Multifaceted effects of collision sports participation to the brain:Structural connectivity findings
Poster No:
1217
Submission Type:
Abstract Submission
Authors:
Christian John Saludar1, Maryam Tayebi1,2, Eryn Kwon1,2,3, Joshua McGeown2,3, Edward John Clarkson1,2, Tuterangi Nepe-Apatu2, Paul Condron2,3, Leigh Potter2, Samantha Holdsworth2,3, Mātai mTBI Research Group2, Justin Fernandez1,2, Miriam Scadeng2,3, Alan Wang1,2,3, Vickie Shim1,2
Institutions:
1Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand, 2Mātai Medical Research Institute, Gisborne, New Zealand, 3Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland, Auckland, New Zealand
First Author:
Christian John Saludar
Auckland Bioengineering Institute, University of Auckland
Auckland, New Zealand
Auckland Bioengineering Institute, University of Auckland
Auckland, New Zealand
Co-Author(s):
Maryam Tayebi
Auckland Bioengineering Institute, University of Auckland|Mātai Medical Research Institute
Auckland, New Zealand|Gisborne, New Zealand
Auckland Bioengineering Institute, University of Auckland|Mātai Medical Research Institute
Auckland, New Zealand|Gisborne, New Zealand
Eryn Kwon
Auckland Bioengineering Institute, University of Auckland|Mātai Medical Research Institute|Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland
Auckland, New Zealand|Gisborne, New Zealand|Auckland, New Zealand
Auckland Bioengineering Institute, University of Auckland|Mātai Medical Research Institute|Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland
Auckland, New Zealand|Gisborne, New Zealand|Auckland, New Zealand
Joshua McGeown
Mātai Medical Research Institute|Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland
Gisborne, New Zealand|Auckland, New Zealand
Mātai Medical Research Institute|Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland
Gisborne, New Zealand|Auckland, New Zealand
Edward John Clarkson
Auckland Bioengineering Institute, University of Auckland|Mātai Medical Research Institute
Auckland, New Zealand|Gisborne, New Zealand
Auckland Bioengineering Institute, University of Auckland|Mātai Medical Research Institute
Auckland, New Zealand|Gisborne, New Zealand
Paul Condron
Mātai Medical Research Institute|Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland
Gisborne, New Zealand|Auckland, New Zealand
Mātai Medical Research Institute|Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland
Gisborne, New Zealand|Auckland, New Zealand
Samantha Holdsworth
Mātai Medical Research Institute|Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland
Gisborne, New Zealand|Auckland, New Zealand
Mātai Medical Research Institute|Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland
Gisborne, New Zealand|Auckland, New Zealand
Justin Fernandez
Auckland Bioengineering Institute, University of Auckland|Mātai Medical Research Institute
Auckland, New Zealand|Gisborne, New Zealand
Auckland Bioengineering Institute, University of Auckland|Mātai Medical Research Institute
Auckland, New Zealand|Gisborne, New Zealand
Miriam Scadeng
Mātai Medical Research Institute|Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland
Gisborne, New Zealand|Auckland, New Zealand
Mātai Medical Research Institute|Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland
Gisborne, New Zealand|Auckland, New Zealand
Alan Wang
Auckland Bioengineering Institute, University of Auckland|Mātai Medical Research Institute|Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland
Auckland, New Zealand|Gisborne, New Zealand|Auckland, New Zealand
Auckland Bioengineering Institute, University of Auckland|Mātai Medical Research Institute|Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland
Auckland, New Zealand|Gisborne, New Zealand|Auckland, New Zealand
Vickie Shim, PhD
Auckland Bioengineering Institute, University of Auckland|Mātai Medical Research Institute
Auckland, New Zealand|Gisborne, New Zealand
Auckland Bioengineering Institute, University of Auckland|Mātai Medical Research Institute
Auckland, New Zealand|Gisborne, New Zealand
Introduction:
Exposure to head acceleration events(HAE) and incidence of mild traumatic brain injury(mTBI) are common during collision sports participation. Effects of mTBI and/or HAE exposure on short- and long-term brain health are not well understood. Network connectivity analysis and diffusion MRI(dMRI) allows the measure of physical connection strength between brain regions and is correlated to underlying white matter fiber(Lazar, 2003). We aim to characterize effects of HAE exposure through a longitudinal neuroimaging study in rugby players and compare their scans after a season to their baseline, to controls, and athletes diagnosed with mTBI.
