Methodological Development and Applications of Nonlinear Dynamic Analysis for Neuroimaging

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Background

Linear models and stationary signal processing methodologies have substantially advanced our knowledge about the human brain, which, however, are insufficient and incomplete given that the human brain is one of the most complex systems in the world. To meet this research gap, a recent focus in neuroimaging-based functional brain research is the complexity and temporal dynamics of brain activities. Many nonlinear dynamic analysis methods have been applied to the analysis of neuroimaging data, including but not limited to brain entropy and complexity analysis, dynamic causal models (DCM), and dynamic brain networks. 1. Brain Entropy. Brain entropy quantifies the irregularity of neural activity and its complexity. Brain entropy analysis has produced meaningful results, such as verifying the relationship between brain entropy and brain function and diseases using resting-state fMRI and providing additional information compared to other indicators. However, clear brain entropy map patterns for brain function and diseases are still lacking, and more research is needed to fill this gap and conduct integrated research. 2. Dynamic Causal Model. The dynamic causal model considers the nervous system as a dynamic system and specifies assumptions or models that are more consistent with a distributed response that aligns with the physiological foundation, reflecting the information transmission effect between neurons. However, due to differences in model construction and choice, comparison of results can be challenging. Further exploration of clinical applications, such as understanding how causal relationships of brain activities and regulatory mechanisms under disease states and external stimulation, are also necessary. 3. Dynamic Brain Networks. Brain network analysis has been widely used in the neuroimaging field. It has revealed several large-scale brain networks in humans, such as the default network and the executive control network, and the small-word network property of the brain. However, the dynamic analysis of brain networks for adapting to changes in the external environment needs to be improved. Understanding how brain network modularization dynamically converts to adapt to environmental changes and determining the rules governing these modular adjustment models to explain human behavior relationships are among the most significant challenges in cognitive neuroscience.

Given the wide range of theories related to nonlinear dynamics, this Research Topic focuses primarily on brain entropy and complexity analysis, dynamic causal models, and dynamic brain networks. Furthermore, we welcome developing and applying other approaches to nonlinear dynamics analysis for neuroimaging.

We welcome the submission of manuscripts (Research Article, Brief Research Article, Mini-review, Review, etc.) focusing on, but not limited to, the following themes:
- Methodological development and applications of brain entropy and complexity analysis for neuroimaging
- Methodological development and applications of dynamic causal modeling for neuroimaging
- Methodological development and applications of dynamic network analysis for neuroimaging
- Methodological development and applications of others about nonlinear dynamic analysis for neuroimaging

Keywords: Neuroimaging, Nonlinear Dynamic Analysis, Brain Entropy, Complexity, Dynamic Causal Modeling (DCM), Dynamic Brain Networks, fMRI

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