Differential functional connectivity of the Sensorimotor Lateral r in football players
Poster No:
2339
Submission Type:
Abstract Submission
Authors:
wei zou1, Yidan Qiu2, Fengguang Xia3
Institutions:
1School of Psychology, Key Laboratory of Brain, South China Normal University, Guangzhou, China, Guangzhou, China, China, 2South China Normal University, Guangzhou, Guangdong, 3Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou，China, China
First Author:
wei zou
School of Psychology, Key Laboratory of Brain, South China Normal University, Guangzhou, China
Guangzhou, China, China
School of Psychology, Key Laboratory of Brain, South China Normal University, Guangzhou, China
Guangzhou, China, China
Co-Author(s):
Fengguang Xia
Institute for Brain Research and Rehabilitation, South China Normal University
Guangzhou，China, China
Institute for Brain Research and Rehabilitation, South China Normal University
Guangzhou，China, China
Introduction:
Long-term motor skill learning can induce plastic structural and functional reorganization of the brain [1,2]. Motor skill training refers to the process of repeated practice and interactions with the environment to achieve effortless movements [3]. Understanding the interactions between brain regions in football training can help to provide a more comprehensive of brain plasticity and the effects of training on brain function and brain connectivity. Further contributes to the design of more targeted measures in future physical education to improve soccer sport training [4,5]. In this study, rs-fMRI was used to investigate the differences in functional brain activity between football players and non-athletes. The resting-state functional connectivity indexes were calculated using CONN 20.b software, comparing the differences between the football group and the control group.
Methods:
Subjects
Twenty-three female football players (aged 20.60±4.1 years old) and 24 female non-athlete normal controls (aged 19.63±1.95 years old) were recruited.The study has been approved by the Human Research Ethnics Committee of South China Normal University. Written informed consent was obtained from each subject prior to this study.
Data acquisition
All Magnetic Resonance Imaging (MRI) datasets were obtained on a 3T Siemens Trio Tim MR scanner with the use of a 64 channel phased-array received-only head coil. The rest fMRI datasets were acquired using a gradient echo EPI sequence with the following parameters: TR/TE = 2000/30ms, thickness = 3.5mm, flip angle = 90°, field of view (FOV) = 224mm×224mm, matrix = 64×64. High-resolution structural brain images were obtained using a T1-weighted 3D MP-RAGE sequence (TR/TE = 2300/3.24ms, thickness = 1mm, flip angle = 9°, FOV = 256 mm×256 mm, bandwidth=210Hz/pixel. For each subject, the rest fMRI data and 3D high-resolution brain structural images were acquired in the same session.
Data preprocessing
The fMRI data were preprocessed using DPARSF and CONN based on Matlab (2018a). The preprocessing procedure contained the following steps: 1) Setup, ①Manually enter the number of subjects=47, the repetition time TR=2 and the number of experimental sessions=1 and select the acquisition type and other basic experimental parameter settings. ②Import Dpabi preprocessed functional and structural image data files, ③Define the region of interest (ROI) based on the experimental hypothesis or the results of the pre-processing analysis. The ROI of this study is determined as Sensorimotor Lateral r, (2) Denoising, (3) First-level analyses functional connectivity analysis was performed by selecting the previously defined ROIs, (4) Second-level analyses.
Twenty-three female football players (aged 20.60±4.1 years old) and 24 female non-athlete normal controls (aged 19.63±1.95 years old) were recruited.The study has been approved by the Human Research Ethnics Committee of South China Normal University. Written informed consent was obtained from each subject prior to this study.
Data acquisition
All Magnetic Resonance Imaging (MRI) datasets were obtained on a 3T Siemens Trio Tim MR scanner with the use of a 64 channel phased-array received-only head coil. The rest fMRI datasets were acquired using a gradient echo EPI sequence with the following parameters: TR/TE = 2000/30ms, thickness = 3.5mm, flip angle = 90°, field of view (FOV) = 224mm×224mm, matrix = 64×64. High-resolution structural brain images were obtained using a T1-weighted 3D MP-RAGE sequence (TR/TE = 2300/3.24ms, thickness = 1mm, flip angle = 9°, FOV = 256 mm×256 mm, bandwidth=210Hz/pixel. For each subject, the rest fMRI data and 3D high-resolution brain structural images were acquired in the same session.
Data preprocessing
The fMRI data were preprocessed using DPARSF and CONN based on Matlab (2018a). The preprocessing procedure contained the following steps: 1) Setup, ①Manually enter the number of subjects=47, the repetition time TR=2 and the number of experimental sessions=1 and select the acquisition type and other basic experimental parameter settings. ②Import Dpabi preprocessed functional and structural image data files, ③Define the region of interest (ROI) based on the experimental hypothesis or the results of the pre-processing analysis. The ROI of this study is determined as Sensorimotor Lateral r, (2) Denoising, (3) First-level analyses functional connectivity analysis was performed by selecting the previously defined ROIs, (4) Second-level analyses.
Results:
Figure 1 shows a significant difference in functional connectivity in the right Sensorimotor Lateral, the right Superior Temporal Gyrus, posterior division (pSTG r) between the football group and the control group.
Conclusions:
The present study shows significant differences in functional connectivity between the football players and the control group in Superior Temporal Gyrus in the resting state. These plastic and adaptive changes in brain structure due to exercise training may improve the regulation of sensory, motor and cognitive functions.
Learning and Memory:
Skill Learning 2
Novel Imaging Acquisition Methods:
BOLD fMRI 1
Keywords:
FUNCTIONAL MRI
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