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Related Experiment Video

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Revealing Relationships Among Cognitive Functions Using Functional Connectivity and a Large-Scale Meta-Analysis

Hiroki Kurashige1,2, Jun Kaneko1, Yuichi Yamashita2

  • 1Institute of Innovative Science and Technology, Tokai University, Tokyo, Japan.

Frontiers in Human Neuroscience
|January 31, 2020
PubMed
Summary

This study reveals intricate relationships between 109 cognitive functions using brain mapping and network analysis. Findings map cognitive functions in a 2D space, uncovering sub-functions and their network properties for AI and clinical insights.

Keywords:
data miningfMRIfunctional connectivityhuman brainmachine learningmeta-analysis databasenetwork analysis

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Understanding relationships among cognitive functions is crucial for neuroscience, clinical applications, and AI.
  • Existing methods lack a comprehensive approach to map these complex interrelations.

Purpose of the Study:

  • To develop and apply an exhaustive data mining approach to reveal relationships among 109 cognitive functions.
  • To visualize cognitive function relationships using functional brain mapping and network analysis.
  • To identify sub-concepts within cognitive functions and their network characteristics.

Main Methods:

  • Utilized 109 cognitive function maps (CFMs) from fMRI meta-analysis.
  • Applied resting-state functional connectivity to map CFMs in a 2D space.
  • Conducted conceptual analysis via voxel clustering within CFMs.
  • Performed network analysis on whole-brain parcels based on voxel-to-CFM connectivities.

Main Results:

  • Mapped cognitive functions in a 2D space, showing related functions clustered together.
  • Identified sub-functions within each CFM based on associations with other cognitive functions.
  • Found that parcel informational diversity and local connectivity density correlate with associated cognitive functions.
  • Discovered homogeneous and inhomogeneous network communities related to specific cognitive functions.

Conclusions:

  • The proposed approach effectively integrates functional brain mapping meta-analysis with network neuroscience.
  • This fusion provides novel insights into the relationships among cognitive functions and their underlying network structures.
  • The findings have implications for basic neuroscience, clinical diagnostics, and the development of brain-inspired AI.