Methods:
Thirty-three male high school rugby players underwent brain scans using a 3.0T MRI Scanner. T1-W(0.5mm3 isotropic voxel size) and multi-shell dMRI(b-values(gradient direction)=0,1000,2000,3000 (4,15,15,20);2mm3 isotropic voxel size) were acquired at pre-, mid-, and postseason. Sixteen rugby players with in-season mTBI were scanned within 7-10 days post-injury. 20 matched neurologically healthy non-contact sports athletes underwent a single time-point scan as controls. T1 images were parcellated into 84 regions of the Desikan-Killiany atlas using FreeSurfer(Fischl, 2000). dMRI were preprocessed using FSL(Anderson, 2003). Anatomically Constrained Tractography was performed using Mrtrix3(Tournier, 2019). An adjacency matrix with streamline count between ROIs of the parcellation image was produced.
Graph theory measures were computed using 'NetAnalyseR'(Clarkson, 2024), an R script translation of the Brain Connectivity Toolbox(Rubinov, 2010). Global network metrics were quantified using global efficiency, clustering coefficient, and network density, and network topology metrics using local clustering coefficient, local efficiency, and strength. Streamline count between cortical and subcortical regions were analyzed. ANOVA tests assessed intragroup(pre-, mid-, postseason) and inter-group(control, pre-, post-, mTBI) differences. Post-hoc comparison identified pairwise difference(p<0.05) and p-value was adjusted based on false discovery rate(Benjamini, 1995).
Graph theory measures were computed using 'NetAnalyseR'(Clarkson, 2024), an R script translation of the Brain Connectivity Toolbox(Rubinov, 2010). Global network metrics were quantified using global efficiency, clustering coefficient, and network density, and network topology metrics using local clustering coefficient, local efficiency, and strength. Streamline count between cortical and subcortical regions were analyzed. ANOVA tests assessed intragroup(pre-, mid-, postseason) and inter-group(control, pre-, post-, mTBI) differences. Post-hoc comparison identified pairwise difference(p<0.05) and p-value was adjusted based on false discovery rate(Benjamini, 1995).
Results:
Compared to controls, post-hoc comparison identified significantly higher global efficiency, global clustering coefficient, and inter-nodal connection in mTBI subjects and rugby players. In contrast, rugby players and mTBI subjects had significantly lower intra-nodal connection than controls. Total network connectivity was stratified by regions the streamlines connect. Compared to controls, post-hoc analysis revealed significantly higher cortical-cortical connection in mTBI subjects, and significantly higher subcortical-cortical connection in rugby players(Figure 1). Local clustering coefficient and local efficiency, compared to controls, were significantly higher for rugby players in the left superior frontal cortex, and significantly higher for postseason in the right precentral cortex(Figure 2). No other significant comparisons were found.
Conclusions:
Playing a season of rugby showed no significant changes in network metrics, but differences were found compared to controls. Nodal analysis revealed significantly higher efficiency in cortical areas associated with higher-order cognitive functions and motor planning. This enhanced efficiency adds evidence to the effect of high-performance sports participation to athlete's brain in areas implicated by motor planning, rapid decision-making, and strategic thinking(Schlaffke, 2014).
Differences found between control and rugby players suggest that structural connectivity metrics change on global, topological and nodal level are possibly attributable to HAE exposure during collision sport participation. This is potentially driven by aerobic exercise intensity difference(Herting, 2014). To our knowledge, no other study compared structural connectivity between contact and non-contact sport athletes. Further studies are needed to characterize physiological and clinical implications of identified differences.
Differences found between control and rugby players suggest that structural connectivity metrics change on global, topological and nodal level are possibly attributable to HAE exposure during collision sport participation. This is potentially driven by aerobic exercise intensity difference(Herting, 2014). To our knowledge, no other study compared structural connectivity between contact and non-contact sport athletes. Further studies are needed to characterize physiological and clinical implications of identified differences.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Diffusion MRI Modeling and Analysis 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Novel Imaging Acquisition Methods:
Anatomical MRI
Diffusion MRI
Keywords:
STRUCTURAL MRI
Tractography
Trauma
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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