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Related Experiment Video

Updated: May 17, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Whole-brain causal discovery using fMRI.

Fahimeh Arab¹, AmirEmad Ghassami², Hamidreza Jamalabadi³

¹Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA.

Network Neuroscience (Cambridge, Mass.)

|March 31, 2025

Summary

This summary is machine-generated.

Discovering brain connections from fMRI is hard. A new method, Causal discovery for Large-scale Low-resolution Time-series with Feedback (CaLLTiF), accurately maps brain networks, overcoming limitations of older techniques.

Keywords:

Brain networks Causal discovery Cognitive neuroscience Statistical algorithms fMRI

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Last Updated: May 17, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

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

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

Published on: June 30, 2018

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Area of Science:

Neuroscience
Computational Biology
Network Science

Background:

Discovering causal relationships in functional magnetic resonance imaging (fMRI) data is a significant challenge.
Existing methods like Granger causality and dynamic causal modeling struggle with contemporaneous effects and latent common causes.
Causal structure learning methods face scalability issues and often require acyclic assumptions, limiting their application to large-scale brain networks.

Purpose of the Study:

To address limitations in current fMRI causal discovery methods.
To develop a scalable and accurate method for inferring causal relationships from large-scale, low-resolution time-series fMRI data, incorporating feedback.
To establish a new standard for causal discovery in whole-brain fMRI analysis.

Main Methods:

A taxonomy and comparative analysis of existing fMRI causal discovery methods were performed on simulated data.
A novel constraint-based method, Causal discovery for Large-scale Low-resolution Time-series with Feedback (CaLLTiF), was developed.
CaLLTiF utilizes conditional independence tests on contemporaneous and lagged variables to identify causal links.

Main Results:

CaLLTiF demonstrated superior accuracy and scalability compared to existing methods on simulated fMRI data from the macaque connectome.
Analysis of human resting-state fMRI revealed highly consistent causal connectomes across individuals using CaLLTiF.
Learned causal connectomes exhibit a top-down causal flow from attention and default mode networks to sensorimotor networks, with effects dependent on Euclidean distance and dominated by contemporaneous interactions.

Conclusions:

CaLLTiF represents a significant advancement in causal discovery from whole-brain fMRI data.
The method overcomes key limitations of previous approaches, offering improved accuracy and scalability.
This work sets a new benchmark for future research in understanding brain connectivity and function through causal inference.