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Magnetic Resonance Imaging

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Related Experiment Video

Updated: Mar 6, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Regression DCM for fMRI.

Stefan Frässle¹, Ekaterina I Lomakina², Adeel Razi³

¹Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland.

|March 6, 2017

Summary

This summary is machine-generated.

This study introduces regression Dynamic Causal Models (rDCM) for whole-brain functional magnetic resonance imaging (fMRI) analysis. rDCM significantly accelerates effective connectivity inference in large neural networks.

Keywords:

Bayesian regression Connectomics Dynamic causal modeling Effective connectivity Generative model Variational Bayes

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Last Updated: Mar 6, 2026

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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

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Published on: June 30, 2018

Area of Science:

Computational Neuroscience
Neuroimaging Analysis
Network Science

Background:

Inferring effective connectivity in large-scale neural networks from neuroimaging data is a significant computational challenge.
Traditional Dynamic Causal Models (DCMs) are limited to small networks (approx. 10 regions) due to computational constraints during model inversion.
Existing methods struggle to scale for whole-brain analyses.

Purpose of the Study:

To develop a novel variant of DCM suitable for assessing effective connectivity in large, whole-brain networks using fMRI data.
To significantly improve the computational efficiency of inferring effective connectivity in complex neural systems.
To enable whole-brain connectomics by analyzing large-scale neural interactions.

Main Methods:

Developed regression DCM (rDCM) by translating linear DCM into the frequency domain, reformulating it as Bayesian linear regression.
Implemented a variational Bayesian inversion method for extremely fast inference.
Validated rDCM using simulated and empirical fMRI data across various signal-to-noise ratios (SNR) and repetition times (TR).

Main Results:

rDCM demonstrated significantly accelerated model inversion, orders of magnitude faster than classical DCM.
The method showed face validity across different SNR and TR settings in fMRI data.
Successfully inferred effective connection strengths in a simulated 66-region whole-brain network with 300 parameters.

Conclusions:

rDCM is a computationally efficient approach for inferring effective connectivity in large-scale neural networks.
The method shows significant promise for advancing whole-brain connectomics using individual fMRI data.
rDCM overcomes limitations of traditional DCM, enabling more scalable network analyses.