The acute effects of non-concussive head impacts in sport: A randomised control trial.

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Nathan Delang, Rebecca V. Robertson, Fernando A. Tinoco Mendoza, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4765251/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jun, 2025 Read the published version in Sports Medicine-Open → Version 1 posted 5 You are reading this latest preprint version Abstract Background Head impacts, particularly, non-concussive impacts, are common in sport. Yet, their effects on the brain are poorly understood. Here, we investigated the acute effects of non-concussive impacts on brain microstructure, chemistry, and function using magnetic resonance imaging (MRI) and other techniques. Results Fifteen healthy male soccer players completed this randomised, controlled, crossover trial. Participants completed a soccer heading task (‘Heading’; the Intervention) and an equivalent ‘Kicking’ task (the Control); followed by a series of MRI sequences between ~ 60–120 minutes post-tasks. Blood was also sampled, and cognitive function assessed, pre-, post-, 2.5 hours post-, and 24 hours post-tasks. Brain chemistry: Heading increased total N -acetylaspartate ( p = 0.012) and total creatine ( p = 0.010) levels in the primary motor cortex (but not the dorsolateral prefrontal cortex) as assessed via proton magnetic resonance spectroscopy. Glutamate-glutamine, myoinositol, and total choline levels were not altered in either region. Brain structure: Heading had no effect on diffusion weighted imaging metrics. However, two blood biomarkers expressed in brain microstructures, glial fibrillary acidic protein and neurofilament light, were elevated 24 hours ( p = 0.014) and ~ 7-days ( p = 0.046) post-Heading ( vs . Kicking), respectively. Brain function: Heading decreased tissue conductivity in five brain regions ( p ’s < 0.001) as assessed via electrical properties tomography. However, no differences were identified in: (1) connectivity within major brain networks as assessed via resting-state functional MRI; (2) cerebral blood flow as assessed via pseudo continuous arterial spin labelling; (3) electroencephalography frequencies; or (4) cognitive (memory) function. Conclusions This study identified chemical, microstructural and functional brain alterations in response to an acute non-concussive soccer heading task. These alterations appear to be subtle, with some only detected in specific regions, and no corresponding functional deficits (e.g., cognitive, adverse symptoms) observed. Nevertheless, our findings emphasise the importance of exercising caution when performing repeated non-concussive head impacts in sport. Trial registration ACTRN12621001355864. Date of registration 7/10/2021. URL https//www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=382590&isReview=true Subconcussion contact sport neural astroglial metabolites Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key Points The effects of non-concussive head impacts on the brain are poorly understood. Employing a variety of magnetic resonance imaging sequences after controlled head impacts can interrogate parameters that have not been investigated in previous research. This study observed acute alterations to select chemical, functional and microstructural parameters in the absence of overt cognitive deficits or reported symptoms, after participants completed a controlled soccer heading task. These findings highlight the ‘silent’ physiological changes that can occur after non-concussive head impacts and emphasise the importance of exercising caution when performing repeated head impacts in sport. 1.0 Background Athletes participating in contact/collision sports such as American football, rugby and association football (soccer) can sustain hundreds of head impacts each sporting season [ 1 ]. High strength, unanticipated and/or poorly located head impacts can cause concussion , a type of mild traumatic brain injury (mTBI) that is accompanied by a host of unpleasant symptoms (e.g., headache, blurred vision, nausea) [ 2 ]. Head impacts that do not elicit clinical signs or symptoms are termed ‘ subconcussive ’, or increasingly ‘ non-concussive’ , impacts [ 1 , 3 ]. These impacts are exceedingly common, accounting for > 99% of all head impacts incurred in sport [ 4 , 5 ]. They also have the potential to cause long-term harm, with some evidence suggesting that chronic traumatic encephalopathy (an incurable neurodegenerative disease related to head trauma [ 6 ]) can develop in the presence of non-concussive impacts exclusively [ 7 ]. Despite this, the effects of non-concussive impacts on the brain remain poorly understood [ 8 ]. Several observational studies have investigated the microstructural and physiological sequelae of non-concussive impacts in sport; specifically, those incurred over the course of a sporting season [ 9 – 12 ]. These studies have employed various assessment techniques, including central methods (e.g., magnetic resonance imaging [MRI] sequences such as diffusion weighted imaging [DWI], magnetic resonance spectroscopy [MRS] and functional MRI [fMRI]), peripheral methods (e.g., blood biomarkers), and functional tests (e.g., cognitive tasks) [ 8 , 13 – 15 ]. Recent systematic reviews summarising their findings indicate that non-concussive impacts have the potential to alter brain microstructure, chemistry and function [ 8 , 13 – 15 ]. However, results are inconsistent [ 8 , 13 – 15 ]. It is also noted that observational studies have inherent limitations (e.g., confounders, biases) and cannot establish causation. Interventional studies (particularly, randomised controlled trials [RCTs]) are increasingly being used to investigate the effects of non-concussive impacts on the brain [ 16 ]. When using these designs, impacts are administered in the form of a controlled non-concussive soccer heading task (SHT) [ 16 ]. Studies employing a SHT have shown that non-concussive impacts can elicit significant alterations in neuroelectric (via electroencephalography [EEG]) [ 17 ], cognitive [ 18 , 19 ], neurovascular [ 20 ], neuroophthalmologic [ 21 , 22 ], and vestibular [ 23 , 24 ], function in healthy soccer athletes. SHTs have also been reported to increase blood concentrations of neurofilament light (Nf-L; a biomarker of axonal pathology) [ 25 , 26 ], glial fibrillary acidic protein (GFAP; a biomarker of astrocyte pathology) [ 27 ], and certain inflammatory markers [ 28 ]. However, no interventional studies have investigated the microstructural and physiological effects of non-concussive impacts in sport using MRI techniques. This is important, as MRI has the capacity to interrogate regional chemical, functional and microstructural parameters that have not previously been investigated and allow researchers to predict the functional consequences of any changes observed. The primary aim of this study was to investigate the acute effects of non-concussive impacts, administered in the form of a controlled SHT, on brain microstructure, function and chemistry using MRI techniques. It was hypothesised that non-concussive impacts would result in unfavourable changes to these parameters, as per previous observational studies.[ 8 , 13 , 15 ] The secondary aim was to investigate the effects of non-concussive impacts on neuroelectric activity (via EEG), cognitive function and blood biomarkers of neuronal and astroglial damage (i.e., Nf-L, GFAP) and inflammation (e.g., interleukin [IL]-6, etc). 2.0 Methods 2.1 Study Design A randomised, controlled, crossover trial was conducted at Neuroscience Research Australia (NeuRA; Randwick, NSW). The trial was approved by the University of Sydney’s Human Research Ethics Committee (2021/515) and registered prospectively with the Australian New Zealand Clinical Trials Registry (ACTRN12621001355864). All research was completed in accordance with the Declaration of Helsinki. 2.2 Participants Healthy individuals aged between 18–35 years and with ≥ 5 years of soccer heading experience were recruited. The full eligibility criteria are presented in Figure S1 . Briefly, the key exclusion criteria were: (1) a head, neck, face or eye injury (including a confirmed or suspected concussion) within the last 12 months; (2) an uncontrolled physical or mental health condition; (3) a neurological disorder; (4) a contraindication to MRI; or (5) pregnant or lactating. 2.3 Enrolment Each volunteer completed a face-to-face screen with the trial coordinator (N.D.) and physician (K.R.). Here, they were informed about the nature and risks of experimental procedures, before providing written informed consent and being assessed for eligibility. Eligible participants were familiarised with the cognitive function tasks (Section 2.7.4 Cognitive Function Acquisition) and asked to provide demographic information (including an indication of ‘usual’ [non-specific] concussion symptoms as per the Concussion Recognition Tool [CRT]-5) [ 29 ]. 2.4 Randomisation and Allocation Concealment Participants were randomised to one of two possible treatment orders in a 1:1 ratio at the beginning of their first test session. Specifically, they were assigned a unique identification code (by N.D.) that was linked to a treatment order via a pre-populated randomisation schedule. The schedule was generated in a series of balanced blocks (and one ‘block’ of one) by an investigator (E.C.) using an online random number generator ( https://www.sealedenvelope.com/simple-randomiser/v1/lists ). The schedule could only be accessed by the investigator and one other researcher (P.A.), neither of whom had contact with participants. The balanced blocks also varied in size so that the final treatment order within each block could not be predicted. Treatment allocation was then concealed using sealed, opaque envelopes. 2.5 Treatments Treatments were administered by the trial coordinator (N.D.) and a second investigator (D.M.) on the outdoor fields of Paine Reserve (Randwick, NSW; ~500 m from NeuRA). 2.5.1 Intervention (‘Heading’ Task) The intervention was a SHT (‘Heading’). A JUGS Soccer Machine™ (JUGS® Australia, Cheltenham, Victoria, Australia) was used to launch FIFA regulation size 5 soccer balls at a speed of 35 km⋅h − 1 . Participants performed 20 headers in 20 minutes from ~ 12 meters to the JUGS. They were instructed to hit the ball with their forehead and to direct it back towards the JUGS. Unsuccessful headers (i.e., where there was no contact between the head and the ball) were re-administered. 2.5.2 Control (‘Kicking’ Task) The control was a soccer kicking task (‘Kicking’). It was administered exactly as the intervention, except that participants kicked (rather than headed) the ball (which was launched along the ground). 2.6 Treatment Sessions Participants completed two treatment sessions, Heading or Kicking, separated by ≥ 7 days. 2.6.1 Standardisation Procedures Prior to each treatment session, participants were instructed to: (1) avoid soccer heading and playing other contact sports (> 7 days); (2) avoid using alcohol (> 24 hours), caffeine (> 12 hours), anti-inflammatory medication (> 4 days) and central nervous system (CNS) active drugs (> 7 days); (3) avoid moderate to strenuous exercise (> 12 hours); (4) spend > 8 hours in bed overnight; (5) consume a standardised breakfast (at home) and (6) consume 500 mL of water before arriving at the clinic. 2.6.2 Experimental Procedures Experimental procedures are summarised in Fig. 1 . Briefly, participants arrived at NeuRA between ~ 7:30–8:30 AM and verbally acknowledged compliance to the standardisation procedures. A urine sample was collected to confirm avoidance of CNS active drugs (DrugCheck® NxStep Onsite Urine Drug Test) and to assess hydration status (urine specific gravity [U SG ]; Palette Digital Refractometer, ATAGO, USA). If U SG was > 1.024, likely indicating hypohydration [ 30 ], participants consumed 500 mL of water [ 31 ]. Participants then completed a series of baseline assessments (‘Pre’; Section 2.7 Data Collection), before they were walked to the outdoor field to receive their assigned treatment (i.e., Heading or Kicking). Following treatment, participants returned to NeuRA to complete a series of post-treatment assessments (‘Post’ and ‘2.5 hrs Post’). They left between 12:30 − 1:30 PM but returned the following day to complete their 24-hour post-treatment assessments (‘24 hrs Post’). Participants were instructed to adhere to the same standardisation procedures ahead of this visit. Insert Fig. 1 approximately here 2.7 Data Collection 2.7.1. MRI Acquisition (Primary Outcome) MRI commenced ~ 60 minutes post-treatment and took ~ 60 minutes to complete. The timing of this assessment was selected with consideration for pragmatic factors (e.g., participant transportation) and prior research suggesting that SHTs can elicit immediate alterations in neurovascular and corticomotor function [ 18 , 20 ]. All images were collected by a registered radiographer using a 3T MRI scanner (Ingenia CX, Philips) with a 32-channel head coil. Participants were placed supine into the MRI scanner with their head secured in a tight-fitting head coil with headphones to prevent movement. Images were collected in the following order (time of acquisition post-treatment provided in mean ± SD): (1) T 1 -weighted anatomical (+ 68 ± 6 mins); (2) proton MRS ( 1 H-MRS; +78 ± 7 mins); (3) electrical properties tomography (EPT; +94 ± 7 mins); (4) blood-oxygen-level-dependent (BOLD) resting-state fMRI (rs-fMRI; +102 ± 12 mins); (5) pseudo continuous arterial spin labelling (pCASL; +111 ± 8 mins); and (5) DWI (+ 117 ± 8 mins). Scans were conducted to measure brain chemistry ( 1 H-MRS), function (EPT, rs-fMRI and pCASL) and microstructure (DWI). Participants were instructed to remain awake and focused on a crosshair (displayed on a screen) throughout functional scans. T1-weighted anatomical : A high-resolution 3-dimensional anatomical image set covering the entire brain was acquired for accurate image registration and segmentation (211 sagittal slices; repetition time [TR]/echo time [TE] = 7.3/3.4 ms; flip angle = 8°; slice thickness = 0.9 mm; voxel size = 0.75x0.75x0.9 mm). 1 H-MRS : Single voxel 1 H-MRS was collected from two brain regions: the left dorsolateral prefrontal cortex (dlPFC) and primary motor cortex (M1) in the somatotopic region representing the dominant foot, as these regions have demonstrated neurometabolic alterations in previous observational studies of non-concussive impacts [ 32 – 34 ]. Data from 1 H-MRS were collected using a semiadiabatic Localization by Adiabatic SElective Refocusing (sLASER) sequence (VAriable Power and Optimized Relaxations [VAPOR] water suppression; 64 averages; 2048 data points; TE = 31 ms for dlPFC and 33 ms for M1, TR = 5000 ms; voxel size = 15 mm 3 ). Second order shimming was conducted using the auto-shimming function with the vendor-supplied (Phillips) sLASER sequence; only spectra with full width at half maximum (FWHM) values less than 15 Hz were accepted (otherwise scans were repeated). EPT : Scans were acquired using a balanced fast field echo (bFFE) sequence (TR/TE = 2.54/1.27 ms; flip angle = 25°; nonselective radiofrequency [RF] pulses; compressed SENSE factor 1; RF shimming calibrated with full coverage 2D dual refocusing echo acquisition mode [DREAM]; voxel size = 1 mm 3 ). rs-fMRI : A rs-fMRI series consisting of 250 whole brain BOLD fMRI image volumes was collected (TR/TE = 1500/30 ms; 75 axial slices; voxel size = 2 mm 3 ). pCASL : A resting pCASL series covering the entire brain was acquired (TR/TE = 4188/10.7 ms; 24 axial slices; voxel size = 3x3x6 mm; 384 images). Four background suppression pulses were applied to maximise the sensitivity to blood perfusion [ 35 ]. DWI : A DWI set covering the entire brain was acquired using a single-shot multi-section spin-echo echo-planar pulse sequence (TR/TE = 3000/75 ms; flip angle = 90°; 57 axial slices; voxel size = 2.5 mm 3 ). For each slice, diffusion gradients were applied along 32 phase-encoding directions at b-value = 1000 s/mm 2 , 64 phase-encoding directions at b-value = 3000 s/mm 2 , and one volume acquired at b-value = 0 s/mm 2 . Anatomical and diffusion image sets were visually inspected for artifacts; no participants were excluded from the analysis. 2.7.2 EEG Acquisition A 15-minute resting EEG recording was acquired ~ 2 hours post-treatment using a 64-channel EEG system (ANT Neuro, Netherlands). Electrodes were placed according to the standard 10–20 system [ 36 ], with reference electrodes placed on opposing mastoid processes, and an electrode placed on the orbicularis oculi muscle to monitor eye movements. Participants were tested while seated in a quiet room and instructed to relax, close their eyes and let their mind wander. Continuous EEG data were acquired at a sampling rate of 1000 Hz with online band-pass filtered between 0.01 and 100 Hz. 2.7.3 Blood Acquisition Blood was collected into a 6.0 mL pre-treated EDTA vacutainer and 3.5 mL serum vacutainer at Pre, Post, 2.5 hrs Post and 24 hrs Post. Each vacutainer was centrifuged for 15 minutes at 1500 g and 4˚C within 30 minutes of collection (following coagulation of the serum sample), with plasma and serum stored at − 80˚C until analysis. 2.7.4 Cognitive Function Acquisition Cognitive function was assessed at Pre, Post, 2.5 hrs Post and 24 hrs Post using two computerised tasks from the Cambridge Neuropsychological Test Automated Battery (CANTAB): the Paired Associate Learning (PAL; ~8 minutes duration) and Spatial Working Memory (SWM; ~4 minutes duration) tasks [ 37 , 38 ]. These tasks have demonstrated sensitivity to the effects of SHTs [ 18 ]. Participants completed the tasks in a quiet room and were instructed to take their time and minimise errors. 