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Updated: Oct 29, 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

Dyconnmap: Dynamic connectome mapping-A neuroimaging python module.

Avraam D Marimpis^1,2,3, Stavros I Dimitriadis^2,4,5,6,7,8, Rainer Goebel^1,3

¹Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.

Human Brain Mapping

|July 12, 2021

Summary

This summary is machine-generated.

Network neuroscience analyzes brain function using dynamic functional connectivity. This study introduces "chronnectome" to map time-varying brain activity, aiding in understanding cognition and brain states.

Keywords:

EEG MEG chronnectomics complex networks dynamic connectivity fMRI functional connectivity graph theory human connectome python statistical analysis

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3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

Area of Science:

Network neuroscience
Complex systems analysis
Neuroimaging data analysis

Background:

Human brain cognition and function remain incompletely understood despite neuroimaging advances.
Network neuroscience models brain structure and function using anatomical or functional connections.
Increasing neuroimaging data complexity necessitates new tools for dynamic pattern analysis.

Purpose of the Study:

To introduce novel algorithms and network metrics for capturing dynamic functional connectivity.
To define and explore the concept of the "chronnectome" for analyzing time-varying brain activity.
To demonstrate the utility of dynamic network analysis for identifying brain states.

Main Methods:

Development of algorithms and network metrics to analyze time-varying functional connectivity.
Introduction of the "chronnectome" concept to study temporal correlations in brain activity.
Utilizing a Python module (dyconnmap) for dynamic complex network analysis.

Main Results:

Algorithms and metrics effectively capture the dynamic evolution of functional connectivity.
The "chronnectome" approach facilitates the detection of evolving brain states.
The dyconnmap module shows promise for analyzing functional magnetic resonance imaging (fMRI) data.

Conclusions:

Dynamic functional connectivity analysis, termed "chronnectome," offers new insights into brain function.
Temporal mining of dynamic functional networks aids in understanding cognitive processes and brain states.
The dyconnmap Python module provides a valuable tool for neuroimaging research.