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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

Groupwise whole-brain parcellation from resting-state fMRI data for network node identification.

X Shen¹, F Tokoglu, X Papademetris

¹Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT 06520, USA. xilin.shen@yale.edu

|June 11, 2013

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This study introduces a novel graph-theory approach for functional Magnetic Resonance Imaging (fMRI) network analysis. The method creates reproducible brain parcellations, ensuring functional homogeneity for more accurate network studies.

Area of Science:

Neuroimaging
Network Neuroscience
Computational Neuroscience

Background:

Functional Magnetic Resonance Imaging (fMRI) network analysis requires precise node definition.
Existing atlases may lack functional homogeneity within nodes, potentially misrepresenting brain activity.
A robust method for defining functionally consistent brain regions is needed.

Purpose of the Study:

To develop a groupwise, graph-theory-based parcellation method for defining functionally homogeneous brain nodes.
To ensure consistency of these node definitions across a group of individuals.
To provide reproducible functional atlases for fMRI network analysis.

Main Methods:

A groupwise optimization approach was employed to parcellate brain data.

Keywords:

Functional MRI Graph-theory-based parcellation Network analysis Resting-state connectivity Whole-brain atlas

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Functional homogeneity within each defined subunit (node) was ensured.

Parcellation reproducibility was assessed across multiple groups of healthy volunteers.

Main Results:

The proposed graph-theory approach demonstrated high reproducibility for brain parcellation subunits.
Parcellation results were generated for 100, 200, and 300 subunits, considering fMRI resolution.
Three functional atlases at different parcellation levels are available online.

Conclusions:

The developed groupwise parcellation method offers a reliable way to define nodes for fMRI network analysis.
This approach enhances functional homogeneity and group-level consistency.
Freely available functional atlases and interfacing tools support broader application in neuroimaging research.