2.8 Data Processing and Analysis 2.8.1 MRI Processing and Analysis 1 H-MRS : All analysis specifics, including visualisation of voxel placement and sample spectra, are presented in Table S1 (MRSinMRS Acquisition and Analysis Checklist) [ 39 ]. The spectrum and unsuppressed water spectrum for each participant were analysed by N.D. (unblinded) using Totally Automatic Robust Quantitation in NMR (TARQUIN; v4.3.10) [ 40 ]. Pre-processing consisted of eddy current correction, lipid filtering, automatic referencing water residual removal using Hankel singular value decomposition, zero-order phase correction, and automatic referencing using zero filling. The neurometabolites of interest were: total N -acetylaspatate (tNAA; NAA + N acetyl glutamate), myo-inositol (mI), total choline (tCho; choline-containing compounds), total creatine (tCr; creatine + phosphocreatine), and glutamate/glutamine (Glx). Neurometabolites were analysed and reported as water-referenced levels (using the default TARQUIN processing) rather than using internal neurometabolite references (e.g., tCr), as several neurometabolites including tCr may be influenced by non-concussive impacts [ 15 ]. The quality of all spectra were examined using the line width/FWHM of fitted spectra and signal-to-noise ratio (SNR) using TARQUIN’s default processing. Data were excluded if FWHM > 15 Hz or SNR was < 5 (no data were discarded on this basis; Table S1 ). In addition, the accuracy of voxel placement was visually inspected through heat maps. Data from poorly placed voxels were discarded. Tissue parcellation (grey and white matter) within each voxel was reported (Table S1 ). EPT : Data were processed by J.C. (blinded to treatment) to produce conductivity maps according to methods described by Cao and colleagues [ 41 ]. In brief, T1-weighted turbo field echo images were co-registered and segmented into white matter, grey matter and cerebrospinal fluid using FSL [ 42 ], to alleviate boundary artifacts. Within each tissue type, an average parabolic phase fitting method was used to reduce artifacts amplified in the Laplacian [ 43 ], and the second derivatives of the fitted phase were taken to calculate conductivity. The conductivity maps of each participant from both sessions were normalised into Montreal Neurological Institute (MNI) space (voxel size 2 mm isotropic) using statistical parametric mapping (SPM) 12 [ 44 ]. rs-fMRI : Using SPM 12 [ 44 ], and custom software, fMRI images were processed by N.D. (unblinded). Images were slice-time and motion corrected, and global signal drifts removed using the detrending method described by Macey and colleagues [ 45 ]. Physiological noise was corrected (cardiac frequency band 60–120 beats per minute + 1 harmonic; respiratory frequency band 8–25 breaths per minute + 1 harmonic) using the DRIFTER toolbox [ 46 ], and the six-parameter movement-related signal changes modelled and removed using a linear modelling of realignment parameters procedure [ 45 ]. The fMRI images were then co-registered to participant’s T1 anatomical image, the T1 image then spatially normalised to the MNI template and normalisation parameters applied to the fMRI images. The fMRI images were then spatially smoothed using a 6 mm FWHM Gaussian filter. Independent components analysis (ICA) was performed using the Group ICA toolbox [ 47 ] to define major brain networks [ 48 – 50 ]. Thirty independent components were extracted using the Infomax ICA algorithm [ 51 ], and major networks identified by visual inspection. We selected nine components from six major brain networks: the salience, sensorimotor, visual, default mode, cerebellar and executive control networks (Figure S2 ). pCASL : Using SPM 12 [ 44 ], pCASL data were analysed by N.D. (unblinded). All pCASL sets were realigned, co-registered to each participant’s source image, and a mean cerebral blood flow (CBF) map created using the subtraction method from the ASL toolbox [ 52 ]. Each participant’s source images were spatially normalised to MNI space and the parameters applied to the CBF maps. The CBF maps were smoothed using a 6 mm FWHM Gaussian filter. DWI : During acquisition, a coding error occurred that corrupted the acquisitions with diffusion gradients at b-value = 1000 s/mm 2 . Consequently, b-1000 DWI were removed from the image set. Using SPM12 [ 44 ], the remaining images were processed by N.J. (blinded to treatment). Images were corrected for motion, eddy current and b0 distortion. Elements of the diffusion tensor were computed from the images using a linear model, then fractional anisotropy (FA) and mean diffusivity (MD) whole-brain maps were derived. The FA and MD maps were resliced into 1.5 mm isotropic voxel sizes and co-registered to each individual’s T1-weighted anatomical image to ensure all images were in the same three-dimensional space. Subsequently, they were spatially normalised to MNI space using the previously calculated parameters from T1 images and spatially smoothed using a 5 mm FWHM Gaussian filter. In addition, a fixel-based analysis (FBA) was conducted using MRtrix3 [ 53 ], to determine tract-specific quantities of fibre density (FD), fibre cross section (FC) and a combination of both (FDC). Data were processed by M.G. (blinded to treatment) according to previous published methods [ 54 ]. 2.8.2 EEG Processing and Analysis Processing of EEG data were performed in Matlab (Version R2020b; MathWorks, Inc., Natick, MA, USA) and the FieldTrip toolbox by N.D. (unblinded) [ 55 ]. Prior to processing, data were bandpass filtered between 0.01 and 35 Hz. Initially, large artefacts and poor-quality channels were identified via visual inspection and removed from the data. Following this, an ICA was conducted to remove typical eye artefacts (e.g., blinks and saccades). Poor quality channels were reconstructed via interpolation from neighbouring channels. Finally, the EEG signals were re-referenced to the average of the mastoid electrodes and down sampled to 200 Hz to enhance processing speed. Estimates of cortical power were produced using the fast Fourier transform, at the following frequencies: 0.02–0.09 (at steps of 0.01 Hz), 0.1–0.9 (at steps of 0.1 Hz), and 1–30 (at steps of 1 Hz). The cortical power at each frequency was computed by averaging the power across all EEG channels. Four frequency bands were included for analysis: infra-slow (0.03–0.06 Hz), theta (4–8 Hz), alpha (9–12 Hz), and beta (13–25 Hz). 2.8.3 Blood Biomarkers Analysis Nf-L and GFAP : Plasma samples were analysed using a Simoa HD-X Analyzer (Quanterix, Lexington, MA) using commercially available Simoa kits as per manufacturer’s instructions [ 56 ]. Samples were tested in duplicate by a scientist (W.O.) blinded to treatment. GFAP Discovery assays (Item 102336) were used to quantify GFAP, with participant samples analysed on the same plate, and all samples measuring above the lower limit of quantification (LLOQ; 0.686 pg/mL). For Nf-L, NF-Light V2 Advantage assays (Item 104073) were used, with all Pre and 24 hrs Post samples from the same participant were analysed on the same plate, and the remaining samples analysed separately later (once additional funding was sourced). All samples measured above the LLOQ for Nf-L (1.38 pg/mL). Inflammatory Markers : Serum samples were analysed by a Contract Research Organisation (Eve Technologies, Calgary, AB, Canada). The Human Cytokine 15-Plex Assay Array was performed to determine concentrations of: granulocyte-macrophage colony-stimulating factor, interferon gamma, IL-1β, IL-1RA, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p40, IL-12p70, IL-13, monocyte chemoattractant protein-1 (MCP-1), and tumour necrosis factor-α. The analyses were performed in duplicate by a laboratory technician blinded to treatment. Only Pre and 24 hrs Post samples were analysed due to funding constraints. 2.8.4 Cognitive Function Outcomes The PAL task measures visual memory and learning [ 37 ]. The outcome measures were: number of attempts required to complete the task (‘attempts’) and number of errors made (adjusted for attempts if the participant did not complete the task; ‘adjusted errors’). The SWM task measures working memory, executive functions, and strategy [ 38 ]. The outcome measures were: number of errors (‘errors’) and a strategy score (calculated based on the randomness of participants’ opening boxes, where lower scores indicated better strategy; ‘strategy’). 2.8.5 Treatment Characteristics An impact monitoring mouthguard (Prevent Biometrics™, Edina, MN, USA) was used to measure linear and rotational acceleration of head impacts (peak linear acceleration [PLA]; peak rotational acceleration [PRA]). This device has demonstrated a high degree of accuracy in controlled and field environments (concordance correlation coefficient > 0.8) [ 57 ]. Participants were also asked to rate how ‘well’ they performed each header on an 11-point scale (-5=‘very poorly’; to + 5=‘very well’) and the ‘strength’ of each header on a 5-point scale (1=‘very low’; to 5=‘very high’). Mean heart rate (HR) throughout the 20-minute activity was determined using a chest strap monitor (Polar H10 HR Sensor). 2.8.6 Adverse Event Monitoring Participants were monitored for signs of concussion (adverse event [AE]) using Parts 1–3 of the CRT-5 [ 29 ], at Post, 2.5 hrs Post, 4–8 hrs Post and 24 hrs Post Task (Fig. 1 ). Parts 1 and 2 were used to identify ‘red flag’ and ‘observable sign(s)’ of concussion. Part 3 was used to identify possible (non-specific) ‘symptoms’ of concussion. Participants answered ‘yes’, ‘no’ or ‘maybe’ to the ‘red flag(s)’, ‘observable sign(s)’ and potential ‘symptoms’. Responses were documented, reviewed and escalated to the trial physician, as necessary. 2.9 Sample Size A target sample size of 15 was selected with consideration of practical factors such as time, cost, and resource allocation, rather than formal power analysis. This pragmatic approach reflects the current lack of interventional studies investigating the acute effects of non-concussive impacts on brain structure, function and chemistry using MRI. 2.10 Statistical Analyses The EPT, fMRI, ASL and DWI data were analysed using Matlab (Version R2023b; MathWorks, Inc., Natick, MA, USA) and EEG data using Matlab (Version R2020b). The remaining data were analysed using R (Version 4.2.2) [ 58 ]. 2.10.1 Electrical Properties Tomography (EPT), Resting-State Functional Magnetic Resonance Imaging (rs-fMRI), Arterial Spin Labelling (ASL) and Diffusion Weighted Imaging (DWI) Second level, random effects, paired analyses were conducted to determine significant differences at a voxel-by-voxel level ( p < 0.05, false discovery rate [FDR] corrected, minimum cluster = 10 contiguous voxels). For the rs-fMRI network analyses, each analysis was restricted by creating a mask of the relevant network using both treatments’ scan images ( p < 0.05, FDR corrected). Significant differences for all MRI scans were then overlaid onto a mean T1-weighted anatomical image set. 2.10.2 Diffusion Weighted Imaging (DWI) Fixel-Based Analysis A general liner model was fitted to every fixel to compare between treatments for all metrics (FD, FC, FDC). A whole brain tractogram consisting of two million streamlines was used for statistical inference using connectivity-based fixel enhancement [ 54 ]. Data were analysed between treatment using non-parametric permutation testing (5000 permutations; p < 0.05, family wise-error [FWE] corrected). 2.10.3 Electroencephalography (EEG) Global cortical power of each frequency band (i.e., infraslow, theta, alpha and beta) between treatments were compared using paired t-tests with significance set a p < 0.05. In addition, to identify a group of channels where significant differences existed, the spatial distribution of power differences between treatments and within infraslow, theta, alpha and beta bands, were examined using cluster-based permutation tests (4000 permutations; p < 0.05, corrected using the ‘cluster’ function) [ 59 ]. 2.10.4 Other Data Continuous variables (neurometabolites [ 1 H-MRS], blood biomarkers, treatment characteristics) were analysed using linear mixed-effects models and the ‘lme4’ and ‘emmeans’ packages [ 60 , 61 ]. The models included Treatment, Time, and Treatment \(\:\times\:\) Time interaction as fixed effects and had a random intercept (Participant) and slope (Treatment), as appropriate. The models were generated using the restricted maximum likelihood criterion and no covariance structure was specified (unstructured). If the residuals were non-normally distributed (Shapiro–Wilk test, p < 0.05) or heteroskedastic (Levene test, p < 0.05), the data were square-root transformed (and if unimproved, log-transformed) and reanalysed. If an appropriate model could not be generated, the ‘best’ of those described above (i.e., simplest model violating the fewest assumptions) was utilised. Note: As plasma Nf-L concentrations can remain elevated for 22 days following SHTs [ 25 ], a separate analysis was conducted, using Session (1 or 2), Treatment Order (Heading–Kicking or Kicking–Heading) and Session \(\:\times\:\) Treatment Order interaction as a fixed effects and a random intercept (Participant) and slope (Treatment Order). Count variables (cognitive function) were analysed using generalised linear mixed-effects models and the ‘glmmTMB’ and ‘emmeans’ packages [ 61 , 62 ]. These models included the same fixed and random effects structure as above, and were fitted to a Poisson distribution, unless over-dispersed, and/or zero-inflated. In these instances, a negative binomial, zero-inflated Poisson, or zero-inflated negative binomial distribution was substituted, respectively (with both parts of the zero-inflated models containing the same fixed effects). Two-sided pairwise comparisons were used to compare estimated marginal means across Treatment and/or Time if a significant main effect of Treatment, Time, or a Treatment \(\:\times\:\) Time interaction (or equivalent for Nf-L) was observed. Data were presented as mean ± SD unless non-normally distributed (in which case, data were presented as Median [IQR]). Statistical significance was accepted as p < 0.05 (Dunn–Šidák-corrected) and effect sizes were calculated as Hedges’ g [ 63 ]. 3.0 Results 3.1 Participant and Treatment Characteristics Recruitment commenced in November 2021 and concluded 12 months later. Eighteen volunteers signed informed consent and 15 were randomised (Fig. 2 ). Of those randomised, 14 received both treatments (i.e., as intended) and one received one treatment (i.e., Heading only) after being unable to complete the second session for personal reasons. All 15 participants were included in the final sample (except where the analytical technique employed could not handle missing data) (Table 1 ). Participants completed their sessions between seven and 25 days apart (9 ± 4 days). Note that due to difficulties with recruitment, only male participants completed this trial. Table 1 Participant Characteristics ID Age (years) BMI (kg/m 2 ) Dominant Foot Predominant playing position Number of headers in last 12 months a Number of years heading experience Number of previous concussions Length of time since last concussion (years) Time of season b Days between trials 1 24 28.1 Right Centre Midfield 528 18 0 NA Pre-Season 25 c 2 22 23.6 Right Centre Attacking Midfield 928 14 0 NA Pre-Season 7 3 25 24.2 Right Centre Back 160 13 0 NA In-Season 7 4 29 24.0 Right Centre Defending Midfield 192 20 0 NA In-Season 7 5 18 19.0 Right Wide Back 336 9 0 NA Off-Season 8 6 34 28.4 Right Centre Defensive Midfield 1520 28 2 17 In-Season 7 7 20 23.3 Right Wing 144 8 1 8 In-Season 8 8 20 29.7 Right Centre Back 96 8 2 4 In-Season 7 9 27 25.6 Right Centre Back 1760 14 0 NA Post-Season 8 10 22 24.0 Right Centre Back 0 5 0 NA Off-Season 8 11 32 28.2 Right Centre Back 540 25 0 NA Post-Season 7 12 20 25.1 Right Striker 1248 8 0 NA Post-Season NA d 13 29 25.8 Left Centre Back 832 23 2 5 Post-Season 8 14 27 24.3 Right Centre Midfield 12 19 0 NA Off-Season 8 15 27 24.3 Right Centre Back 204 20 5 4 Off-Season 9 Mean ± SD 25 ± 5 25.2 ± 2.7 567 ± 549 16 ± 7 9 ± 4 a The number of head impacts in the last 12 months was subjectively assessed via a standardised questionnaire. 27 b Pre-Season: (Scheduled training prior to In-Season but after Off-Season - possible infrequent games); In-Season (Scheduled training and frequent competitive games [at least weekly]); Post-Season (No/minimal scheduled training and between In-Season and Off-Season - possible infrequent games); Off-Season (No scheduled training/games) c Participant had longer than anticipated time between trials due to being diagnosed with SARS-CoV-2 between Trial 1 and 2. d Participant did not complete Trial 2 due to family reasons. Insert Fig. 2 approximately here Insert Table 1 approximately here Treatment characteristics are summarised in Table S2 . The average PLA and PRA of headers was 15.8 ± 5.6 g and 1271 ± 602 rad/s 2 , respectively. No head impacts were recorded on Kicking. Participants rated the strength of headers as 3 (IQR: 2–4) on a 5-point scale and how well they performed each header as 1 (IQR: -1–3) on a -5 to 5 scale. Mean HR tended to be higher during Heading than Kicking (81 ± 12; 79 ± 12 bpm, p = 0.081, g = 0.159). 3.2 Magnetic Resonance Imaging (MRI) Only the 14 participants who received both treatments could be included in the MRI analyses, except for the 1 H-MRS analysis (details below). 3.2.1 Proton Magnetic Resonance Spectroscopy ( 1 H-MRS) Fifteen participants were included in the dlPFC analyses. Only 14 were included in the M1 analyses due to inaccurate voxel placement (on both sessions; ID: 1). Heading increased tNAA ( p = 0.012, g = 0.593) and tCr ( p = 0.010, g = 0.702) levels in the M1 compared to Kicking. No other significant differences were observed in either region (Table 2 ; all p ’s > 0.05). Table 2 Neurometabolite levels between trials assessed using Magnetic Resonance Spectroscopy ROI Metabolite Kicking Heading p value Hedges’ g n Concentration (mM) n Concentration (mM) dlPFC Glx 14 7.04 ± 1.57 15 7.95 ± 1.76 0.156 0.514 tNAA 14 9.04 ± 0.89 15 8.74 ± 0.76 0.333 -0.343 tCr 14 5.99 ± 0.89 15 5.95 ± 0.36 0.993 -0.063 tCho 14 1.74 ± 0.26 15 1.71 ± 0.17 0.659 -0.137 mI 14 3.12 ± 0.67 15 3.02 ± 0.65 0.619 -0.142 M1 Glx 13 6.99 ± 1.01 14 7.55 ± 1.57 0.287 0.404 tNAA 13 9.02 ± 0.50 14 9.43 ± 0.71 0.012 0.593 tCr 13 5.53 ± 0.31 14 5.82 ± 0.44 0.010 0.702 tCho 13 1.56 ± 0.15 14 1.56 ± 0.12 0.894 -0.034 mI 13 2.88 ± 0.54 14 2.76 ± 0.36 0.511 -0.241 Abbreviations: dlPFC – dorsolateral prefrontal cortex, Glx – glutamate and glutamine, M1 – primary motor cortex, mI – myo-inositol, mM (millimolar), tNAA – total N -acetyl aspartate ( N -acetyl aspartate and N -acetyl glutamate), ROI – region of interest, tCho – total choline (choline-containing compounds), tCr – total creatine (creatine and phosphocreatine). Data presented as mean ± SD. Bold values represent statistically significant changes ( p < 0.05). Insert Table 2 approximately here 3.2.2 Electrical Properties Tomography (EPT) Heading significantly reduced tissue conductivity in 11 clusters located in the white matter of the frontal, occipital, temporal and parietal lobes, and cerebellum. The location, size and T values of these clusters are presented in Fig. 3 . No increases in conductivity were observed. Insert Fig. 3 approximately here 3.2.3 Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) Heading had no significant effects on network connectivity strengths in any of the six brain networks identified (i.e., the salience, sensorimotor, visual, default mode, cerebellar and executive control). 