Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players
Harish RaviPrakash1, Syed Muhammad Anwar1,2, Nadia M Biassou3
1Department of Computer Science, Center for Research in Computer Vision, University of Central Florida, Orlando, FL, United States.
Frontiers in Neuroscience
|March 8, 2021
Summary
Brain imaging reveals distinct functional and anatomical differences in chess players, supporting the idea that skill acquisition changes brain structure and function. This study introduces a novel computational tool for analyzing these changes.
Area of Science:
- Neuroscience
- Computational Biology
- Cognitive Science
Background:
- Analyzing differences between healthy individuals with varying skill proficiency is challenging.
- Existing brain imaging analysis often focuses on disease diagnosis, not skill-based variations.
Purpose of the Study:
- To develop computational tools for exploring functional and anatomical brain differences in healthy individuals based on skill proficiency.
- To investigate differences between amateur and professional chess players using neuroimaging data.
Main Methods:
- Utilized resting-state functional magnetic resonance imaging (fMRI) for functional connectivity (FC).
- Employed T1-weighted MRI for morphometric connectivity (MC).
- Combined FC and MC into a novel functional morphometric similarity connectome (FMSC) and used machine learning (support vector machine) for classification.
Main Results:
- Identified significant functional and anatomical differences in the saliency and ventral attention networks between amateur and professional chess players.
- Demonstrated the effectiveness of the FMSC algorithm in differentiating between skill groups.
Conclusions:
- Skill acquisition, like chess expertise, leads to measurable changes in brain function and anatomy.
- The developed FMSC algorithm provides a valid computational pipeline for testing neuroscience hypotheses on skill-induced brain plasticity.
