Understanding Mental Disorders via Computational Brain Network Modeling
Deadline for manuscript submissions: closed (26 December 2023) | Viewed by 199
Interests: biological sciences; pharmacology
Interests: mathematical modelling; neuron; computational intelligence models; bioinformatics and computational biology; artificial neural networks; artificial intelligence; biophysics neuroscience; neural networks
Mental disorders are complex conditions that affect millions of people worldwide and their understanding and treatment remain major challenges for the scientific community. The traditional approach to studying mental disorders has been largely based on investigating the effects of individual brain regions or genes, neglecting the fact that these disorders arise from the intricate interactions among different brain regions and neural circuits. Computational brain network modeling offers a powerful tool to understand the complex dynamics of the brain and its functional connectivity in mental disorders. By simulating brain activity at the network level, computational models can help to elucidate the underlying mechanisms of mental disorders, identify potential biomarkers, and develop new treatments. This special issue aims to provide a comprehensive overview of the latest advances in computational brain network modeling of mental disorders, bringing together experts in the field to present cutting-edge research on a range of topics. The issue will cover a broad range of disorders and will explore the application of various modeling techniques to advance our understanding of these conditions. Overall, this special issue aims to promote a deeper understanding of the complex interactions between brain networks and mental disorders, and to highlight the potential of computational modeling as a tool for advancing our understanding of these conditions.
Dr. Cristiane Salum
Dr. Antonio Roque
Manuscript Submission Information
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- mental disorders
- computational brain network modeling
- functional connectivity
- potential biomarkers
- modeling techniques