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Updated: Jun 22, 2026

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

MATLAB toolbox for functional connectivity.

Dongli Zhou¹, Wesley K Thompson, Greg Siegle

¹Department of Statistics, University of Pittsburgh, Pittsburgh, PA 15260, USA. doz5@pitt.edu

|June 13, 2009

Summary

This summary is machine-generated.

This study introduces a MATLAB toolbox for analyzing brain functional connectivity, comparing various methods to understand their relationships and providing guidance for their use in neuroimaging research.

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Last Updated: Jun 22, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

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Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Area of Science:

Neuroimaging
Cognitive Neuroscience
Computational Neuroscience

Background:

Functional connectivity (FC) measures correlations in brain region activation.
PET and fMRI are common methods, but quantification varies, creating ambiguity.
Understanding different FC measures is crucial for accurate interpretation.

Purpose of the Study:

To implement and evaluate diverse functional connectivity measures.
To compare whole time-series and trial-based approaches.
To provide recommendations for using these measures in neuroimaging.

Main Methods:

Developed a MATLAB toolbox for functional connectivity analysis.
Categorized measures into whole time-series and trial-based.
Evaluated measures using simulations and a real fMRI dataset.

Main Results:

All implemented measures detected functional connectivity between dACC and DLPFC.
Different participants showed qualitatively distinct functional connectivity patterns.
The study highlights the variability in functional connectivity quantification.

Conclusions:

The toolbox facilitates the comparison of various functional connectivity metrics.
Recommendations are provided for selecting appropriate measures.
Understanding measure-specific patterns is key for interpreting brain connectivity.