3.2.4 Pseudo Continuous Arterial Spin Labelling (pCASL) Heading had no significant effects on resting CBF in any brain region. 3.2.5 Diffusion Weighted Imaging (DWI) Heading had no significant effects on FA or MD in any brain region. In the fixel-based analysis, Heading had no significant effects on FB, FC or FBC in any brain region (example images from the analysis provided in Figure S3). 3.3 Electroencephalography (EEG) Of the 14 participants who received both treatments, 12 were included in these analyses. Indeed, EEG data could not be collected for one participant (on Kicking) due to technical difficulties and was unreadable for another (on both treatments). Heading had no significant effects on infraslow, theta, alpha or beta frequency bands (Table S3). For cluster analyses, no significant differences for any individual band was identified (all p > 0.05, Fig. 4 A). However, Heading tended to decrease alpha frequency power in five left posterior channels (Fig. 4 B; p = 0.066). Insert Fig. 4 approximately here 3.4 Blood Biomarkers 3.4.1 Plasma Neurofilament-Light (Nf-L) and Glial Fibrillary Acidic Protein (GFAP) Fifteen participants were included. Nine of the 120 samples (7.5%) could not be collected due to difficulties with vascular access. For Nf-L, the mean ± SD intra-assay coefficient of variation (CV) for the duplicate samples was 6.0 ± 5.1%. There was no significant interaction between Treatment and Time ( p = 0.510; Fig. 5 A). However, a significant interaction between Session and Treatment Order ( p < 0.001) was observed (Fig. 5 B). Post hoc comparisons showed that participants had higher plasma Nf-L concentrations on Session 2 with Treatment Order Heading–Kicking ( n = 8) than Kicking–Heading ( n = 7) (6.60 [IQR: 4.64–7.63] vs. 3.70 [IQR: 3.47–4.82] pg/mL; p = 0.046; g = 1.201). No significant difference between Treatment Orders were observed at Session 1 (5.18 [IQR: 3.85–5.51] vs. 3.76 [IQR: 3.34–4.92] pg/mL; p = 0.765, g = 0.370). Insert Fig. 5 approximately here For GFAP, the mean ± SD intra-assay CV for the duplicate samples was 5.5 ± 5.1%. A significant interaction between Treatment and Time was observed ( p = 0.043; Fig. 5 C). Post hoc comparisons showed that Heading increased GFAP concentrations at 24 hrs Post compared to Kicking (81.0 [IQR: 65.2–88.2] vs. 66.5 [IQR: 59.3–77.1] pg/mL; p = 0.014; Hedges’ g = 0.637). No other significant differences were observed. 3.4.2 Serum Inflammatory Markers Fifteen participants were included. Three of 60 samples (5%) could not be collected due to difficulties with vascular access. The mean intra-assay CV for the samples ranged from 11.8 ± 11.7% (MCP-1) to 36.0 ± 27.6% (IL-6). Due to extremely high CVs, the utility of data is limited. We have provided the results of these analyses in Table S6 for completeness, but recommend they be interpreted with a high degree of caution. 3.5 Cognitive Function Fifteen participants were included. No significant Treatment, Time or Treatment \(\:\times\:\) Time interactions were observed (all p ’s > 0.05; Table S4). Note: SWM errors were not formally analysed as participants demonstrated a high degree of accuracy on all treatments and time points (i.e., achieved zero errors on 81% of occasions) (Table S4). 3.6 Adverse Event (AE) Monitoring The frequency of possible (but non-specific) symptoms of concussion are presented in Table S5. The most commonly reported symptom was ‘Pressure in the head’ (9/15; 60%) Post-Heading, followed by Headache (6/15; 40%) 2.5 hrs Post-Heading. Both abated within 24 hours. The remaining symptoms were relatively infrequent. No participants experienced a concussion. 4.0 Discussion This RCT investigated the acute effects of non-concussive impacts on brain function, chemistry and microstructure utilising MRI and other methods. Contrary to our hypothesis, significant changes were only observed in select outcomes. With respect to brain function, several regions displayed significant reductions in tissue conductivity, as assessed via EPT, while no changes in brain network connectivity (assessed via rs-fMRI) or CBF (assessed via pCASL) were found. Accompanying this, a non-significant trend toward reduced alpha frequency power (assessed via EEG) was noted in the left parietal/occipital cortex. Non-concussive impacts regionally altered brain chemistry (assessed via 1 H-MRS) as assessed via, with increases in tNAA and tCr observed within the M1 (but not the dlPFC). We found no significant effects of non-concussive impacts on brain microstructure, as assessed via DWI. However, two blood biomarkers (GFAP and Nf-L) expressed in brain microstructures, were significantly elevated 24 hours and ~ 7-days post Heading, respectively. These changes were observed in the absence of significant adverse symptoms and detectable alterations in cognitive function. No previous studies have used EPT to investigate the effects of non-concussive (or concussive) impacts on the electrical properties of the brain. Our finding of reduced tissue conductivity in several brain regions is, therefore, novel and indicates that white matter conductivity is affected by non-concussive impacts. White matter is typically more susceptible to injury from biomechanical forces than grey matter due to its anisotropic and rheological properties [ 64 – 66 ]. However, it is unclear why almost no alterations were observed within frontal regions (i.e., where the soccer balls were received). The more posterior alterations could represent a contrecoup mechanism, whereby the movement of the brain within the skull produces a secondary impact elsewhere [ 67 ]. The largest cluster demonstrating reduced conductivity (> 200 voxels) was located posteriorly across the left optic radiation, which transmits visual information to the visual cortex [ 68 ]. Our participants did not experience symptoms related to vision (e.g., double vision, blank or vacant look). However, previous interventional studies have shown that non-concussive impacts can affect oculomotor and neuro-ophthalmologic function [ 21 , 22 ]. Interestingly, this general region of the brain also tended to demonstrate reduced alpha activity (as assessed via EEG), which may be reflective of increased excitability to visual regions or visual processing [ 69 , 70 ]. That said, a previous interventional study utilising EEG found that whole brain and channel-wise alpha power was unaltered following a SHT when compared to the control trial [ 17 ]. Thus, the effect of non-concussive impacts on EEG metrics and in relation to visual function requires further investigation. Other functional metrics including brain network connectivity via rs-fMRI, CBF via pCASL and cognitive (memory) function, showed no significant changes from non-concussive impacts. Previous observational studies have detected increases in brain network connectivity (in sensorimotor, visual and cerebellum networks) [ 50 ] and CBF following non-concussive impacts [ 71 , 72 ]. In these studies, longer exposure periods (e.g., a full season) and different contact sports (e.g., American Football) could potentially produce stronger effects. However, observational studies also often have significant limitations (e.g., confounders, biases) that are difficult to control [ 73 ]. That said, one interventional study did find that non-concussive impacts altered performance on the same neurocognitive tasks used in this investigation [ 18 ]. Specifically, errors on both the SWM and PAL tasks were increased following heading, compared to baseline [ 18 ]. However, this study utilised a more demanding SHT (i.e., 20 headers in 10 minutes, at a speed of ~ 39km/hr and distance of 6 meters) [ 18 ]. Ultimately, with neither brain network connectivity nor CBF demonstrating significant effects (including brain regions involved in memory [ 74 ]), it is not surprising that cognitive function was unaltered in the current study. Two recent meta-analyses have investigated the effects of non-concussive impacts on brain chemistry via 1 H-MRS [ 15 , 75 ]. Both included observational studies only, as interventional studies were lacking. Neither meta-analysis reported significant differences in tNAA, tCr, tCho, and Glx levels between ‘cases’ (i.e., individuals exposed to non-concussive impacts) and controls [ 15 , 75 ]. However, one found that tNAA (considered a biomarker of energy utilisation and neuronal health in mTBI [ 32 , 76 ]) and tCr (considered a biomarker of energy homeostasis [ 76 ]) levels decreased from the pre-season to the mid-/post-season period [ 15 ]. In contrast, our study found that non-concussive impacts increased tNAA and tCr levels within the M1. This could be indicative of mitochondrial hypermetabolism in this region [ 76 ]. Again, the inconsistent findings could be due to methodological differences between studies. Alternatively, these studies may be detecting the same response along a temporal continuum (e.g., initial hypermetabolism as seen in concussive impacts [ 77 ], followed by delayed hypometabolism). Finally, the absence of frontal alterations (i.e., in the dlPFC) could be representative of a contrecoup mechanism as suggested earlier. The current study used two analytical approaches to interrogate DWI data. No significant effects were observed on FA or MD, nor on the FBA metrics of FC, FD or FDC; suggesting that microstructural tissue alterations were not readily apparent [ 78 ]. Two recent systematic reviews have summarised the effects of non-concussive impacts on brain microstructure via DWI [ 13 , 79 ]. Both reviews (which again included observational studies, only) concluded that non-concussive impacts typically, albeit somewhat inconsistently, decrease FA and increase MD in predominantly white matter regions [ 13 , 79 ]. As previously noted, the inconsistent findings could be due to methodological differences between studies. No studies have previously investigated the effects of non-concussive impacts on DWI using FBA. While no significant microstructural alterations were observed using DWI, two blood biomarkers expressed in brain microstructures, Nf-L and GFAP, were significantly elevated post Heading. First, a large effect size was observed in an axonal injury marker, Nf-L, ~ 7-days post Heading. Three previous interventional studies have likewise shown that non-concussive impacts increase blood Nf-L concentrations and that concentrations can remain elevated for a prolonged period (e.g., > 22 days) [ 25 , 26 , 80 ]. Though, in these cases, changes emerged within 24 hours. This could be because a more intensive SHT was employed (e.g., one study administered 40 headers in 20 minutes at a speed of 77.4 km/hr) [ 25 ]. Alternatively, we might have been unable to detect an effect at 24 hrs because of treatment order effects. Nonetheless, the observed change in plasma Nf-L concentration suggests some degree of microstructural disruption to axons or other neural components [ 81 ]. The disparity between this finding and those from DWI metrics, may reflect a greater sensitivity of the blood biomarker to subtle microstructural damage. Second, a moderate effect size increase in plasma concentrations of an astroglial pathology marker, GFAP, was observed 24 hrs post Heading. Two previous interventional studies have investigated the effects of non-concussive impacts on plasma GFAP concentrations [ 27 , 80 ]. One found no difference 2 hrs or 24 hrs post Heading (compared to a Kicking control) [ 80 ], while the other found that concentrations were increased 2 hrs, but not 24 hrs, post-Heading (compared to baseline) [ 27 ]. The significant and delayed response observed in our trial could be due to the fact that our SHT was more intensive than that utilised in previous investigations [ 27 , 80 ]. Nevertheless, the observed change in plasma GFAP concentration is indicative of an astrocytic response, which may reflect astrogliosis, or disruption to astrocyte integrity [ 27 , 82 ]. The extent of this alteration appears subtle, given that no alterations to astrocyte activation were observed in DWI metrics, infraslow oscillations (via EEG) or CBF (via pCASL) [ 83 , 84 ]. Our study was not without limitations. First, it had a relatively small sample size; thus, may be under-powered to detect additional effects. Second, we could not blind participants or researchers involved in trial activities to treatments. Third, we are unable to comment on the effect of the SHT on serum inflammatory markers due to extremely high CVs between duplicate samples. Finally, our study only included young, healthy males. Thus, results may not be generalisable to other populations. 5.0 Conclusions this RCT demonstrates that non-concussive impacts; specifically, those administered in the form of a controlled SHT, can alter select markers of brain function, chemistry and microstructure. These include changes to tissue conductivity, brain concentrations of tNAA and tCr, and plasma Nf-L and GFAP concentrations. These alterations appear to be subtle, with some only detected in specific regions and no corresponding functional deficits (e.g., cognitive, adverse symptoms) observed. Nevertheless, our findings suggest that non-concussive impacts have the potential to elevate metabolism and impair neuronal/glial cell functioning, particularly in mid- to posterior- brain regions. These observations substantiate suggestions that prolonged exposure to non-concussive impacts has long term consequences for the brain health and suggest that individuals should exercise caution when performing repeated non-concussive impacts in sport. Abbreviations 1 H-MRS proton magnetic resonance spectroscopy bFFE balanced fast field echo BOLD blood-oxygen-level-dependent CBF cerebral blood flow CNS central nervous system CRT concussion recognition tool CV coefficient of variation dlPFC dorsolateral prefrontal cortex DREAM dual refocusing echo acquisition mode DWI diffusion weighted imaging EEG electroencephalography EPT electrical properties tomography FA fractional anisotropy FBA fixel-based analysis FC fibre cross section FD fibre density FDC fibre density + cross section FDR false discovery rate fMRI functional magnetic resonance imaging FWE family wise-error FWHM full width at half maximum GFAP glial fibrillary acidic protein Glx glutamate/glutamine HR heart rate ICA independent components analysis IL interleukin LLOQ lower limit of quantification M1 primary motor cortex MCP-1 monocyte chemoattractant protein-1 MD mean diffusivity mI myo-inositol MNI Montreal Neurological Institute MRI magnetic resonance imaging MRS magnetic resonance spectroscopy mTBI mild traumatic brain injury NeuRA Neuroscience Research Australia Nf-L neurofilament light PAL paired associate learning pCASL pseudo continuous arterial spin labelling PLA peak linear acceleration PRA peak rotational acceleration RCT randomised controlled trial RF radiofrequency rs-fMRI resting-state functional magnetic resonance imaging SHT soccer heading task sLASER semiadiabatic Localization by Adiabatic SElective Refocusing SNR signal-to-noise ratio SPM statistical parametric mapping SWM spatial working memory TARQUIN Totally Automatic Robust Quantitation in NMR tCho total choline tCr total creatine TE echo time tNAA total N -acetylaspatate TR repetition time U SG urine specific gravity VAPOR VAriable Power and Optimized Relaxations Declarations Ethics Approval and Consent to Participate: The trial was approved by the University of Sydney’s Human Research Ethics Committee (2021/515) and registered prospectively with the Australian New Zealand Clinical Trials Registry (ACTRN12621001355864). All research was completed in accordance with the Declaration of Helsinki. Informed consent was obtained from all individual participants included in the study. Consent for publication: Not applicable. Competing Interests: The authors have no competing interests to declare that are relevant to the content of this article. Funding: This study was primarily supported by funding from the Lambert Initiative for Cannabinoid Therapeutics (a philanthropically funded centre for medicinal cannabis research at the University of Sydney). Additional funding support was provided internally from Griffith University and Monash University. Author Contributions: N.D., L.A.H., C.R., S.J.M., B.D., C.I., E.A.C., P.J.A., S.B., M.E.B., K.R., I.S.M., & D.M. conceptualised the research project. N.D., R.V.R., F.A.T., K.R., & D.M. contributed to data collection. N.D., R.V.R., F.A.T., L.A.H., C.R., S.J.M., B.D., C.I., A.L.P, M.A.G., N.W.J., J.C., W.T.O., & D.M. contributed to data analysis and interpretation of results. N.D. and D.M. drafted the manuscript. All authors critically reviewed and edited the manuscript prior to submission. Acknowledgements: The authors acknowledge the facilities and scientific and technical assistance of the National Imaging Facility, a National Collaborative Research Infrastructure Strategy (NCRIS) capability, at NeuRA Imaging, NeuRA, UNSW Node. We also acknowledge and greatly appreciate the technical expertise of Paul M. Macey and Judy Zhu in the development of software codes. the Randwick City Council for their approval to use the sporting fields for treatment sessions. The ongoing financial support of Barry and Joy Lambert is gratefully acknowledged by the research team. 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Maris E, Oostenveld R. Nonparametric statistical testing of EEG-and MEG-data. J Neurosci Methods. 2007;164(1):177–90. Bates D et al. Package ‘lme4’. URL http://lme4 . r-forge. r-project. org, 2009. Lenth R et al. Package ‘emmeans’ . 2019. Magnusson A, et al. Package ‘glmmtmb’. R Package Version. 2017;0(2):0. Cohen J. Statistical Power Analysis for the Behavioral Sciences. Routledge; 1988. Park E, Baker AJ. The Pathophysiology of Concussion , in Tackling the Concussion Epidemic: A Bench to Bedside Approach , T.A. Schweizer and A.J. Baker, Editors. 2022, Springer International Publishing: Cham. pp. 25–41. Giordano C, et al. The influence of anisotropy on brain injury prediction. J Biomech. 2014;47(5):1052–9. Grevesse T, et al. Opposite rheological properties of neuronal microcompartments predict axonal vulnerability in brain injury. Sci Rep. 2015;5:9475. Payne WN, De Jesus O, Payne AN. Contrecoup Brain Injury. StatPearls Publishing; 2022. Treasure Island (FL). Ramos-Fresnedo A et al. Chap. 2 - Supratentorial White Matter Tracts , in Comprehensive Overview of Modern Surgical Approaches to Intrinsic Brain Tumors , K. Chaichana and A. Quiñones-Hinojosa, Editors. 