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Related Experiment Video

Updated: Jun 16, 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

Mapping change in large networks.

Martin Rosvall¹, Carl T Bergstrom

¹Department of Biology, University of Washington, Seattle, Washington, United States of America. rosvall@u.washington.edu

|January 30, 2010

Summary

This summary is machine-generated.

This study introduces significance clustering with bootstrap resampling to differentiate real network trends from noise. It reveals how neuroscience evolved into a distinct discipline by analyzing journal citation patterns.

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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

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Published on: May 7, 2014

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Last Updated: Jun 16, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Area of Science:

Network science
Bibliometrics
Scientific collaboration

Background:

Interaction patterns across various domains (biology, technology, economy, science) are dynamic.
Network analysis and clustering are crucial for understanding large-scale structures.
Distinguishing genuine trends from noise is essential for studying network evolution, but current methods are insufficient.

Purpose of the Study:

To develop a method for assigning statistical significance to network partitions.
To enable the identification of meaningful structural changes in networks.
To connect network structure changes with functional implications.

Main Methods:

Bootstrap resampling combined with significance clustering to identify statistically significant network partitions.
Alluvial diagrams to visualize and summarize significant structural changes.
Analysis of citation patterns among approximately 7000 scientific journals over a decade.

Main Results:

The proposed method effectively distinguishes significant network changes from random fluctuations.
Alluvial diagrams provide a clear visualization of evolving network structures.
Analysis of scientific journal citations demonstrates the transformation of neuroscience from an interdisciplinary field to a standalone discipline.

Conclusions:

Significance clustering with bootstrap resampling offers a robust approach to studying dynamic networks.
This methodology allows for the objective mapping of scientific field evolution.
The findings illustrate a significant shift in the structure and maturity of the neuroscience discipline over the past decade.