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Recent Progress in Brain Network Models for Medical Applications: A Review
Chenfei Ye1, Yixuan Zhang2, Chen Ran2
1International Research Institute for Artificial Intelligence, Harbin Institute of Technology at Shenzhen, Shenzhen, China.
Health Data Science
|July 9, 2024
Summary
Brain network models (BNMs) integrate neuroimaging and mathematical modeling to analyze brain dynamics in neurological disorders. This review explores BNM applications, challenges, and future directions for clinical use.
Area of Science:
- Computational neuroscience
- Neuroimaging analysis
- Mathematical modeling of brain networks
Background:
- Pathological brain changes impact function through the connectome.
- Brain network models (BNMs) integrate multimodal neuroimaging with neural mass modeling.
- BNMs quantify aberrant network dynamics in neurological and psychiatric disorders.
Purpose of the Study:
- Review advancements in BNM-based medical applications.
- Discuss current challenges and propose solutions in the field.
- Explore future directions for BNM research and clinical integration.
Main Methods:
- Utilizing neural mass models within the BNM framework.
- Linking simulated functional signals to empirical neurophysiological data.
- Integrating multimodal neuroimaging data for model construction.
Main Results:
- BNMs show promise in exploring neuropathological mechanisms.
- BNMs can elucidate therapeutic effects and predict disease outcomes.
- Individualized BNM simulations may guide clinical neuromodulation treatments.
Conclusions:
- BNMs offer potential for understanding neuropathology's effect on brain dynamics.
- BNMs can aid clinical diagnosis and treatment decision-making.
- Addressing current constraints is crucial for clinical BNM implementation.