2019, Academic Press. pp. 23–35. Romei V et al. Resting electroencephalogram alpha-power over posterior sites indexes baseline visual cortex excitability. NeuroReport, 2008. 19(2). Hanslmayr S, et al. Visual discrimination performance is related to decreased alpha amplitude but increased phase locking. Neurosci Lett. 2005;375(1):64–8. Brett BL, et al. Longitudinal alterations in cerebral perfusion following a season of adolescent contact sport participation compared to non-contact athletes. Volume 40. NeuroImage: Clinical; 2023. p. 103538. Slobounov SM, et al. The effect of repetitive subconcussive collisions on brain integrity in collegiate football players over a single football season: a multi-modal neuroimaging study. Volume 14. Neuroimage: clinical; 2017. pp. 708–18. Boyko EJ. Observational research — opportunities and limitations. J Diabetes Complicat. 2013;27(6):642–8. Balsters JH, Robertson IH, Calhoun VD. BOLD Frequency Power Indexes Working Memory Performance. Front Hum Neurosci. 2013;7:207. Joyce JM, et al. Magnetic resonance spectroscopy of traumatic brain injury and subconcussive hits: A systematic review and meta-analysis. J Neurotrauma. 2022;39(21–22):1455–76. Rae CD. A guide to the metabolic pathways and function of metabolites observed in human brain 1H magnetic resonance spectra. Neurochem Res. 2014;39(1):1–36. Giza CC, Hovda DA. The new neurometabolic cascade of concussion. Neurosurgery. 2014;75(0 4):S24–33. Baliyan V, et al. Diffusion weighted imaging: Technique and applications. World J Radiol. 2016;8(9):785–98. Koerte IK, et al. Diffusion Imaging of Sport-related Repetitive Head Impacts—A Systematic Review. Neuropsychol Rev. 2023;33(1):122–43. Nowak MK, et al. ADHD May Associate With Reduced Tolerance to Acute Subconcussive Head Impacts: A Pilot Case-Control Intervention Study. J Atten Disord. 2022;26(1):125–39. Arslan B, Zetterberg H. Neurofilament light chain as neuronal injury marker – what is needed to facilitate implementation in clinical laboratory practice? Clinical Chemistry and Laboratory Medicine (CCLM), 2023. 61(7): p. 1140–9. Abdelhak A, et al. Blood GFAP as an emerging biomarker in brain and spinal cord disorders. Nat Reviews Neurol. 2022;18(3):158–72. Howarth C. The contribution of astrocytes to the regulation of cerebral blood flow. Front NeuroSci. 2014;8:87930. Hughes SW, et al. 1 0 - Infraslow (< 0.1Hz) oscillations in thalamic relay nuclei: basic mechanisms and significance to health and disease states . In: Van Someren EJW, et al. editors. Progress in Brain Research. Elsevier; 2011. pp. 145–62. Supplementary Files SupplementaryFiles7.docx Cite Share Download PDF Status: Published Journal Publication published 18 Jun, 2025 Read the published version in Sports Medicine-Open → Version 1 posted Reviewers agreed at journal 03 Aug, 2024 Reviewers invited by journal 30 Jul, 2024 Editor invited by journal 29 Jul, 2024 Editor assigned by journal 23 Jul, 2024 First submitted to journal 22 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4765251","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":333942389,"identity":"322338fe-9de9-43fc-9d5f-3f5f06ada353","order_by":0,"name":"Nathan Delang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIie2PsUoDQRCGBw62Wth2guQdBg4SQ+TyKnss5AXSBJR4IKQ0rYWvICQI1isDlyZge6BF0mhjcTYhlThERJvNWQruB/sv+zMfwwJEIn8TgrWkkePHJyhPAGxUrGSrEGU1RPy9Ql5iNYRmpQvJYm3PnrLbh5uN9/Z4Is39o4YsLwJKr1AjsuWzu6teSBREaVxfgwsq5HUHrWLXqUrgt538RZojDckhpbuz7+zSqxL2W8ibrSjnB7dAPuWMzPRL0UoUDiusRphfssVK7ZXWnFXau6ZlGlSWF4u63vLAzMqk9nZipNlUr+PTdkiB5PPKC7Q/GwrNfzMA45unIpFI5H/yAWLLXJqLb2DbAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-9925-278X","institution":"School of Health Sciences and Social Work, Griffith University, Gold Coast, Queensland, Australia","correspondingAuthor":true,"prefix":"","firstName":"Nathan","middleName":"","lastName":"Delang","suffix":""},{"id":333942390,"identity":"a5308a30-5279-415b-be1e-60a68a79ccb8","order_by":1,"name":"Rebecca V. Robertson","email":"","orcid":"","institution":"The University of Sydney School of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"V.","lastName":"Robertson","suffix":""},{"id":333942391,"identity":"714bf50e-102d-44a8-81e9-8f9c2458bb24","order_by":2,"name":"Fernando A. Tinoco Mendoza","email":"","orcid":"","institution":"The University of Sydney School of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"A. Tinoco","lastName":"Mendoza","suffix":""},{"id":333942392,"identity":"1458fdaa-3847-41b7-8cba-5557ac175b4d","order_by":3,"name":"Luke A. Henderson","email":"","orcid":"","institution":"The University of Sydney School of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Luke","middleName":"A.","lastName":"Henderson","suffix":""},{"id":333942393,"identity":"d3b58b7d-cd2f-4bec-93d8-90a016f92427","order_by":4,"name":"Caroline Rae","email":"","orcid":"","institution":"Neuroscience Research Australia","correspondingAuthor":false,"prefix":"","firstName":"Caroline","middleName":"","lastName":"Rae","suffix":""},{"id":333942394,"identity":"4a933a6c-79e1-4fa3-a2f3-e5e69ad7f63c","order_by":5,"name":"Stuart J. 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Green","email":"","orcid":"","institution":"Neuroscience Research Australia","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"A.","lastName":"Green","suffix":""},{"id":333942401,"identity":"0401a962-43c4-4184-93b3-1495d7fdd2a2","order_by":12,"name":"Nicholas W. Jenneke","email":"","orcid":"","institution":"The University of Sydney School of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Nicholas","middleName":"W.","lastName":"Jenneke","suffix":""},{"id":333942402,"identity":"e482156e-373e-42ad-9d10-a1bd75d64b9f","order_by":13,"name":"Jun Cao","email":"","orcid":"","institution":"University of New South Wales School of Biomedical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Cao","suffix":""},{"id":333942403,"identity":"a63dcff5-9f76-4649-8f15-a09a07e35c0d","order_by":14,"name":"William T. O’Brien","email":"","orcid":"","institution":"Monash University Central Clinical School","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"T.","lastName":"O’Brien","suffix":""},{"id":333942404,"identity":"37ae15fc-42ac-40a7-bc8b-81e002ba1ed9","order_by":15,"name":"Shane Ball","email":"","orcid":"","institution":"The University of Sydney School of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shane","middleName":"","lastName":"Ball","suffix":""},{"id":333942405,"identity":"180a7e1f-5498-4630-8dcd-0987122ccdf6","order_by":16,"name":"Michael E. Buckland","email":"","orcid":"","institution":"Royal Prince Alfred Hospital","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"E.","lastName":"Buckland","suffix":""},{"id":333942406,"identity":"56128d95-52f2-4b32-9913-d7ff7a94dd10","order_by":17,"name":"Katherine Rae","email":"","orcid":"","institution":"The Sports Clinic, The University of Sydney, Sydney, New South Wales, Australia","correspondingAuthor":false,"prefix":"","firstName":"Katherine","middleName":"","lastName":"Rae","suffix":""},{"id":333942407,"identity":"93579441-16fe-4110-840a-868cc05b3bde","order_by":18,"name":"Iain S. McGregor","email":"","orcid":"","institution":"The University of Sydney Brain and Mind Centre","correspondingAuthor":false,"prefix":"","firstName":"Iain","middleName":"S.","lastName":"McGregor","suffix":""},{"id":333942408,"identity":"0618ec4f-43ad-48a9-9e46-6cab7e7d4854","order_by":19,"name":"Danielle McCartney","email":"","orcid":"","institution":"The University of Sydney Brain and Mind Centre","correspondingAuthor":false,"prefix":"","firstName":"Danielle","middleName":"","lastName":"McCartney","suffix":""}],"badges":[],"createdAt":"2024-07-18 23:05:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4765251/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4765251/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40798-025-00867-0","type":"published","date":"2025-06-18T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63901903,"identity":"cf85abc2-4ad3-4530-84f5-f0d92457f420","added_by":"auto","created_at":"2024-09-03 14:25:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26822,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental procedures.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGrey represents UDS and U\u003csub\u003eSG\u003c/sub\u003e checks; Yellow represents the cognitive tests and blood draw; Orange represents the trial intervention (soccer task); Blue represents MRI; Green represents EEG. Crosses (X) are used to signify adverse event (concussion symptom) checks. Abbreviations: EEG – electroencephalography; MRI – magnetic resonance imaging; UDS – urine drug screen; U\u003csub\u003eSG\u003c/sub\u003e – urine specific gravity.\u003c/p\u003e","description":"","filename":"Figure1ExperimentalProcedures.png","url":"https://assets-eu.researchsquare.com/files/rs-4765251/v1/63db769fc8f90ebd57ffeb0c.png"},{"id":63901907,"identity":"3b69880b-a1ad-4e27-9d05-d1c781ac273a","added_by":"auto","created_at":"2024-09-03 14:25:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":111734,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCONSORT Participant Flow Diagram.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eDid not complete second trial (Kicking) due to family reasons. \u003csup\u003eb\u003c/sup\u003eAll participants who were randomised were included in the final (analytical) sample (except where the specific analytical technique could not handle missing data).\u003c/p\u003e","description":"","filename":"Figure2CONSORTFlowDiagram.png","url":"https://assets-eu.researchsquare.com/files/rs-4765251/v1/dffa115df6ed10a5467c7f5f.png"},{"id":63901906,"identity":"5cca7618-4a8a-4ca8-b357-45a3277b7f21","added_by":"auto","created_at":"2024-09-03 14:25:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":555476,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocations of significant clusters from electrical properties tomography data (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eabove\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e) and statistical details (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ebelow\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e) between treatment sessions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCooler (blue) colours represent higher t values and a decrease in signal intensity. Location of each sagittal and axial slice in Montreal Neurological Institute space are indicated at the bottom left of each slice. Abbreviations: MCP – middle cerebellar peduncle; MdLF – middle longitudinal fasciculus; MNI – Montreal Neurological Institute; OR – optic radiation; SI – signal intensity; SLF – superior longitudinal fasciculus.\u003c/p\u003e","description":"","filename":"Figure3EPT.png","url":"https://assets-eu.researchsquare.com/files/rs-4765251/v1/ebf53d22aafed0878e94239b.png"},{"id":63902704,"identity":"3bb615f1-299f-4e1b-ad24-96987aa4abbf","added_by":"auto","created_at":"2024-09-03 14:33:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElectroencephalography data between treatment sessions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Power spectra across all frequencies between treatment sessions. (B) A trend (\u003cem\u003ep\u003c/em\u003e=0.066) for reduced alpha frequency power in five left posterior channels on Heading. Cooler (blue) colours represent higher t values and a decrease in power (scale provided on right).\u003c/p\u003e","description":"","filename":"Figure4EEG.png","url":"https://assets-eu.researchsquare.com/files/rs-4765251/v1/c59deff0a9631f05f87d488e.png"},{"id":63901904,"identity":"3d9573e6-4d8a-4e77-a5ff-635d61ae83ba","added_by":"auto","created_at":"2024-09-03 14:25:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116434,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlasma biomarker concentrations.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Nf-L concentration between treatment and timepoint. (B) Nf-L concentration between sessions and treatment order. Session 2 was ≥7 days (mean=9 days) after session 1. (C) GFAP concentration between treatment and timepoint. Note that the simplest model violating the fewest assumptions (i.e., untransformed) was used for analysis of GFAP, although still failed Shapiro-Wilk test of residuals (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). Abbreviations: GFAP – glial fibrillary acidic protein; mL – millilitre; Nf-L – neurofilament light; pg – picogram.\u003c/p\u003e","description":"","filename":"Figure5NfLandGFAP.png","url":"https://assets-eu.researchsquare.com/files/rs-4765251/v1/c0219829f7e1238cda11a3b6.png"},{"id":85231844,"identity":"d21e8f45-c78f-4789-89d1-0734b86f435e","added_by":"auto","created_at":"2025-06-23 16:09:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2896074,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4765251/v1/75d70f3b-5a6c-4b27-baba-f95e7689971a.pdf"},{"id":63901908,"identity":"decd3bdd-64cc-4839-acc5-318de063787e","added_by":"auto","created_at":"2024-09-03 14:25:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2261509,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiles7.docx","url":"https://assets-eu.researchsquare.com/files/rs-4765251/v1/110d2d1c54cfc25bb0c2f022.docx"}],"financialInterests":"","formattedTitle":"The acute effects of non-concussive head impacts in sport: A randomised control trial.","fulltext":[{"header":"Key Points","content":"\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe effects of non-concussive head impacts on the brain are poorly understood. Employing a variety of magnetic resonance imaging sequences after controlled head impacts can interrogate parameters that have not been investigated in previous research.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThis study observed acute alterations to select chemical, functional and microstructural parameters in the absence of overt cognitive deficits or reported symptoms, after participants completed a controlled soccer heading task.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThese findings highlight the \u0026lsquo;silent\u0026rsquo; physiological changes that can occur after non-concussive head impacts and emphasise the importance of exercising caution when performing repeated head impacts in sport.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e\u003c/p\u003e"},{"header":"1.0 Background","content":"\u003cp\u003eAthletes participating in contact/collision sports such as American football, rugby and association football (soccer) can sustain hundreds of head impacts each sporting season [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. High strength, unanticipated and/or poorly located head impacts can cause \u003cem\u003econcussion\u003c/em\u003e, a type of mild traumatic brain injury (mTBI) that is accompanied by a host of unpleasant symptoms (e.g., headache, blurred vision, nausea) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Head impacts that do not elicit clinical signs or symptoms are termed \u0026lsquo;\u003cem\u003esubconcussive\u003c/em\u003e\u0026rsquo;, or increasingly \u0026lsquo;\u003cem\u003enon-concussive\u0026rsquo;\u003c/em\u003e, impacts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These impacts are exceedingly common, accounting for \u0026gt;\u0026thinsp;99% of all head impacts incurred in sport [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. They also have the potential to cause long-term harm, with some evidence suggesting that chronic traumatic encephalopathy (an incurable neurodegenerative disease related to head trauma [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]) can develop in the presence of non-concussive impacts exclusively [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite this, the effects of non-concussive impacts on the brain remain poorly understood [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral observational studies have investigated the microstructural and physiological sequelae of non-concussive impacts in sport; specifically, those incurred over the course of a sporting season [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These studies have employed various assessment techniques, including \u003cem\u003ecentral methods\u003c/em\u003e (e.g., magnetic resonance imaging [MRI] sequences such as diffusion weighted imaging [DWI], magnetic resonance spectroscopy [MRS] and functional MRI [fMRI]), \u003cem\u003eperipheral methods\u003c/em\u003e (e.g., blood biomarkers), and \u003cem\u003efunctional tests\u003c/em\u003e (e.g., cognitive tasks) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Recent systematic reviews summarising their findings indicate that non-concussive impacts have the potential to alter brain microstructure, chemistry and function [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, results are inconsistent [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It is also noted that observational studies have inherent limitations (e.g., confounders, biases) and cannot establish causation.\u003c/p\u003e \u003cp\u003eInterventional studies (particularly, randomised controlled trials [RCTs]) are increasingly being used to investigate the effects of non-concussive impacts on the brain [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. When using these designs, impacts are administered in the form of a controlled non-concussive soccer heading task (SHT) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Studies employing a SHT have shown that non-concussive impacts can elicit significant alterations in neuroelectric (via electroencephalography [EEG]) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], cognitive [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], neurovascular [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], neuroophthalmologic [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and vestibular [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], function in healthy soccer athletes. SHTs have also been reported to increase blood concentrations of neurofilament light (Nf-L; a biomarker of axonal pathology) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], glial fibrillary acidic protein (GFAP; a biomarker of astrocyte pathology) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and certain inflammatory markers [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, no interventional studies have investigated the microstructural and physiological effects of non-concussive impacts in sport using MRI techniques. This is important, as MRI has the capacity to interrogate regional chemical, functional and microstructural parameters that have not previously been investigated and allow researchers to predict the functional consequences of any changes observed.