Understanding Mental Disorders via Computational Brain Network Modeling

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

Deadline for manuscript submissions: closed (26 December 2023) | Viewed by 1549

Special Issue Editors


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Guest Editor
Center for Mathematics, Computing and Cognition, Universidade Federal do ABC, Nucleo Interdisciplinar Neurociencia Aplicada, Sao Bernardo Do Campo, Brazil
Interests: biological sciences; pharmacology

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Guest Editor
Faculty of Philosophy, Sciences and Letters at Ribeirão Preto, Institute of Physics, University Sao Paulo, Ribeirao Preto, SP, Brazil
Interests: mathematical modelling; neuron; computational intelligence models; bioinformatics and computational biology; artificial neural networks; artificial intelligence; biophysics neuroscience; neural networks

Special Issue Information

Dear Colleagues,

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
Guest Editors

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Keywords

  • mental disorders
  • computational brain network modeling
  • functional connectivity
  • potential biomarkers
  • modeling techniques

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Published Papers (1 paper)

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Research

33 pages, 2596 KiB  
Article
A Computational Model for the Simulation of Prepulse Inhibition and Its Modulation by Cortical and Subcortical Units
by Thiago Ohno Bezerra, Antonio C. Roque and Cristiane Salum
Brain Sci. 2024, 14(5), 502; https://doi.org/10.3390/brainsci14050502 - 15 May 2024
Viewed by 1129
Abstract
The sensorimotor gating is a nervous system function that modulates the acoustic startle response (ASR). Prepulse inhibition (PPI) phenomenon is an operational measure of sensorimotor gating, defined as the reduction of ASR when a high intensity sound (pulse) is preceded in milliseconds by [...] Read more.
The sensorimotor gating is a nervous system function that modulates the acoustic startle response (ASR). Prepulse inhibition (PPI) phenomenon is an operational measure of sensorimotor gating, defined as the reduction of ASR when a high intensity sound (pulse) is preceded in milliseconds by a weaker stimulus (prepulse). Brainstem nuclei are associated with the mediation of ASR and PPI, whereas cortical and subcortical regions are associated with their modulation. However, it is still unclear how the modulatory units can influence PPI. In the present work, we developed a computational model of a neural circuit involved in the mediation (brainstem units) and modulation (cortical and subcortical units) of ASR and PPI. The activities of all units were modeled by the leaky-integrator formalism for neural population. The model reproduces basic features of PPI observed in experiments, such as the effects of changes in interstimulus interval, prepulse intensity, and habituation of ASR. The simulation of GABAergic and dopaminergic drugs impaired PPI by their effects over subcortical units activity. The results show that subcortical units constitute a central hub for PPI modulation. The presented computational model offers a valuable tool to investigate the neurobiology associated with disorder-related impairments in PPI. Full article
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