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: 30 December 2024 | Viewed by 209
Special Issue Editor
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
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Keywords
- neurological disorders
- neuroimaging analysis
- biomarker identification
- computational neuroscience
- machine learning
- brain aging
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