\u003c/p\u003e \u003cp\u003eThe primary aim of this study was to investigate the acute effects of non-concussive impacts, administered in the form of a controlled SHT, on brain microstructure, function and chemistry using MRI techniques. It was hypothesised that non-concussive impacts would result in unfavourable changes to these parameters, as per previous observational studies.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] The secondary aim was to investigate the effects of non-concussive impacts on neuroelectric activity (via EEG), cognitive function and blood biomarkers of neuronal and astroglial damage (i.e., Nf-L, GFAP) and inflammation (e.g., interleukin [IL]-6, etc).\u003c/p\u003e"},{"header":"2.0 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eA randomised, controlled, crossover trial was conducted at Neuroscience Research Australia (NeuRA; Randwick, NSW). The trial was approved by the University of Sydney\u0026rsquo;s Human Research Ethics Committee (2021/515) and registered prospectively with the Australian New Zealand Clinical Trials Registry (ACTRN12621001355864). All research was completed in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Participants\u003c/h2\u003e \u003cp\u003eHealthy individuals aged between 18\u0026ndash;35 years and with \u0026ge;\u0026thinsp;5 years of soccer heading experience were recruited. The full eligibility criteria are presented in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Briefly, the key exclusion criteria were: (1) a head, neck, face or eye injury (including a confirmed or suspected concussion) within the last 12 months; (2) an uncontrolled physical or mental health condition; (3) a neurological disorder; (4) a contraindication to MRI; or (5) pregnant or lactating.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Enrolment\u003c/h2\u003e \u003cp\u003eEach volunteer completed a face-to-face screen with the trial coordinator (N.D.) and physician (K.R.). Here, they were informed about the nature and risks of experimental procedures, before providing written informed consent and being assessed for eligibility. Eligible participants were familiarised with the cognitive function tasks (Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e2.7.4\u003c/span\u003e Cognitive Function Acquisition) and asked to provide demographic information (including an indication of \u0026lsquo;usual\u0026rsquo; [non-specific] concussion symptoms as per the Concussion Recognition Tool [CRT]-5) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Randomisation and Allocation Concealment\u003c/h2\u003e \u003cp\u003eParticipants were randomised to one of two possible treatment orders in a 1:1 ratio at the beginning of their first test session. Specifically, they were assigned a unique identification code (by N.D.) that was linked to a treatment order via a pre-populated randomisation schedule. The schedule was generated in a series of balanced blocks (and one \u0026lsquo;block\u0026rsquo; of one) by an investigator (E.C.) using an online random number generator (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sealedenvelope.com/simple-randomiser/v1/lists\u003c/span\u003e\u003cspan address=\"https://www.sealedenvelope.com/simple-randomiser/v1/lists\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The schedule could only be accessed by the investigator and one other researcher (P.A.), neither of whom had contact with participants. The balanced blocks also varied in size so that the final treatment order within each block could not be predicted. Treatment allocation was then concealed using sealed, opaque envelopes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Treatments\u003c/h2\u003e \u003cp\u003eTreatments were administered by the trial coordinator (N.D.) and a second investigator (D.M.) on the outdoor fields of Paine Reserve (Randwick, NSW; ~500 m from NeuRA).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Intervention (\u0026lsquo;Heading\u0026rsquo; Task)\u003c/h2\u003e \u003cp\u003eThe intervention was a SHT (\u0026lsquo;Heading\u0026rsquo;). A JUGS Soccer Machine\u0026trade; (JUGS\u0026reg; Australia, Cheltenham, Victoria, Australia) was used to launch FIFA regulation size 5 soccer balls at a speed of 35 km\u0026sdot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Participants performed 20 headers in 20 minutes from ~\u0026thinsp;12 meters to the JUGS. They were instructed to hit the ball with their forehead and to direct it back towards the JUGS. Unsuccessful headers (i.e., where there was no contact between the head and the ball) were re-administered.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Control (\u0026lsquo;Kicking\u0026rsquo; Task)\u003c/h2\u003e \u003cp\u003eThe control was a soccer kicking task (\u0026lsquo;Kicking\u0026rsquo;). It was administered exactly as the intervention, except that participants kicked (rather than headed) the ball (which was launched along the ground).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Treatment Sessions\u003c/h2\u003e \u003cp\u003eParticipants completed two treatment sessions, Heading or Kicking, separated by \u0026ge;\u0026thinsp;7 days.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1 Standardisation Procedures\u003c/h2\u003e \u003cp\u003ePrior to each treatment session, participants were instructed to: (1) avoid soccer heading and playing other contact sports (\u0026gt;\u0026thinsp;7 days); (2) avoid using alcohol (\u0026gt;\u0026thinsp;24 hours), caffeine (\u0026gt;\u0026thinsp;12 hours), anti-inflammatory medication (\u0026gt;\u0026thinsp;4 days) and central nervous system (CNS) active drugs (\u0026gt;\u0026thinsp;7 days); (3) avoid moderate to strenuous exercise (\u0026gt;\u0026thinsp;12 hours); (4) spend\u0026thinsp;\u0026gt;\u0026thinsp;8 hours in bed overnight; (5) consume a standardised breakfast (at home) and (6) consume 500 mL of water before arriving at the clinic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2 Experimental Procedures\u003c/h2\u003e \u003cp\u003eExperimental procedures are summarised in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Briefly, participants arrived at NeuRA between ~\u0026thinsp;7:30\u0026ndash;8:30 AM and verbally acknowledged compliance to the standardisation procedures. A urine sample was collected to confirm avoidance of CNS active drugs (DrugCheck\u0026reg; NxStep Onsite Urine Drug Test) and to assess hydration status (urine specific gravity [U\u003csub\u003eSG\u003c/sub\u003e]; Palette Digital Refractometer, ATAGO, USA). If U\u003csub\u003eSG\u003c/sub\u003e was \u0026gt;\u0026thinsp;1.024, likely indicating hypohydration [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], participants consumed 500 mL of water [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Participants then completed a series of baseline assessments (\u0026lsquo;Pre\u0026rsquo;; Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e2.7\u003c/span\u003e Data Collection), before they were walked to the outdoor field to receive their assigned treatment (i.e., Heading or Kicking). Following treatment, participants returned to NeuRA to complete a series of post-treatment assessments (\u0026lsquo;Post\u0026rsquo; and \u0026lsquo;2.5 hrs Post\u0026rsquo;). They left between 12:30\u0026thinsp;\u0026minus;\u0026thinsp;1:30 PM but returned the following day to complete their 24-hour post-treatment assessments (\u0026lsquo;24 hrs Post\u0026rsquo;). Participants were instructed to adhere to the same standardisation procedures ahead of this visit.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eInsert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003eapproximately here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Data Collection\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.7.1. MRI Acquisition (Primary Outcome)\u003c/h2\u003e \u003cp\u003eMRI commenced\u0026thinsp;~\u0026thinsp;60 minutes post-treatment and took\u0026thinsp;~\u0026thinsp;60 minutes to complete. The timing of this assessment was selected with consideration for pragmatic factors (e.g., participant transportation) and prior research suggesting that SHTs can elicit immediate alterations in neurovascular and corticomotor function [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. All images were collected by a registered radiographer using a 3T MRI scanner (Ingenia CX, Philips) with a 32-channel head coil. Participants were placed supine into the MRI scanner with their head secured in a tight-fitting head coil with headphones to prevent movement. Images were collected in the following order (time of acquisition post-treatment provided in mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD): (1) T\u003csub\u003e1\u003c/sub\u003e-weighted anatomical (+\u0026thinsp;68\u0026thinsp;\u0026plusmn;\u0026thinsp;6 mins); (2) proton MRS (\u003csup\u003e1\u003c/sup\u003eH-MRS; +78\u0026thinsp;\u0026plusmn;\u0026thinsp;7 mins); (3) electrical properties tomography (EPT; +94\u0026thinsp;\u0026plusmn;\u0026thinsp;7 mins); (4) blood-oxygen-level-dependent (BOLD) resting-state fMRI (rs-fMRI; +102\u0026thinsp;\u0026plusmn;\u0026thinsp;12 mins); (5) pseudo continuous arterial spin labelling (pCASL; +111\u0026thinsp;\u0026plusmn;\u0026thinsp;8 mins); and (5) DWI (+\u0026thinsp;117\u0026thinsp;\u0026plusmn;\u0026thinsp;8 mins). Scans were conducted to measure brain chemistry (\u003csup\u003e1\u003c/sup\u003eH-MRS), function (EPT, rs-fMRI and pCASL) and microstructure (DWI). Participants were instructed to remain awake and focused on a crosshair (displayed on a screen) throughout functional scans.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eT1-weighted anatomical\u003c/span\u003e: A high-resolution 3-dimensional anatomical image set covering the entire brain was acquired for accurate image registration and segmentation (211 sagittal slices; repetition time [TR]/echo time [TE]\u0026thinsp;=\u0026thinsp;7.3/3.4 ms; flip angle\u0026thinsp;=\u0026thinsp;8\u0026deg;; slice thickness\u0026thinsp;=\u0026thinsp;0.9 mm; voxel size\u0026thinsp;=\u0026thinsp;0.75x0.75x0.9 mm).\u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e1\u003c/span\u003e \u003c/sup\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eH-MRS\u003c/span\u003e: Single voxel \u003csup\u003e1\u003c/sup\u003eH-MRS was collected from two brain regions: the left dorsolateral prefrontal cortex (dlPFC) and primary motor cortex (M1) in the somatotopic region representing the dominant foot, as these regions have demonstrated neurometabolic alterations in previous observational studies of non-concussive impacts [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Data from \u003csup\u003e1\u003c/sup\u003eH-MRS were collected using a semiadiabatic Localization by Adiabatic SElective Refocusing (sLASER) sequence (VAriable Power and Optimized Relaxations [VAPOR] water suppression; 64 averages; 2048 data points; TE\u0026thinsp;=\u0026thinsp;31 ms for dlPFC and 33 ms for M1, TR\u0026thinsp;=\u0026thinsp;5000 ms; voxel size\u0026thinsp;=\u0026thinsp;15 mm\u003csup\u003e3\u003c/sup\u003e). Second order shimming was conducted using the auto-shimming function with the vendor-supplied (Phillips) sLASER sequence; only spectra with full width at half maximum (FWHM) values less than 15 Hz were accepted (otherwise scans were repeated).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEPT\u003c/span\u003e: Scans were acquired using a balanced fast field echo (bFFE) sequence (TR/TE\u0026thinsp;=\u0026thinsp;2.54/1.27 ms; flip angle\u0026thinsp;=\u0026thinsp;25\u0026deg;; nonselective radiofrequency [RF] pulses; compressed SENSE factor 1; RF shimming calibrated with full coverage 2D dual refocusing echo acquisition mode [DREAM]; voxel size\u0026thinsp;=\u0026thinsp;1 mm\u003csup\u003e3\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ers-fMRI\u003c/span\u003e: A rs-fMRI series consisting of 250 whole brain BOLD fMRI image volumes was collected (TR/TE\u0026thinsp;=\u0026thinsp;1500/30 ms; 75 axial slices; voxel size\u0026thinsp;=\u0026thinsp;2 mm\u003csup\u003e3\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003epCASL\u003c/span\u003e: A resting pCASL series covering the entire brain was acquired (TR/TE\u0026thinsp;=\u0026thinsp;4188/10.7 ms; 24 axial slices; voxel size\u0026thinsp;=\u0026thinsp;3x3x6 mm; 384 images). Four background suppression pulses were applied to maximise the sensitivity to blood perfusion [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDWI\u003c/span\u003e: A DWI set covering the entire brain was acquired using a single-shot multi-section spin-echo echo-planar pulse sequence (TR/TE\u0026thinsp;=\u0026thinsp;3000/75 ms; flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;; 57 axial slices; voxel size\u0026thinsp;=\u0026thinsp;2.5 mm\u003csup\u003e3\u003c/sup\u003e). For each slice, diffusion gradients were applied along 32 phase-encoding directions at b-value\u0026thinsp;=\u0026thinsp;1000 s/mm\u003csup\u003e2\u003c/sup\u003e, 64 phase-encoding directions at b-value\u0026thinsp;=\u0026thinsp;3000 s/mm\u003csup\u003e2\u003c/sup\u003e, and one volume acquired at b-value\u0026thinsp;=\u0026thinsp;0 s/mm\u003csup\u003e2\u003c/sup\u003e. Anatomical and diffusion image sets were visually inspected for artifacts; no participants were excluded from the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.7.2 EEG Acquisition\u003c/h2\u003e \u003cp\u003eA 15-minute resting EEG recording was acquired\u0026thinsp;~\u0026thinsp;2 hours post-treatment using a 64-channel EEG system (ANT Neuro, Netherlands). Electrodes were placed according to the standard 10\u0026ndash;20 system [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], with reference electrodes placed on opposing mastoid processes, and an electrode placed on the orbicularis oculi muscle to monitor eye movements. Participants were tested while seated in a quiet room and instructed to relax, close their eyes and let their mind wander. Continuous EEG data were acquired at a sampling rate of 1000 Hz with online band-pass filtered between 0.01 and 100 Hz.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.7.3 Blood Acquisition\u003c/h2\u003e \u003cp\u003eBlood was collected into a 6.0 mL pre-treated EDTA vacutainer and 3.5 mL serum vacutainer at Pre, Post, 2.5 hrs Post and 24 hrs Post. Each vacutainer was centrifuged for 15 minutes at 1500 g and 4˚C within 30 minutes of collection (following coagulation of the serum sample), with plasma and serum stored at \u0026minus;\u0026thinsp;80˚C until analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.7.4 Cognitive Function Acquisition\u003c/h2\u003e \u003cp\u003eCognitive function was assessed at Pre, Post, 2.5 hrs Post and 24 hrs Post using two computerised tasks from the Cambridge Neuropsychological Test Automated Battery (CANTAB): the Paired Associate Learning (PAL; ~8 minutes duration) and Spatial Working Memory (SWM; ~4 minutes duration) tasks [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These tasks have demonstrated sensitivity to the effects of SHTs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Participants completed the tasks in a quiet room and were instructed to take their time and minimise errors.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Data Processing and Analysis\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.8.1 MRI Processing and Analysis\u003c/h2\u003e \u003cp\u003e \u003csup\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e1\u003c/span\u003e \u003c/sup\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eH-MRS\u003c/span\u003e: All analysis specifics, including visualisation of voxel placement and sample spectra, are presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (MRSinMRS Acquisition and Analysis Checklist) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The spectrum and unsuppressed water spectrum for each participant were analysed by N.D. (unblinded) using Totally Automatic Robust Quantitation in NMR (TARQUIN; v4.3.10) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Pre-processing consisted of eddy current correction, lipid filtering, automatic referencing water residual removal using Hankel singular value decomposition, zero-order phase correction, and automatic referencing using zero filling. The neurometabolites of interest were: total \u003cem\u003eN\u003c/em\u003e-acetylaspatate (tNAA; NAA\u0026thinsp;+\u0026thinsp;\u003cem\u003eN\u003c/em\u003e acetyl glutamate), myo-inositol (mI), total choline (tCho; choline-containing compounds), total creatine (tCr; creatine\u0026thinsp;+\u0026thinsp;phosphocreatine), and glutamate/glutamine (Glx). Neurometabolites were analysed and reported as water-referenced levels (using the default TARQUIN processing) rather than using internal neurometabolite references (e.g., tCr), as several neurometabolites including tCr may be influenced by non-concussive impacts [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe quality of all spectra were examined using the line width/FWHM of fitted spectra and signal-to-noise ratio (SNR) using TARQUIN\u0026rsquo;s default processing. Data were excluded if FWHM\u0026thinsp;\u0026gt;\u0026thinsp;15 Hz or SNR was \u0026lt;\u0026thinsp;5 (no data were discarded on this basis; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In addition, the accuracy of voxel placement was visually inspected through heat maps. Data from poorly placed voxels were discarded. Tissue parcellation (grey and white matter) within each voxel was reported (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEPT\u003c/span\u003e: Data were processed by J.C. (blinded to treatment) to produce conductivity maps according to methods described by Cao and colleagues [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In brief, T1-weighted turbo field echo images were co-registered and segmented into white matter, grey matter and cerebrospinal fluid using FSL [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], to alleviate boundary artifacts. Within each tissue type, an average parabolic phase fitting method was used to reduce artifacts amplified in the Laplacian [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and the second derivatives of the fitted phase were taken to calculate conductivity. The conductivity maps of each participant from both sessions were normalised into Montreal Neurological Institute (MNI) space (voxel size 2 mm isotropic) using statistical parametric mapping (SPM) 12 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ers-fMRI\u003c/span\u003e: Using SPM 12 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and custom software, fMRI images were processed by N.D. (unblinded). Images were slice-time and motion corrected, and global signal drifts removed using the detrending method described by Macey and colleagues [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Physiological noise was corrected (cardiac frequency band 60\u0026ndash;120 beats per minute\u0026thinsp;+\u0026thinsp;1 harmonic; respiratory frequency band 8\u0026ndash;25 breaths per minute\u0026thinsp;+\u0026thinsp;1 harmonic) using the DRIFTER toolbox [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and the six-parameter movement-related signal changes modelled and removed using a linear modelling of realignment parameters procedure [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The fMRI images were then co-registered to participant\u0026rsquo;s T1 anatomical image, the T1 image then spatially normalised to the MNI template and normalisation parameters applied to the fMRI images. The fMRI images were then spatially smoothed using a 6 mm FWHM Gaussian filter. Independent components analysis (ICA) was performed using the Group ICA toolbox [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] to define major brain networks [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Thirty independent components were extracted using the Infomax ICA algorithm [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], and major networks identified by visual inspection. We selected nine components from six major brain networks: the salience, sensorimotor, visual, default mode, cerebellar and executive control networks (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003epCASL\u003c/span\u003e: Using SPM 12 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], pCASL data were analysed by N.D. (unblinded). All pCASL sets were realigned, co-registered to each participant\u0026rsquo;s source image, and a mean cerebral blood flow (CBF) map created using the subtraction method from the ASL toolbox [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Each participant\u0026rsquo;s source images were spatially normalised to MNI space and the parameters applied to the CBF maps. The CBF maps were smoothed using a 6 mm FWHM Gaussian filter.