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Related Experiment Video

Updated: Nov 1, 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

Structurally constrained effective brain connectivity.

Alessandro Crimi¹, Luca Dodero², Fabio Sambataro³

¹Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia, via Enrico Melen 83, Genova 16152, Italy; Institute of Neuropathology, University Hospital of Zurich, Raemistrasse 100, Zurich 8091, Switzerland.

|June 20, 2021

Summary

This summary is machine-generated.

This study introduces a new model for effective brain connectivity, linking brain structure to function. The model improves understanding of brain networks and aids in identifying biomarkers for conditions like autism spectrum disorder.

Keywords:

Autism spectrum disorder Connectome DCM DWI Diffusion MRI Effective connectivity Granger Tractography fMRI

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

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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

Area of Science:

Neuroscience
Computational Biology
Biophysics

Background:

Understanding the brain's structure-function relationship is crucial for neuroscience.
Integrating structural and functional data aids in deciphering brain network principles.
Existing models may not fully capture the nuances of effective connectivity.

Purpose of the Study:

To propose a constrained autoregressive model for effective connectivity analysis.
To investigate how brain structure modulates neural function.
To identify novel biomarkers for subject group characterization.

Main Methods:

A constrained autoregressive model is developed, re-weighting structural connectivity to explain functional co-activations.
The model minimizes reconstruction error using a structural connectivity prior.
It accounts for both direct and indirect connections and is compatible with raw and deconvoluted BOLD signals.

Main Results:

The proposed model's effective connectivity representation shows improved accuracy compared to Dynamic Causal Modeling.
In a community detection task, effective connectivity clusters better represent functional interactions while preserving structural organization.
The model achieved better discrimination in a case-control study differentiating healthy subjects from those with autism spectrum disorder.

Conclusions:

The constrained autoregressive model offers a robust method for analyzing effective brain connectivity.
This approach enhances the understanding of structure-function relationships in neural networks.
The model shows potential for biomarker discovery and clinical applications in neurological disorders.