Ordinary Differential Equation (ODE)-based Brain Connectomics

Description

Ordinary Differential Equations (ODEs) provide a robust mathematical framework for modeling the dynamic behavior of brain networks. Unlike static analyses, ODEs capture the temporal evolution of neural activity, enabling detailed exploration of how network states evolve over time. This capability is particularly critical in neuroscience, where brain activity is inherently dynamic and shaped by complex interactions between structural connectivity and functional processes. ODE-based models enable the simulation of perturbations, such as external stimuli or pathological changes, as they propagate through neural networks, offering valuable insights into the mechanisms underlying cognitive functions and neurological disorders.

Furthermore, ODEs present a principled approach to integrating multimodal data, such as structural MRI, functional MRI, and electrophysiological recordings, by modeling how structural connectivity drives dynamic functional interactions. Topics of interest include ODE-based modeling in neuroscience, the integration of multimodal data, and causal inference in brain networks. This workshop will also investigate how ODE models can advance our understanding of cognitive functions and dysfunctions, as well as the integration of neural ODE frameworks for dynamic modeling in connectomics.

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