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDWI\u003c/span\u003e: During acquisition, a coding error occurred that corrupted the acquisitions with diffusion gradients at b-value\u0026thinsp;=\u0026thinsp;1000 s/mm\u003csup\u003e2\u003c/sup\u003e. Consequently, b-1000 DWI were removed from the image set. Using SPM12 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], the remaining images were processed by N.J. (blinded to treatment). Images were corrected for motion, eddy current and b0 distortion. Elements of the diffusion tensor were computed from the images using a linear model, then fractional anisotropy (FA) and mean diffusivity (MD) whole-brain maps were derived. The FA and MD maps were resliced into 1.5 mm isotropic voxel sizes and co-registered to each individual\u0026rsquo;s T1-weighted anatomical image to ensure all images were in the same three-dimensional space. Subsequently, they were spatially normalised to MNI space using the previously calculated parameters from T1 images and spatially smoothed using a 5 mm FWHM Gaussian filter.\u003c/p\u003e \u003cp\u003eIn addition, a fixel-based analysis (FBA) was conducted using MRtrix3 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], to determine tract-specific quantities of fibre density (FD), fibre cross section (FC) and a combination of both (FDC). Data were processed by M.G. (blinded to treatment) according to previous published methods [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e2.8.2 EEG Processing and Analysis\u003c/h2\u003e \u003cp\u003eProcessing of EEG data were performed in Matlab (Version R2020b; MathWorks, Inc., Natick, MA, USA) and the FieldTrip toolbox by N.D. (unblinded) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Prior to processing, data were bandpass filtered between 0.01 and 35 Hz. Initially, large artefacts and poor-quality channels were identified via visual inspection and removed from the data. Following this, an ICA was conducted to remove typical eye artefacts (e.g., blinks and saccades). Poor quality channels were reconstructed via interpolation from neighbouring channels. Finally, the EEG signals were re-referenced to the average of the mastoid electrodes and down sampled to 200 Hz to enhance processing speed. Estimates of cortical power were produced using the fast Fourier transform, at the following frequencies: 0.02\u0026ndash;0.09 (at steps of 0.01 Hz), 0.1\u0026ndash;0.9 (at steps of 0.1 Hz), and 1\u0026ndash;30 (at steps of 1 Hz). The cortical power at each frequency was computed by averaging the power across all EEG channels. Four frequency bands were included for analysis: infra-slow (0.03\u0026ndash;0.06 Hz), theta (4\u0026ndash;8 Hz), alpha (9\u0026ndash;12 Hz), and beta (13\u0026ndash;25 Hz).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e2.8.3 Blood Biomarkers Analysis\u003c/h2\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNf-L and GFAP\u003c/span\u003e: Plasma samples were analysed using a Simoa HD-X Analyzer (Quanterix, Lexington, MA) using commercially available Simoa kits as per manufacturer\u0026rsquo;s instructions [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Samples were tested in duplicate by a scientist (W.O.) blinded to treatment. GFAP Discovery assays (Item 102336) were used to quantify GFAP, with participant samples analysed on the same plate, and all samples measuring above the lower limit of quantification (LLOQ; 0.686 pg/mL). For Nf-L, NF-Light V2 Advantage assays (Item 104073) were used, with all Pre and 24 hrs Post samples from the same participant were analysed on the same plate, and the remaining samples analysed separately later (once additional funding was sourced). All samples measured above the LLOQ for Nf-L (1.38 pg/mL).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInflammatory Markers\u003c/span\u003e: Serum samples were analysed by a Contract Research Organisation (Eve Technologies, Calgary, AB, Canada). The Human Cytokine 15-Plex Assay Array was performed to determine concentrations of: granulocyte-macrophage colony-stimulating factor, interferon gamma, IL-1β, IL-1RA, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p40, IL-12p70, IL-13, monocyte chemoattractant protein-1 (MCP-1), and tumour necrosis factor-α. The analyses were performed in duplicate by a laboratory technician blinded to treatment. Only Pre and 24 hrs Post samples were analysed due to funding constraints.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e2.8.4 Cognitive Function Outcomes\u003c/h2\u003e \u003cp\u003eThe PAL task measures visual memory and learning [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The outcome measures were: number of attempts required to complete the task (\u0026lsquo;attempts\u0026rsquo;) and number of errors made (adjusted for attempts if the participant did not complete the task; \u0026lsquo;adjusted errors\u0026rsquo;). The SWM task measures working memory, executive functions, and strategy [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The outcome measures were: number of errors (\u0026lsquo;errors\u0026rsquo;) and a strategy score (calculated based on the randomness of participants\u0026rsquo; opening boxes, where lower scores indicated better strategy; \u0026lsquo;strategy\u0026rsquo;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e2.8.5 Treatment Characteristics\u003c/h2\u003e \u003cp\u003eAn impact monitoring mouthguard (Prevent Biometrics\u0026trade;, Edina, MN, USA) was used to measure linear and rotational acceleration of head impacts (peak linear acceleration [PLA]; peak rotational acceleration [PRA]). This device has demonstrated a high degree of accuracy in controlled and field environments (concordance correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.8) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Participants were also asked to rate how \u0026lsquo;well\u0026rsquo; they performed each header on an 11-point scale (-5=\u0026lsquo;very poorly\u0026rsquo;; to +\u0026thinsp;5=\u0026lsquo;very well\u0026rsquo;) and the \u0026lsquo;strength\u0026rsquo; of each header on a 5-point scale (1=\u0026lsquo;very low\u0026rsquo;; to 5=\u0026lsquo;very high\u0026rsquo;). Mean heart rate (HR) throughout the 20-minute activity was determined using a chest strap monitor (Polar H10 HR Sensor).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e2.8.6 Adverse Event Monitoring\u003c/h2\u003e \u003cp\u003eParticipants were monitored for signs of concussion (adverse event [AE]) using Parts 1\u0026ndash;3 of the CRT-5 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], at Post, 2.5 hrs Post, 4\u0026ndash;8 hrs Post and 24 hrs Post Task (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Parts 1 and 2 were used to identify \u0026lsquo;red flag\u0026rsquo; and \u0026lsquo;observable sign(s)\u0026rsquo; of concussion. Part 3 was used to identify possible (non-specific) \u0026lsquo;symptoms\u0026rsquo; of concussion. Participants answered \u0026lsquo;yes\u0026rsquo;, \u0026lsquo;no\u0026rsquo; or \u0026lsquo;maybe\u0026rsquo; to the \u0026lsquo;red flag(s)\u0026rsquo;, \u0026lsquo;observable sign(s)\u0026rsquo; and potential \u0026lsquo;symptoms\u0026rsquo;. Responses were documented, reviewed and escalated to the trial physician, as necessary.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Sample Size\u003c/h2\u003e \u003cp\u003eA target sample size of 15 was selected with consideration of practical factors such as time, cost, and resource allocation, rather than formal power analysis. This pragmatic approach reflects the current lack of interventional studies investigating the acute effects of non-concussive impacts on brain structure, function and chemistry using MRI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical Analyses\u003c/h2\u003e \u003cp\u003eThe EPT, fMRI, ASL and DWI data were analysed using Matlab (Version R2023b; MathWorks, Inc., Natick, MA, USA) and EEG data using Matlab (Version R2020b). The remaining data were analysed using R (Version 4.2.2) [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.10.1 Electrical Properties Tomography (EPT), Resting-State Functional Magnetic Resonance Imaging (rs-fMRI), Arterial Spin Labelling (ASL) and Diffusion Weighted Imaging (DWI)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSecond level, random effects, paired analyses were conducted to determine significant differences at a voxel-by-voxel level (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, false discovery rate [FDR] corrected, minimum cluster\u0026thinsp;=\u0026thinsp;10 contiguous voxels). For the rs-fMRI network analyses, each analysis was restricted by creating a mask of the relevant network using both treatments\u0026rsquo; scan images (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR corrected). Significant differences for all MRI scans were then overlaid onto a mean T1-weighted anatomical image set.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e2.10.2 Diffusion Weighted Imaging (DWI) Fixel-Based Analysis\u003c/h2\u003e \u003cp\u003eA general liner model was fitted to every fixel to compare between treatments for all metrics (FD, FC, FDC). A whole brain tractogram consisting of two million streamlines was used for statistical inference using connectivity-based fixel enhancement [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Data were analysed between treatment using non-parametric permutation testing (5000 permutations; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, family wise-error [FWE] corrected).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e2.10.3 Electroencephalography (EEG)\u003c/h2\u003e \u003cp\u003eGlobal cortical power of each frequency band (i.e., infraslow, theta, alpha and beta) between treatments were compared using paired t-tests with significance set a \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. In addition, to identify a group of channels where significant differences existed, the spatial distribution of power differences between treatments and within infraslow, theta, alpha and beta bands, were examined using cluster-based permutation tests (4000 permutations; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, corrected using the \u0026lsquo;cluster\u0026rsquo; function) [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e2.10.4 Other Data\u003c/h2\u003e \u003cp\u003eContinuous variables (neurometabolites [\u003csup\u003e1\u003c/sup\u003eH-MRS], blood biomarkers, treatment characteristics) were analysed using linear mixed-effects models and the \u0026lsquo;lme4\u0026rsquo; and \u0026lsquo;emmeans\u0026rsquo; packages [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The models included Treatment, Time, and Treatment \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e Time interaction as fixed effects and had a random intercept (Participant) and slope (Treatment), as appropriate. The models were generated using the restricted maximum likelihood criterion and no covariance structure was specified (unstructured). If the residuals were non-normally distributed (Shapiro\u0026ndash;Wilk test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) or heteroskedastic (Levene test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the data were square-root transformed (and if unimproved, log-transformed) and reanalysed. If an appropriate model could not be generated, the \u0026lsquo;best\u0026rsquo; of those described above (i.e., simplest model violating the fewest assumptions) was utilised. Note: As plasma Nf-L concentrations can remain elevated for 22 days following SHTs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], a separate analysis was conducted, using Session (1 or 2), Treatment Order (Heading\u0026ndash;Kicking or Kicking\u0026ndash;Heading) and Session \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e Treatment Order interaction as a fixed effects and a random intercept (Participant) and slope (Treatment Order).\u003c/p\u003e \u003cp\u003eCount variables (cognitive function) were analysed using generalised linear mixed-effects models and the \u0026lsquo;glmmTMB\u0026rsquo; and \u0026lsquo;emmeans\u0026rsquo; packages [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. These models included the same fixed and random effects structure as above, and were fitted to a Poisson distribution, unless over-dispersed, and/or zero-inflated. In these instances, a negative binomial, zero-inflated Poisson, or zero-inflated negative binomial distribution was substituted, respectively (with both parts of the zero-inflated models containing the same fixed effects).\u003c/p\u003e \u003cp\u003eTwo-sided pairwise comparisons were used to compare estimated marginal means across Treatment and/or Time if a significant main effect of Treatment, Time, or a Treatment \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e Time interaction (or equivalent for Nf-L) was observed. Data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD unless non-normally distributed (in which case, data were presented as Median [IQR]). Statistical significance was accepted as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Dunn\u0026ndash;Šid\u0026aacute;k-corrected) and effect sizes were calculated as Hedges\u0026rsquo; g [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Participant and Treatment Characteristics\u003c/h2\u003e \u003cp\u003eRecruitment commenced in November 2021 and concluded 12 months later. Eighteen volunteers signed informed consent and 15 were randomised (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Of those randomised, 14 received both treatments (i.e., as intended) and one received one treatment (i.e., Heading only) after being unable to complete the second session for personal reasons. All 15 participants were included in the final sample (except where the analytical technique employed could not handle missing data) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants completed their sessions between seven and 25 days apart (9\u0026thinsp;\u0026plusmn;\u0026thinsp;4 days). Note that due to difficulties with recruitment, only male participants completed this trial.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003e(years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDominant Foot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePredominant playing position\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of headers in last 12 months\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNumber of years heading experience\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNumber of previous concussions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLength of time since last concussion (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTime of season\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDays between trials\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentre Midfield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePre-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentre Attacking Midfield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePre-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentre Back\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIn-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentre Defending Midfield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIn-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWide Back\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOff-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentre Defensive Midfield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIn-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIn-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentre Back\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIn-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentre Back\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePost-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentre Back\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOff-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentre Back\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePost-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStriker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePost-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentre Back\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePost-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentre Midfield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOff-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentre Back\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOff-Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e567\u0026thinsp;\u0026plusmn;\u0026thinsp;549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ea\u003c/sup\u003eThe number of head impacts in the last 12 months was subjectively assessed via a standardised questionnaire.