Characteristics Of Resting State Networks Of Elite Skating Athletes: An ICA Analysis

Abstract

Long-term motor skill learning can lead neuro plasticity changes. Until now, this conclusion is mainly proved by task fMRI and spontaneous brain activity evidence. However, these methods heavily rely on prior knowledge and hypotheses, and exist algorithm limitations. PURPOSE: To investigate the differences of resting state networks (RSNs) between elite skating athletes and non-athlete controls by means of data-driven approach. METHODS: Resting state fMRI data were acquired by Philips Achieva 3.0T scanner with a standard 32 channel head coil from 15 Chinese national level skating athletes (all men, 20.87±1.78 years old, with an average training year of 9.67±3.50) and 15 demographically matched healthy controls (all men, 20.85±1.83 years old). Gift was used to perform ICA (independent component analysis) arithmetic calculations and identify RSNs, including default mode network, somatomotor network, dorsal attention network, left fronto-parietal network and visual network. A two-sample t-test was then conducted using SPM12 to investigate whether there were significant differences between two groups. Results were reported when voxel significant at a level of p < 0.01. Cluster-level whole-brain family wise error (FWE) was applied for multiple comparison correction (cluster p< 0.01). Coordinates are given in Montreal Neurological Institute(MNI) space. RESULTS: 1. Athletes showed higher spontaneous activity in postcentral gyrus (cluster size =230, peak coordinate =69, -12,18, peak t =11.88) and cingulate gyrus (cluster size =167, peak coordinate= 6, -3,39, peak t =6.65) in somatomotor network. 2. Athletes showed higher spontaneous activity in precuneus (cluster size =337, peak coordinate =30, -66,39, peak t =10.38) in dorsal attention network. CONCLUSIONS: Elite skating athletes showed better functional connectivity in somatomotor network and dorsal attention network, which may further indicate that long-term specialized motor training may promote functional network activation patterns.