A generative model of whole-brain effective connectivity | JoVE Visualize

Area of Science:

Computational neuroimaging
Systems neuroscience
Network neuroscience

Background:

Inferring effective (directed) connection strengths from fMRI data in whole-brain models is a significant challenge.
Existing methods like regression dynamic causal modeling (rDCM) scale to large networks but face interpretability and parameter estimation issues.
Large-scale networks with thousands of connections are difficult to interpret, and precise estimation of all parameters is often hindered by insufficient data points per parameter.

Purpose of the Study:

To introduce sparsity constraints into the variational Bayesian framework of rDCM for task-based fMRI.
To enable efficient effective connectivity analyses in whole-brain networks without requiring a priori connectivity structure assumptions.
To address the challenges of interpretability and parameter estimation in large-scale network analyses.

Main Methods:

Incorporation of sparsity constraints into the variational Bayesian framework of regression dynamic causal modeling (rDCM).
Derivation of variational Bayesian update equations for sparse rDCM.
Validation using both simulated and empirical functional magnetic resonance imaging (fMRI) data.

Main Results:

Demonstrated feasibility of inferring effective connection strengths from fMRI data in networks exceeding 100 regions and 10,000 connections.
Achieved whole-brain effective connectivity inference in single subjects with a run-time under one minute using parallelized code.
Sparse rDCM successfully prunes fully connected networks during model inversion without prior structural assumptions.

Conclusions:

Sparse rDCM provides a computationally efficient and scalable solution for whole-brain effective connectivity inference from fMRI data.
The method overcomes limitations of previous approaches, enabling precise parameter estimation and improved interpretability in large-scale neural networks.
Potential applications include advancing connectomics research and clinical neuromodeling, such as patient phenotyping based on whole-brain network structure.