\u003csup\u003e27\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003eb\u003c/sup\u003ePre-Season: (Scheduled training prior to In-Season but after Off-Season - possible infrequent games); In-Season (Scheduled training and frequent competitive games [at least weekly]); Post-Season (No/minimal scheduled training and between In-Season and Off-Season - possible infrequent games); Off-Season (No scheduled training/games)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ec\u003c/sup\u003eParticipant had longer than anticipated time between trials due to being diagnosed with SARS-CoV-2 between Trial 1 and 2.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ed\u003c/sup\u003eParticipant did not complete Trial 2 due to family reasons.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eInsert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003eapproximately here\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eInsert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003eapproximately here\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTreatment characteristics are summarised in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. The average PLA and PRA of headers was 15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6 g and 1271\u0026thinsp;\u0026plusmn;\u0026thinsp;602 rad/s\u003csup\u003e2\u003c/sup\u003e, respectively. No head impacts were recorded on Kicking. Participants rated the strength of headers as 3 (IQR: 2\u0026ndash;4) on a 5-point scale and how well they performed each header as 1 (IQR: -1\u0026ndash;3) on a -5 to 5 scale. Mean HR tended to be higher during Heading than Kicking (81\u0026thinsp;\u0026plusmn;\u0026thinsp;12; 79\u0026thinsp;\u0026plusmn;\u0026thinsp;12 bpm, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.081, g\u0026thinsp;=\u0026thinsp;0.159).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Magnetic Resonance Imaging (MRI)\u003c/h2\u003e \u003cp\u003eOnly the 14 participants who received both treatments could be included in the MRI analyses, except for the \u003csup\u003e1\u003c/sup\u003eH-MRS analysis (details below).\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Proton Magnetic Resonance Spectroscopy (\u003csup\u003e1\u003c/sup\u003eH-MRS)\u003c/h2\u003e \u003cp\u003eFifteen participants were included in the dlPFC analyses. Only 14 were included in the M1 analyses due to inaccurate voxel placement (on both sessions; ID: 1). Heading increased tNAA (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, g\u0026thinsp;=\u0026thinsp;0.593) and tCr (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010, g\u0026thinsp;=\u0026thinsp;0.702) levels in the M1 compared to Kicking. No other significant differences were observed in either region (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; all \u003cem\u003ep\u003c/em\u003e\u0026rsquo;s\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNeurometabolite levels between trials assessed using Magnetic Resonance Spectroscopy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eROI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMetabolite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eKicking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHeading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHedges\u0026rsquo; g\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConcentration (mM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConcentration (mM)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edlPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e7.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e7.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etNAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e8.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e5.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etCho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e7.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etNAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e9.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e5.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etCho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: dlPFC \u0026ndash; dorsolateral prefrontal cortex, Glx \u0026ndash; glutamate and glutamine, M1 \u0026ndash; primary motor cortex, mI \u0026ndash; myo-inositol, mM (millimolar), tNAA \u0026ndash; total \u003cem\u003eN\u003c/em\u003e-acetyl aspartate (\u003cem\u003eN\u003c/em\u003e-acetyl aspartate and \u003cem\u003eN\u003c/em\u003e-acetyl glutamate), ROI \u0026ndash; region of interest, tCho \u0026ndash; total choline (choline-containing compounds), tCr \u0026ndash; total creatine (creatine and phosphocreatine). Data presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. \u003cb\u003eBold\u003c/b\u003e values represent statistically significant changes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eInsert\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003eapproximately here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Electrical Properties Tomography (EPT)\u003c/h2\u003e \u003cp\u003eHeading significantly reduced tissue conductivity in 11 clusters located in the white matter of the frontal, occipital, temporal and parietal lobes, and cerebellum. The location, size and \u003cem\u003eT\u003c/em\u003e values of these clusters are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. No increases in conductivity were observed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eInsert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003eapproximately here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Resting-State Functional Magnetic Resonance Imaging (rs-fMRI)\u003c/h2\u003e \u003cp\u003eHeading had no significant effects on network connectivity strengths in any of the six brain networks identified (i.e., the salience, sensorimotor, visual, default mode, cerebellar and executive control).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Pseudo Continuous Arterial Spin Labelling (pCASL)\u003c/h2\u003e \u003cp\u003eHeading had no significant effects on resting CBF in any brain region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5 Diffusion Weighted Imaging (DWI)\u003c/h2\u003e \u003cp\u003eHeading had no significant effects on FA or MD in any brain region. In the fixel-based analysis, Heading had no significant effects on FB, FC or FBC in any brain region (example images from the analysis provided in Figure S3).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Electroencephalography (EEG)\u003c/h2\u003e \u003cp\u003eOf the 14 participants who received both treatments, 12 were included in these analyses. Indeed, EEG data could not be collected for one participant (on Kicking) due to technical difficulties and was unreadable for another (on both treatments).\u003c/p\u003e \u003cp\u003eHeading had no significant effects on infraslow, theta, alpha or beta frequency bands (Table S3). For cluster analyses, no significant differences for any individual band was identified (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). However, Heading tended to decrease alpha frequency power in five left posterior channels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.066).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eInsert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cem\u003eapproximately here\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Blood Biomarkers\u003c/h2\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Plasma Neurofilament-Light (Nf-L) and Glial Fibrillary Acidic Protein (GFAP)\u003c/h2\u003e \u003cp\u003eFifteen participants were included. Nine of the 120 samples (7.5%) could not be collected due to difficulties with vascular access.\u003c/p\u003e \u003cp\u003eFor Nf-L, the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD intra-assay coefficient of variation (CV) for the duplicate samples was 6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1%. There was no significant interaction between Treatment and Time (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.510; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). However, a significant interaction between Session and Treatment Order (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Post hoc comparisons showed that participants had higher plasma Nf-L concentrations on Session 2 with Treatment Order Heading\u0026ndash;Kicking (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8) than Kicking\u0026ndash;Heading (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7) (6.60 [IQR: 4.64\u0026ndash;7.63] vs. 3.70 [IQR: 3.47\u0026ndash;4.82] pg/mL; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046; g\u0026thinsp;=\u0026thinsp;1.201). No significant difference between Treatment Orders were observed at Session 1 (5.18 [IQR: 3.85\u0026ndash;5.51] vs. 3.76 [IQR: 3.34\u0026ndash;4.92] pg/mL; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.765, g\u0026thinsp;=\u0026thinsp;0.370).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eInsert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cem\u003eapproximately here\u003c/em\u003e\u003c/p\u003e \u003cp\u003eFor GFAP, the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD intra-assay CV for the duplicate samples was 5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1%. A significant interaction between Treatment and Time was observed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Post hoc comparisons showed that Heading increased GFAP concentrations at 24 hrs Post compared to Kicking (81.0 [IQR: 65.2\u0026ndash;88.2] vs. 66.5 [IQR: 59.3\u0026ndash;77.1] pg/mL; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014; Hedges\u0026rsquo; g\u0026thinsp;=\u0026thinsp;0.637). No other significant differences were observed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Serum Inflammatory Markers\u003c/h2\u003e \u003cp\u003eFifteen participants were included. Three of 60 samples (5%) could not be collected due to difficulties with vascular access. The mean intra-assay CV for the samples ranged from 11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7% (MCP-1) to 36.0\u0026thinsp;\u0026plusmn;\u0026thinsp;27.6% (IL-6). Due to extremely high CVs, the utility of data is limited. We have provided the results of these analyses in Table S6 for completeness, but recommend they be interpreted with a high degree of caution.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec42\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Cognitive Function\u003c/h2\u003e \u003cp\u003eFifteen participants were included. No significant Treatment, Time or Treatment \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e Time interactions were observed (all \u003cem\u003ep\u003c/em\u003e\u0026rsquo;s\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table S4). Note: SWM errors were not formally analysed as participants demonstrated a high degree of accuracy on all treatments and time points (i.e., achieved zero errors on 81% of occasions) (Table S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec43\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Adverse Event (AE) Monitoring\u003c/h2\u003e \u003cp\u003eThe frequency of possible (but non-specific) symptoms of concussion are presented in Table S5. The most commonly reported symptom was \u0026lsquo;Pressure in the head\u0026rsquo; (9/15; 60%) Post-Heading, followed by Headache (6/15; 40%) 2.5 hrs Post-Heading. Both abated within 24 hours. The remaining symptoms were relatively infrequent. No participants experienced a concussion.\u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eThis RCT investigated the acute effects of non-concussive impacts on brain function, chemistry and microstructure utilising MRI and other methods. Contrary to our hypothesis, significant changes were only observed in select outcomes. With respect to brain function, several regions displayed significant reductions in tissue conductivity, as assessed via EPT, while no changes in brain network connectivity (assessed via rs-fMRI) or CBF (assessed via pCASL) were found. Accompanying this, a non-significant trend toward reduced alpha frequency power (assessed via EEG) was noted in the left parietal/occipital cortex. Non-concussive impacts regionally altered brain chemistry (assessed via \u003csup\u003e1\u003c/sup\u003eH-MRS) as assessed via, with increases in tNAA and tCr observed within the M1 (but not the dlPFC). We found no significant effects of non-concussive impacts on brain microstructure, as assessed via DWI. However, two blood biomarkers (GFAP and Nf-L) expressed in brain microstructures, were significantly elevated 24 hours and ~\u0026thinsp;7-days post Heading, respectively. These changes were observed in the absence of significant adverse symptoms and detectable alterations in cognitive function.\u003c/p\u003e \u003cp\u003eNo previous studies have used EPT to investigate the effects of non-concussive (or concussive) impacts on the electrical properties of the brain. Our finding of reduced tissue conductivity in several brain regions is, therefore, novel and indicates that white matter conductivity is affected by non-concussive impacts. White matter is typically more susceptible to injury from biomechanical forces than grey matter due to its anisotropic and rheological properties [\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. However, it is unclear why almost no alterations were observed within frontal regions (i.e., where the soccer balls were received). The more posterior alterations could represent a contrecoup mechanism, whereby the movement of the brain within the skull produces a secondary impact elsewhere [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe largest cluster demonstrating reduced conductivity (\u0026gt;\u0026thinsp;200 voxels) was located posteriorly across the left optic radiation, which transmits visual information to the visual cortex [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Our participants did not experience symptoms related to vision (e.g., double vision, blank or vacant look). However, previous interventional studies have shown that non-concussive impacts can affect oculomotor and neuro-ophthalmologic function [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Interestingly, this general region of the brain also tended to demonstrate reduced alpha activity (as assessed via EEG), which may be reflective of increased excitability to visual regions or visual processing [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. That said, a previous interventional study utilising EEG found that whole brain and channel-wise alpha power was unaltered following a SHT when compared to the control trial [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Thus, the effect of non-concussive impacts on EEG metrics and in relation to visual function requires further investigation.\u003c/p\u003e \u003cp\u003eOther functional metrics including brain network connectivity via rs-fMRI, CBF via pCASL and cognitive (memory) function, showed no significant changes from non-concussive impacts. Previous observational studies have detected increases in brain network connectivity (in sensorimotor, visual and cerebellum networks) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and CBF following non-concussive impacts [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. In these studies, longer exposure periods (e.g., a full season) and different contact sports (e.g., American Football) could potentially produce stronger effects. However, observational studies also often have significant limitations (e.g., confounders, biases) that are difficult to control [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. That said, one interventional study did find that non-concussive impacts altered performance on the same neurocognitive tasks used in this investigation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Specifically, errors on both the SWM and PAL tasks were increased following heading, compared to baseline [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, this study utilised a more demanding SHT (i.e., 20 headers in 10 minutes, at a speed of ~\u0026thinsp;39km/hr and distance of 6 meters) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Ultimately, with neither brain network connectivity nor CBF demonstrating significant effects (including brain regions involved in memory [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]), it is not surprising that cognitive function was unaltered in the current study.\u003c/p\u003e \u003cp\u003eTwo recent meta-analyses have investigated the effects of non-concussive impacts on brain chemistry via \u003csup\u003e1\u003c/sup\u003eH-MRS [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Both included observational studies only, as interventional studies were lacking. Neither meta-analysis reported significant differences in tNAA, tCr, tCho, and Glx levels between \u0026lsquo;cases\u0026rsquo; (i.e., individuals exposed to non-concussive impacts) and controls [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. However, one found that tNAA (considered a biomarker of energy utilisation and neuronal health in mTBI [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]) and tCr (considered a biomarker of energy homeostasis [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]) levels decreased from the pre-season to the mid-/post-season period [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In contrast, our study found that non-concussive impacts \u003cem\u003eincreased\u003c/em\u003e tNAA and tCr levels within the M1. This could be indicative of mitochondrial hypermetabolism in this region [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Again, the inconsistent findings could be due to methodological differences between studies. Alternatively, these studies may be detecting the same response along a temporal continuum (e.g., initial hypermetabolism as seen in concussive impacts [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], followed by delayed hypometabolism). Finally, the absence of frontal alterations (i.e., in the dlPFC) could be representative of a contrecoup mechanism as suggested earlier.