
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