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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Related Experiment Video

Updated: Sep 21, 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

A Riemannian approach to predicting brain function from the structural connectome.

Oualid Benkarim¹, Casey Paquola², Bo-Yong Park³

¹McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.

|May 31, 2022

Summary

This summary is machine-generated.

Researchers developed a new method to predict brain function from its wiring using random walks on the structural connectome. This approach accurately models brain network interactions, highlighting the importance of polysynaptic pathways in complex brain networks.

Keywords:

Diffusion maps Functional connectivity Manifold optimization Structural connectome

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Last Updated: Sep 21, 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

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

Neuroscience
Computational Neuroscience
Network Science

Background:

Brain function relies on structural connections, but the precise rules are unclear.
Functional brain interactions may arise from mono- and polysynaptic pathways.
Understanding this relationship is key to deciphering brain dynamics.

Purpose of the Study:

To develop a novel computational approach for predicting functional brain interactions from structural connectomes.
To emulate dynamic communication mechanisms using random walks on brain networks.
To investigate the role of polysynaptic pathways in large-scale brain networks.

Main Methods:

Utilized diffusion maps and Riemannian optimization to model random walks on the structural connectome.
Predicted functional interactions as a weighted combination of these random walks.
Validated the approach using data from the Human Connectome Project (HCP) and Microstructure-Informed Connectomics (MICs) cohorts.

Main Results:

The proposed method outperformed existing approaches in predicting functional interactions.
Predictive performance plateaued around the third random walk, suggesting a limit to the influence of longer pathways.
Transmodal cortical networks (default mode and frontoparietal) required more walks, indicating greater reliance on polysynaptic communication compared to primary systems.

Conclusions:

The novel random walk approach effectively predicts functional brain interactions from structural connectivity.
Polysynaptic communication is increasingly important in higher-order transmodal brain networks.
This work provides insights into the micro- and macroscale principles governing brain network dynamics.