\u003c/p\u003e \u003cp\u003eThe current study used two analytical approaches to interrogate DWI data. No significant effects were observed on FA or MD, nor on the FBA metrics of FC, FD or FDC; suggesting that microstructural tissue alterations were not readily apparent [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Two recent systematic reviews have summarised the effects of non-concussive impacts on brain microstructure via DWI [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Both reviews (which again included observational studies, only) concluded that non-concussive impacts typically, albeit somewhat inconsistently, decrease FA and increase MD in predominantly white matter regions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. As previously noted, the inconsistent findings could be due to methodological differences between studies. No studies have previously investigated the effects of non-concussive impacts on DWI using FBA.\u003c/p\u003e \u003cp\u003eWhile no significant microstructural alterations were observed using DWI, two blood biomarkers expressed in brain microstructures, Nf-L and GFAP, were significantly elevated post Heading. First, a large effect size was observed in an axonal injury marker, Nf-L, ~\u0026thinsp;7-days post Heading. Three previous interventional studies have likewise shown that non-concussive impacts increase blood Nf-L concentrations and that concentrations can remain elevated for a prolonged period (e.g., \u0026gt;\u0026thinsp;22 days) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Though, in these cases, changes emerged within 24 hours. This could be because a more intensive SHT was employed (e.g., one study administered 40 headers in 20 minutes at a speed of 77.4 km/hr) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Alternatively, we might have been unable to detect an effect at 24 hrs because of treatment order effects. Nonetheless, the observed change in plasma Nf-L concentration suggests some degree of microstructural disruption to axons or other neural components [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. The disparity between this finding and those from DWI metrics, may reflect a greater sensitivity of the blood biomarker to subtle microstructural damage.\u003c/p\u003e \u003cp\u003eSecond, a moderate effect size increase in plasma concentrations of an astroglial pathology marker, GFAP, was observed 24 hrs post Heading. Two previous interventional studies have investigated the effects of non-concussive impacts on plasma GFAP concentrations [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. One found no difference 2 hrs or 24 hrs post Heading (compared to a Kicking control) [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], while the other found that concentrations were increased 2 hrs, but not 24 hrs, post-Heading (compared to baseline) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The significant and delayed response observed in our trial could be due to the fact that our SHT was more intensive than that utilised in previous investigations [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Nevertheless, the observed change in plasma GFAP concentration is indicative of an astrocytic response, which may reflect astrogliosis, or disruption to astrocyte integrity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. The extent of this alteration appears subtle, given that no alterations to astrocyte activation were observed in DWI metrics, infraslow oscillations (via EEG) or CBF (via pCASL) [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study was not without limitations. First, it had a relatively small sample size; thus, may be under-powered to detect additional effects. Second, we could not blind participants or researchers involved in trial activities to treatments. Third, we are unable to comment on the effect of the SHT on serum inflammatory markers due to extremely high CVs between duplicate samples. Finally, our study only included young, healthy males. Thus, results may not be generalisable to other populations.\u003c/p\u003e"},{"header":"5.0 Conclusions","content":"\u003cp\u003ethis RCT demonstrates that non-concussive impacts; specifically, those administered in the form of a controlled SHT, can alter select markers of brain function, chemistry and microstructure. These include changes to tissue conductivity, brain concentrations of tNAA and tCr, and plasma Nf-L and GFAP concentrations. These alterations appear to be subtle, with some only detected in specific regions and no corresponding functional deficits (e.g., cognitive, adverse symptoms) observed. Nevertheless, our findings suggest that non-concussive impacts have the potential to elevate metabolism and impair neuronal/glial cell functioning, particularly in mid- to posterior- brain regions. These observations substantiate suggestions that prolonged exposure to non-concussive impacts has long term consequences for the brain health and suggest that individuals should exercise caution when performing repeated non-concussive impacts in sport.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003csup\u003e1\u003c/sup\u003eH-MRS\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eproton magnetic resonance spectroscopy\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ebFFE\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ebalanced fast field echo\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBOLD\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eblood-oxygen-level-dependent\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCBF\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ecerebral blood flow\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCNS\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ecentral nervous system\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCRT\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003econcussion recognition tool\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCV\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ecoefficient of variation\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003edlPFC\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003edorsolateral prefrontal cortex\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDREAM\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003edual refocusing echo acquisition mode\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDWI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ediffusion weighted imaging\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eEEG\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eelectroencephalography\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eEPT\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eelectrical properties tomography\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003efractional anisotropy\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFBA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003efixel-based analysis\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFC\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003efibre cross section\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFD\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003efibre density\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFDC\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003efibre density\u0026thinsp;+\u0026thinsp;cross section\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFDR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003efalse discovery rate\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003efMRI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003efunctional magnetic resonance imaging\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFWE\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003efamily wise-error\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFWHM\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003efull width at half maximum\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eGFAP\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eglial fibrillary acidic protein\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eGlx\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eglutamate/glutamine\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eheart rate\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eICA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eindependent components analysis\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIL\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003einterleukin\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLLOQ\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003elower limit of quantification\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eM1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eprimary motor cortex\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMCP-1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003emonocyte chemoattractant protein-1\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMD\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003emean diffusivity\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003emI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003emyo-inositol\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMNI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMontreal Neurological Institute\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e 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\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eecho time\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003etNAA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003etotal \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eN\u003c/span\u003e-acetylaspatate\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003erepetition time\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eU\u003csub\u003eSG\u003c/sub\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eurine specific gravity\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eVAPOR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eVAriable Power and Optimized Relaxations\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics Approval and Consent to Participate:\u003c/strong\u003e \u003cp\u003eThe trial was approved by the University of Sydney\u0026rsquo;s Human Research Ethics Committee (2021/515) and registered prospectively with the Australian New Zealand Clinical Trials Registry (ACTRN12621001355864). All research was completed in accordance with the Declaration of Helsinki. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests:\u003c/h2\u003e \u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis study was primarily supported by funding from the Lambert Initiative for Cannabinoid Therapeutics (a philanthropically funded centre for medicinal cannabis research at the University of Sydney). Additional funding support was provided internally from Griffith University and Monash University.\u003c/p\u003e\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e \u003cp\u003eN.D., L.A.H., C.R., S.J.M., B.D., C.I., E.A.C., P.J.A., S.B., M.E.B., K.R., I.S.M., \u0026amp; D.M. conceptualised the research project. N.D., R.V.R., F.A.T., K.R., \u0026amp; D.M. contributed to data collection. N.D., R.V.R., F.A.T., L.A.H., C.R., S.J.M., B.D., C.I., A.L.P, M.A.G., N.W.J., J.C., W.T.O., \u0026amp; D.M. contributed to data analysis and interpretation of results. N.D. and D.M. drafted the manuscript. All authors critically reviewed and edited the manuscript prior to submission.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eThe authors acknowledge the facilities and scientific and technical assistance of the National Imaging Facility, a National Collaborative Research Infrastructure Strategy (NCRIS) capability, at NeuRA Imaging, NeuRA, UNSW Node. We also acknowledge and greatly appreciate the technical expertise of Paul M. Macey and Judy Zhu in the development of software codes. the Randwick City Council for their approval to use the sporting fields for treatment sessions. The ongoing financial support of Barry and Joy Lambert is gratefully acknowledged by the research team.\u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials:\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBailes JE, et al. Role of subconcussion in repetitive mild traumatic brain injury: A review. 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Self-reported head injury symptoms exacerbated in those with previous concussions following an acute bout of purposeful soccer heading. Res sports Med. 2020;28(2):217\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWallace C, et al. Heading in soccer increases serum neurofilament light protein and SCAT3 symptom metrics. BMJ open sport Exerc Med. 2018;4(1):e000433.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWirsching A, et al. Association of acute increase in plasma neurofilament light with repetitive subconcussive head impacts: a pilot randomized control trial. J Neurotrauma. 2019;36(4):548\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNowak MK, et al. Neuro-ophthalmologic and blood biomarker responses in ADHD following subconcussive head impacts: a case-control trial. Front Psychiatry. 2023;14:1230463.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuibregtse ME, et al. 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Elsevier; 2011. pp. 145\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sports-medicine-open","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"smoa","sideBox":"Learn more about [Sports Medicine-Open](http://sportsmedicine-open.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/smoa/default.aspx","title":"Sports Medicine-Open","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Subconcussion, contact sport, neural, astroglial, metabolites","lastPublishedDoi":"10.21203/rs.3.rs-4765251/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4765251/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHead impacts, particularly, \u003cem\u003enon-concussive\u003c/em\u003e impacts, are common in sport. Yet, their effects on the brain are poorly understood. Here, we investigated the acute effects of non-concussive impacts on brain microstructure, chemistry, and function using magnetic resonance imaging (MRI) and other techniques.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFifteen healthy male soccer players completed this randomised, controlled, crossover trial. Participants completed a soccer heading task (\u0026lsquo;Heading\u0026rsquo;; the Intervention) and an equivalent \u0026lsquo;Kicking\u0026rsquo; task (the Control); followed by a series of MRI sequences between ~\u0026thinsp;60\u0026ndash;120 minutes post-tasks. Blood was also sampled, and cognitive function assessed, pre-, post-, 2.5 hours post-, and 24 hours post-tasks. Brain chemistry: Heading increased total \u003cem\u003eN\u003c/em\u003e-acetylaspartate (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) and total creatine (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010) levels in the primary motor cortex (but not the dorsolateral prefrontal cortex) as assessed via proton magnetic resonance spectroscopy. Glutamate-glutamine, myoinositol, and total choline levels were not altered in either region. Brain structure: Heading had no effect on diffusion weighted imaging metrics. However, two blood biomarkers expressed in brain microstructures, glial fibrillary acidic protein and neurofilament light, were elevated 24 hours (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) and ~\u0026thinsp;7-days (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) post-Heading (\u003cem\u003evs\u003c/em\u003e. Kicking), respectively. Brain function: Heading decreased tissue conductivity in five brain regions (\u003cem\u003ep\u003c/em\u003e\u0026rsquo;s\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as assessed via electrical properties tomography. However, no differences were identified in: (1) connectivity within major brain networks as assessed via resting-state functional MRI; (2) cerebral blood flow as assessed via pseudo continuous arterial spin labelling; (3) electroencephalography frequencies; or (4) cognitive (memory) function.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study identified chemical, microstructural and functional brain alterations in response to an acute non-concussive soccer heading task. These alterations appear to be subtle, with some only detected in specific regions, and no corresponding functional deficits (e.g., cognitive, adverse symptoms) observed. Nevertheless, our findings emphasise the importance of exercising caution when performing repeated non-concussive head impacts in sport.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eACTRN12621001355864. Date of registration 7/10/2021. URL https//www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=382590\u0026amp;isReview=true\u003c/p\u003e","manuscriptTitle":"The acute effects of non-concussive head impacts in sport: A randomised control trial.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-03 14:25:53","doi":"10.21203/rs.3.rs-4765251/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-08-03T05:18:44+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-30T22:14:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Sports Medicine-Open","date":"2024-07-29T08:56:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-24T00:09:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sports Medicine-Open","date":"2024-07-23T01:40:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sports-medicine-open","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"smoa","sideBox":"Learn more about [Sports Medicine-Open](http://sportsmedicine-open.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/smoa/default.aspx","title":"Sports Medicine-Open","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2d47409b-ba0c-4b79-bfe2-36d9241a3a89","owner":[],"postedDate":"September 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T16:07:59+00:00","versionOfRecord":{"articleIdentity":"rs-4765251","link":"https://doi.org/10.1186/s40798-025-00867-0","journal":{"identity":"sports-medicine-open","isVorOnly":false,"title":"Sports Medicine-Open"},"publishedOn":"2025-06-18 15:57:20","publishedOnDateReadable":"June 18th, 2025"},"versionCreatedAt":"2024-09-03 14:25:53","video":"","vorDoi":"10.1186/s40798-025-00867-0","vorDoiUrl":"https://doi.org/10.1186/s40798-025-00867-0","workflowStages":[]},"version":"v1","identity":"rs-4765251","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4765251","identity":"rs-4765251","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}