Correlation between functional connectivity, strength, and body mass index in soccer players
Poster No:
1446
Submission Type:
Abstract Submission
Authors:
Pourya Abbasi1, Alireza Fallahi2, Maryam Nourshahi3, Mohammad-Reza Nazem-Zadeh4
Institutions:
1Shahid Beheshti University, Tehran, Iran, 2Hamedan University of Technology, Hamedan, Iran, 3Shahid Beheshti University, Tehran, Tehran, 4Monash University, Melbourne, VIC
First Author:
Co-Author(s):
Introduction:
Soccer is a highly demanding sport that requires top physical capabilities and complex movement patterns. Previous studies have linked lower body fat percentages with improved sprinting, acceleration, and jumping performance (Nikolaidis, 2012). Our brain consists of interconnected regions forming large-scale networks, analyzed using functional magnetic resonance imaging (fMRI). Exercise, particularly resistance training, has been found to enhance cognitive functions through increased brain-derived neurotrophic factor levels. This has been shown to modify functional connectivity within several brain networks (Lira et al., 2020; Voss et al., 2010). We investigated the correlation between sport-related parameters and resting-state functional connectivity in soccer players.
Methods:
Twenty-five male soccer players (age 18-26 yrs, mean 22.4 ± 2.5 yrs) from different soccer clubs of the first nation division. Their anthropometric variables, including body height, body weight, and body mass index (BMI), were assessed using a portable system (Seca 222) and an InBody 770. The concentric isokinetic strength (peak torque of quadriceps [PT-Q]) was measured for the dominant limb via an isokinetic dynamometer (Biodex, Multi-joint System 3; Biodex Medical).
All subjects underwent brain imaging and scanned using a 3-Tesla Siemens Magnetom Prisma MRI (Siemens Prisma, Erlangen, Germany). Subjects are instructed to lie still for 15 minutes, focus on a visually presented screen, and keep their eyes open during fMRI. The first ten images were discarded to allow stabilization of the BOLD signal. Slice timing correction was done for the remaining volumes. Using the normalization parameters estimated by the T1 structural image, the realigned functional volumes were spatially normalized to the Montreal Neurological Institute (MNI) space DPARSF 4.3 (http://rfmri.org/dpabi).
Functional connectivity was calculated by using the Pearson correlation coefficient between each pair of ROI time series representing brain regional activity. To examine the relation between sport parameters and functional connectivity values, linear regression analysis was applied by using MATLAB 9.10. We applied FDR correction for multiple testing and corrected p-value = 0.05 was applied for significant relations in linear regression model.
All subjects underwent brain imaging and scanned using a 3-Tesla Siemens Magnetom Prisma MRI (Siemens Prisma, Erlangen, Germany). Subjects are instructed to lie still for 15 minutes, focus on a visually presented screen, and keep their eyes open during fMRI. The first ten images were discarded to allow stabilization of the BOLD signal. Slice timing correction was done for the remaining volumes. Using the normalization parameters estimated by the T1 structural image, the realigned functional volumes were spatially normalized to the Montreal Neurological Institute (MNI) space DPARSF 4.3 (http://rfmri.org/dpabi).
Functional connectivity was calculated by using the Pearson correlation coefficient between each pair of ROI time series representing brain regional activity. To examine the relation between sport parameters and functional connectivity values, linear regression analysis was applied by using MATLAB 9.10. We applied FDR correction for multiple testing and corrected p-value = 0.05 was applied for significant relations in linear regression model.
Results:
The BMI correlated negatively with the IPSR (intraparietal sulcus right)– LPFCL (lateral prefrontal cortex left) connection (p-value = 0.01, r2 = 0.19) related to DAN and FPN respectively (fig 1). The PT-Q also correlated positively with the mPFC (medial prefrontal cortex) - LPL (lateral parietal left) connection (p-value = 0.00, r2 = 0.40) related to DMN (fig 2).
·Fig 1. Significant relation of the ‘BMI’ parameter with functional connections. IPSR = intraparietal sulcus right medial prefrontal cortex – LPFCL = lateral prefrontal cortex left
·Fig 2. Significant relation of the ‘PT-Q’ parameter with functional connections. mPFC= medial prefrontal cortex – LPL= lateral parietal left
Conclusions:
The mPFC and LP contribute to various cognitive functions including episodic memory retrieval, attention. Thus, an increase in players' muscle strength correlates with enhanced cognitive function (Junior et al., 2024). With regard to BMI and body fat percentage, high level of these measures decreases cognitive abilities (Figley et al., 2016), as the IPSR and LPFCL are vital brain regions involved in various cognitive processes, such as attentional orienting and manipulation of information in working memory, respectively. Previous study demonstrated that there was a positive relationship between passing, shooting, dribbling skills, and functional connectivity of the sensorimotor network (SMN) and other physical components like agility, sprinting with DAN (Abbasi et al., 2025). This highlights the importance of cognitive and physical abilities working together to optimize athletic performance. Our findings reveal a significant correlation between resting-state functional connectivity, isokinetic strength performance, and BMI in soccer players.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Other - soccer performance, Isokinetic strength, fMRI, functional networks, brain, resting state functional connectivity
1|2Indicates the priority used for review
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Provide references using APA citation style.
Figley, C. R., Asem, J. S., Levenbaum, E. L., & Courtney, S. M. (2016). Effects of body mass index and body fat percent on default mode, executive control, and salience network structure and function. Frontiers in neuroscience, 10, 234.
Junior, M. B., Tavares, L. F. J., Nagata, G. Y., Barroso, L. S. S., Fernandes, H. B., Souza-Gomes, A. F., Miranda, A. S., & Nunes-Silva, A. (2024). Impact of strength training intensity on brain-derived neurotrophic factor. International journal of sports medicine, 45(02), 155-161.
Lira, F. S., de Freitas, M. C., Gerosa-Neto, J., Cholewa, J. M., & Rossi, F. E. (2020). Comparison between full-body vs. split-body resistance exercise on the brain-derived neurotrophic factor and immunometabolic response. The Journal of Strength & Conditioning Research, 34(11), 3094-3102.
Nikolaidis, P. (2012). Association between body mass index, body fat per cent and muscle power output in soccer players. Open Medicine, 7(6), 783-789.
Voss, M. W., Erickson, K. I., Prakash, R. S., Chaddock, L., Malkowski, E., Alves, H., Kim, J. S., Morris, K. S., White, S. M., & Wójcicki, T. R. (2010). Functional connectivity: a source of variance in the association between cardiorespiratory fitness and cognition? Neuropsychologia, 48(5), 1394-1406.