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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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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

Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox.

Andre Santos Ribeiro¹, Luis Miguel Lacerda², Hugo Alexandre Ferreira³

¹Division of Brain Sciences, Department of Medicine, Centre for Neuropsychopharmacology, Imperial College London , UK ; Faculty of Sciences, Institute of Biophysics and Biomedical Engineering, University of Lisbon , Portugal.

|July 25, 2015

Summary

This summary is machine-generated.

The Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox streamlines brain network analysis using diverse neuroimaging data. It integrates pre-processing, connectivity, and graph theory for comprehensive human brain studies.

Keywords:

Connectivity DTI MIBCA MRI Multimodal PET Toolbox fMRI

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Area of Science:

Neuroimaging
Brain Connectivity
Computational Neuroscience

Background:

Neuroimaging studies have advanced understanding of brain network organization.
Data analysis for multimodal neuroimaging is time-consuming and complex.
A unified toolbox for end-to-end multimodal brain connectivity analysis was lacking.

Purpose of the Study:

To develop the Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox.
To reduce data processing time and enable comprehensive brain connectivity analysis.
To integrate various connectivity analyses for multimodal imaging data.

Main Methods:

MIBCA is an automated, all-in-one MATLAB toolbox for multimodal neuroimaging data.
It performs pre-processing, structural, functional, and effective connectivity analyses.
Utilizes established software (Freesurfer, SPM, FSL) and graph theory metrics.

Main Results:

Structural data showed high inter-hemispheric symmetry and intra-hemispheric modularity.
Functional data exhibited lower inter-hemispheric symmetry and high inter-hemispheric modularity.
Age-related differences (<40 vs >40 years) revealed significant changes in brain volume, thickness, and diffusivity.

Conclusions:

MIBCA bridges the gap between numerous existing neuroimaging tools.
It offers novel ways to combine, analyze, and visualize multimodal brain data.
Facilitates a deeper understanding of human brain organization and connectivity.