Adaptive changes in the functional connectivity of the M1 in basketball athletes based on mICA
Poster No:
1383
Submission Type:
Abstract Submission
Authors:
Xiaoxia Du1, Yapeng Qi2
Institutions:
1Shanghai Sport University, Shanghai, Shanghai, 2Shanghai University of Sport, Shanghai, Shanghai
First Author:
Co-Author:
Introduction:
The precentral gyrus (M1) encompasses a continuous somatic homunculus, which regulates body movements from the feet to the face. Recent investigations conducted by Evan M. Gordon and Nico U. F. Dosenbach's research team, utilizing precise functional magnetic resonance imaging (fMRI) techniques, have revealed that M1 is differentiated into distinct regions(Gordon et al., 2023). Each of these regions corresponds to specific effector zones (feet, hands, and mouth) and possesses unique connections, structures, and functionalities. This segmentation underscores the significance of M1 in motion control and its relationship with the somatic cognitive action network (SCAN) (Gordon et al., 2023). The objective of this study is to examine whether professional basketball athletes exhibit adaptive changes in the M1 region as a result of prolonged exercise training. It is hypothesized that the M1 region of basketball athletes will demonstrate such adaptive changes, particularly in the functional connections associated with the SCAN network, which plays an important role in motor control.
Methods:
To validate this hypothesis, a cohort of 50 basketball athletes at or above the second competitive level was recruited and matched with 50 non-athletes based on age and gender. We employed the masked-independent component analysis (mICA, https://www.nitrc.org/projects/mica/) method to isolate different functional subregions of M1, successfully establishing connections for brain-wide functional connectivity(Moher Alsady et al., 2016). Split-half reproducibility analysis was performed in the dimensionality range from 2 to 25 components. mICA with 7 dimensions was chosen for further analysis because it showed the highest reproducibility. Components with a correlation coefficient above 0.85 were considered reproducible. A two-sample t-test was subsequently performed to identify inter-group differences.
Results:
Six of these components demonstrated physiological relevance, while one primarily served to eliminate noise. Three components revealed extensive connections with the motor cortex and were categorized within the effector region (Figure 1 A, B, C). In contrast, the remaining three components indicated increased functional connections with the cognitive brain area (cingulo-opercular network), potentially integrating into the SCAN network (Figure 1 D, E, F). In comparison to non-athletes, basketball athletes exhibited enhanced local functional connections in M1(Figure 2D) with the cerebellar and visual cortex, as determined by a nonparametric test with 5000 permutations for TFCE correction (p < 0.05).
·Figure1 Two groups used ICA to divide M1 into six independent components, as well as the functional connections between these six subregions and the whole brain.
·Figure 2 Athletes show increased functional connectivity between the M1 subregion (component D), the cerebellum, and the visual cortex compared to non-athletes.
Conclusions:
The study successfully delineated various subregions of M1 utilizing mICA, differentiating between the effector region and the region linked to the SCAN network. The group analysis results revealed that the connectivity between M1 and the cerebellum and the visual area was augmented in basketball athletes. This enhancement suggests that athletes may possess improved visual integration and coordination in motion control, alongside a fortified regulatory and feedback capacity from the cerebellum during physical activity (Schmahmann et al., 2019).
Learning and Memory:
Skill Learning
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Motor Behavior:
Motor Planning and Execution
Motor Behavior Other
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems 2
Keywords:
FUNCTIONAL MRI
Motor
NORMAL HUMAN
Plasticity
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Moher Alsady, T., Blessing, E. M., & Beissner, F. (2016). MICA—A toolbox for masked independent component analysis of fMRI data. Human Brain Mapping, 37(10), 3544–3556. https://doi.org/10.1002/hbm.23258
Schmahmann, J. D., Guell, X., Stoodley, C. J., & Halko, M. A. (2019). The Theory and Neuroscience of Cerebellar Cognition. Annual Review of Neuroscience, 42(1), 337–364. https://doi.org/10.1146/annurev-neuro-070918-050258