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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: Jun 4, 2025

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

ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI.

Yuqi Fang¹, Junhao Zhang², Linmin Wang²

¹Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.

|December 24, 2024

Summary

This summary is machine-generated.

ACTION is a new open-source toolbox for functional magnetic resonance imaging (fMRI) analysis. It offers data augmentation and deep learning for improved brain network analysis, especially with limited data.

Keywords:

Brain network analysis Deep learning model Federated learning Functional MRI augmentation Toolbox

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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

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

Related Experiment Videos

Last Updated: Jun 4, 2025

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

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

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

Area of Science:

Neuroscience
Computer Science
Biomedical Engineering

Background:

Functional magnetic resonance imaging (fMRI) is crucial for studying brain activity.
Existing fMRI analysis toolboxes lack data augmentation and deep learning capabilities.
Limited or imbalanced fMRI data presents challenges for conventional analysis.

Purpose of the Study:

Introduce Augmentation and Computation Toolbox for braIn netwOrk aNalysis (ACTION).
Provide comprehensive fMRI analysis functions, including data augmentation and deep learning.
Enhance fMRI analysis for limited data scenarios and enable custom algorithm development.

Main Methods:

Developed ACTION, a Python-based, cross-platform toolbox with a user-friendly interface.
Implemented automatic fMRI augmentation for BOLD signals and brain networks.
Integrated deep learning models with pretraining on large-scale fMRI data (3,800+ scans).
Included federated learning strategies for multi-site studies and scripting for custom algorithms.

Main Results:

Demonstrated the effectiveness and user-friendliness of ACTION on real fMRI data.
ACTION streamlines fMRI analysis, offering advanced augmentation and deep learning features.
The toolbox supports brain network construction, feature extraction, and model pretraining.

Conclusions:

ACTION provides a powerful, extensible platform for advanced fMRI analysis.
The toolbox addresses limitations in current fMRI analysis software, particularly for data augmentation and deep learning.
ACTION facilitates robust brain network analysis and supports multi-site research through federated learning.