Cerebral blood flow is associated with matrix metalloproteinase levels during the early symptomatic phase of concussion.

Concussion is associated with disrupted cerebral blood flow (CBF), although there appears to be substantial inter-individual variability in CBF response. At present, the mechanisms of variable CBF response remain incompletely understood, but one potential contributor is matrix metalloproteinase (MMP) expression. In more severe forms of acquired brain injury, MMP up-regulation contributes to CBF impairments via increased blood-brain barrier permeability. A similar relationship is hypothesized for concussion, where recently concussed individuals with higher MMP levels have lower CBF. To test this hypothesis, 35 concussed athletes were assessed longitudinally at early symptomatic injury (median: 5 days post-injury) and at medical clearance (median: 24 days post-injury), along with 71 athletic controls. For all athletes, plasma MMPs were measured and arterial spin labelling was used to measure CBF. Consistent with our hypothesis, higher concentrations of MMP-2 and MMP-3 were correlated with lower global CBF. The correlations between MMPs and global CBF were also significantly diminished for concussed athletes at medical clearance and for athletic controls. These results indicate an inverse relationship between plasma MMP levels and CBF that is specific to the symptomatic phase of concussion. Analyses of regional CBF further showed that correlations with MMP levels exhibited some spatial specificity, with greatest effects in occipital, parietal and temporal lobes. These findings provide new insights into the mechanisms of post-concussion cerebrovascular dysfunction.

Introduction

Concussion is a form of mild traumatic brain injury (TBI) that is associated with complex disturbances in brain physiology. Cerebral blood flow (CBF) has been identified as being particularly sensitive to the effects of concussion [1]. As tight control of CBF is needed to meet time-varying physiologic and neurometabolic demands, its dysregulation can have major consequences, including more severe post-concussive symptoms and prolonged recovery [2-4]. While more severe TBI is associated with major reductions in CBF at early injury [5], the effects of concussion are more variable, with studies variously reporting mean increases in CBF relative to controls [4], mean reductions in CBF [6] and non-significant differences [7] within the first week of injury. These findings suggest substantial inter-individual variability in CBF response after concussive injury. Despite its key role in concussion, the mechanisms underlying heterogeneous CBF disturbances remain incompletely understood.

One potential mechanism of altered CBF is through the up-regulation of matrix metalloproteinases (MMPs). MMPs are a family of intercellular calcium-dependent zinc-endopeptidases that play key roles in cell migration, tissue development, morphogenesis and repair [8, 9]. Under pathological conditions, such as acquired brain injury, the elevated expression of these extracellular matrix-degrading proteinases may have negative physiological effects [10]. In rodent models, MMP expression tends to be increased acutely as part of the “traumatic cascade” and it is associated with increased permeability of the blood-brain barrier (BBB), due to the breakdown of basal lamina proteins and the tight junctions between vascular endothelial cells [11-15]. Secondary effects of increased BBB permeability include cerebral leukocyte infiltration, vasogenic edema [16, 17] and an enhanced neuroinflammatory response [18, 19]. Collectively, the processes initiated by MMP up-regulation after TBI have a detrimental effect on cerebrovascular function and may similarly contribute to CBF disturbances after a concussion. To date, however, the relationship between CBF and MMP expression has been largely unexamined in humans and for milder forms of TBI.

The present study examined associations between global CBF and peripheral MMP values for a cohort of athletes with sport-related concussion, evaluated in the early symptomatic phase of injury and at medical clearance to return to play (RTP), along with a cohort of athletic controls without recent injury. For both groups, a panel of plasma MMPs were measured, and magnetic resonance imaging (MRI) was used to assess resting CBF via arterial spin labelling (ASL). This imaging technique uses magnetically-labelled arterial blood water as an endogenous tracer that tracks the delivery of oxygenated blood to brain tissues. The primary study hypothesis was that recently concussed individuals with higher MMP values would have lower global CBF values (i.e., negative inter-subject correlation between these measures). It was further hypothesized that this relationship would be specific to symptomatic injury, with diminished effects among concussed athletes at RTP and among athletic controls without recent injury. If confirmed, these results would provide evidence that the variable up-regulation of MMPs is a potential mechanism contributing to heterogeneous post-concussion CBF response.

Material and methods

Study participants

Thirty-five (35) athletes were recruited consecutively from university-level sports teams at a single institution through the sports medicine clinic, following a diagnosis of sport-related concussion. Diagnosis was determined by staff physician following sustained direct or indirect contact to the head, with assessment of clinical features as per Concussion in Sport Group guidelines [20] and neurologic assessment including examination of cranial nerves, gait, balance and gross motor function. Imaging was conducted within the first week of injury (median and interquartile range (IQR): 5 [2, 7] days) and at RTP. There was some data loss, as imaging and blood could not always be acquired concurrently. The number of participants with both imaging and blood was 27/35 at symptomatic injury and 27/35 at RTP, with 19/35 participants having data in both sessions (54% overlap). Seventy-one (71) athletes without recent concussion were also recruited consecutively at the start of their competitive season.

All athletes participating in the varsity program had completed baseline clinical assessments with the Sport Concussion Assessment Tool (SCAT, version 3 or 5) [21, 22] before the beginning of their season and all concussed athletes had follow-up SCAT assessments at early symptomatic injury and at RTP. None of the athletes had a history of neurological or psychiatric diseases or sensory/motor impairments. Recruitment and data collection were carried out between June 2015 and October 2017 and the study was conducted in accordance with the Canadian Tri-Council Policy Statement 2 and approval of University of Toronto and St. Michael’s Hospital research ethics boards, with participants giving free and written informed consent. The datasets analyzed for this study can be found in the figshare repository at https://figshare.com/s/d879d6bfb929808835ad.

