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Connectome analysis of male world-class gymnasts using probabilistic multishell, multitissue constrained spherical

Hiroyuki Tomita¹, Koji Kamagata², Christina Andica²

¹Juntendo University Graduate School of Health and Sports Science, Chiba, Japan.

Journal of Neuroscience Research

|July 10, 2021

Summary

Intensive training in world-class gymnasts (WCGs) reshapes brain networks, enhancing connectivity in sensorimotor and other key areas. This brain plasticity is linked to athletic performance and may offer objective evaluation markers.

Keywords:

brain plasticity diffusion MRI graph theory motor skills network-based statistic probabilistic tractography

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Area of Science:

Neuroscience
Sports Science
Brain Imaging

Background:

Long-term intensive training in athletes is associated with enhanced athletic abilities and potential brain plasticity.
Understanding the neural underpinnings of elite athletic performance is crucial for optimizing training and injury prevention.

Purpose of the Study:

To investigate the structural connectome and brain anatomical topology in world-class gymnasts (WCGs) compared to controls.
To identify neural mechanisms associated with elite gymnastic skills and performance.

Main Methods:

Diffusion-weighted magnetic resonance imaging (dMRI) probabilistic tractography was used to map the structural connectome.
A multishell, multitissue constrained spherical deconvolution method enhanced tractography precision.
Network-based statistic and graph theory analysis were employed to compare WCGs and controls.

Main Results:

WCGs exhibited increased connectivity density in subnetworks involving sensorimotor, default mode, attentional, visual, and limbic areas.
Structural connectivity correlated with gymnastic skills and performance metrics (D-score).
Graph theory analysis revealed increased global connectivity strength, decreased characteristic path length, and higher nodal strength/degree in WCGs.

Conclusions:

Long-term intensive training induces significant brain anatomical network plasticity in WCGs.
These findings enhance understanding of neural mechanisms differentiating WCGs and suggest potential objective markers for performance evaluation.