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Brain Imaging

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Related Experiment Video

Updated: Nov 2, 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

Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning.

Simon Wein^1,2, Gustavo Deco^3,4, Ana Maria Tomé⁵

¹CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany.

Computational Intelligence and Neuroscience

|June 17, 2021

Summary

This review explores how machine learning models brain structure and function. It examines how anatomical connections influence dynamic brain activity, advancing our understanding of neural mechanisms.

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Last Updated: Nov 2, 2025

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

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Published on: November 8, 2012

Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

Microdissection of Mouse Brain into Functionally and Anatomically Different Regions

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Published on: February 15, 2021

Area of Science:

Neuroscience
Computational Neuroscience
Medical Imaging

Background:

Brain function relies on complex interactions within a stable structural network.
Diffusion Tensor Imaging (DTI) maps structural connectivity (SC).
Functional Magnetic Resonance Imaging (fMRI) reveals dynamic neuronal activity.

Purpose of the Study:

To review machine learning methods for understanding brain structure-function relationships.
To explore how fast neural dynamics emerge from the anatomical substrate.
To bridge the gap between structural and functional brain imaging analyses.

Main Methods:

Review of recent literature focusing on machine learning applications.
Analysis of computational simulations and graph theory in brain modeling.
Integration of DTI and fMRI data through machine learning.

Main Results:

Machine learning effectively identifies brain networks and classifies brain states.
ML models can elucidate functional interactions based on anatomical data.
The stable structural backbone influences emergent functional dynamics.

Conclusions:

Machine learning offers powerful tools for dissecting brain structure-function relationships.
Understanding these relationships is key to deciphering brain mechanisms.
Further research integrating ML with neuroimaging data is crucial.