Matrix metalloproteinase data

For both groups, plasma MMPs were measured, including MMP-1 (collagenase), MMP-2 and MMP-9 (gelatinases), MMP-3 and MMP-10 (stromelysins). This panel includes MMPs primarily localized to the endothelium and identified as relevant to vascular injury and TBI pathogenesis [23, 24]. Blood was drawn proximal to neuroimaging for concussed athletes (1 [0, 3] days from imaging) and controls (1 [1, 7] days from imaging) by standard venipuncture into a 10-mL K2EDTA tube, equilibrated at room temperature for one hour, and centrifuged for two minutes using a PlasmaPrep 12TM centrifuge (Separation Technology Inc., FL, USA). After centrifugation, plasma was aliquoted and stored at -80°C until analysis. Blood draws were not performed on subjects who were knowingly symptomatic with a viral or bacterial infection, seasonal allergies, or on any medication other than birth control at the time of venipuncture. The MMPs were analyzed by immunoassay on the Meso Scale Discovery (MSD) sector imager 6000, using MSD 96-well MULTI-SPOT® technology (MSD®, Gaithersburg, MD, USA). MMP-1, -3, and -9, were assayed using the Human MMP 3-Plex Ultra-Sensitive kit, while MMP-2 and -10 were assayed using the Human MMP 2-Plex Ultra-Sensitive kit. All assays were run according to manufacturer’s instructions, with individual samples run in duplicate.

Samples were not used for analysis if they fell outside of the range of detection provided by the manufacturer, or if the coefficient of variability (CV) between duplicate samples was greater than 25%. Given these criteria, a single value was removed for MMP-9, with remaining assays providing 100% useable data. The average CV was below 5% for all markers: MMP-1 = 4.3%, MMP-2 = 3.9%, MMP-3 = 4.2%, MMP-9 = 3.6%, MMP-10 = 3.6%. To control for the presence of heavy distribution tails that vary by biomarker (skewness: MMP-1 = 1.23, MMP-2 = -2.25, MMP-3 = 2.53, MMP-9 = 1.55, MMP-10 = 5.72; kurtosis: MMP-1 = 4.03, MMP-2 = 18.08, MMP-3 = 11.67, MMP-9 = 6.40, MMP-10 = 48.96), the values for each biomarker were winsorized at the 90th percentile (2-tailed) over all subject data, concussed and control. The adjusted biomarker distributions better approximated normality (skewness: MMP-1 = 1.06, MMP-2 = 0.24, MMP-3 = 1.26, MMP-9 = 0.93, MMP-10 = 1.10; kurtosis: MMP-1 = 3.28, MMP-2 = 2.41, MMP-3 = 3.76, MMP-9 = 3.18, MMP-10 = 3.65).

Magnetic resonance imaging data

Athletes were imaged using a 3 Tesla MRI system (Magnetom Skyra) with standard multi-channel head coil. A series of structural images were acquired, including a 3D T1-weighted magnetization prepared rapid gradient echo (MPRAGE) scan to assess neuroanatomy and facilitate the alignment of ASL scans to a common template (inversion time (TI)/echo time (TE)/repetition time (TR) = 1090/3.55/2300 ms, flip angle (θ) = 8o, 192 sagittal slices, field of view (FOV) = 240x240 mm, 256x256 pixel matrix, 0.9 mm slice thickness, 0.9x0.9 mm in-plane, bandwidth (BW) = 200 Hz/px), a fluid attenuated inversion recovery (FLAIR) scan to assess for lesions and tissue edema (TI/TE/TR = 1800/387/5000 ms, 160 sagittal slices, FOV = 230x230 mm, 512x512 matrix, 0.9 mm slice thickness, 0.4x0.4 mm in-plane, BW = 751 Hz/px) and a susceptibility-weighted imaging (SWI) scan to assess for micro-hemorrhage (TE/TR = 20/28 ms, θ = 15o, 112 axial slices, FOV = 193x220 mm, 336x384 matrix, 1.2 mm slice thickness, 0.6x0.6 mm in-plane, BW = 120 Hz/px). The structural scans were inspected by an MRI technologist during imaging and later reviewed by a neuroradiologist, with clinical reporting if abnormalities were identified. No abnormalities (including signs of contusion, FLAIR hyper-intensities denoting tissue edema, or SWI hypo-intensities denoting micro-hemorrhage) were found among the study participants.

Brain maps of absolute resting CBF in grey matter were obtained for each participant using a 2D pulsed ASL imaging sequence (PICORE QUIPSS II; TE/TR = 12/2500 ms, TI1/TI1s/TI2 = 700/1600/1800 ms, θ = 90o, 14 oblique-axial slices with FOV = 256x256 mm, 64x64 matrix, 8.0 mm slice thickness with 2.0 mm gap, 4.0x4.0 mm in-plane, BW = 2368 Hz/px). For this sequence, 45 tag-control image pairs were obtained, where taking the difference in signal intensity between image pairs generates a perfusion-weighted image. A single calibration image M0 was also acquired, in order to rescale signal intensities into absolute units of flow. To control for noise and artifact that may confound CBF estimates, the data were processed after acquisition, using Analysis of Functional Neuroimages (AFNI; afni.nimh.nih.gov) software and customized in-house algorithms. Rigid-body correction of between-scan head movements was performed using AFNI 3dvolreg to align tag and control scans to the calibration image M0. To control for signal spikes, e.g., due to head motion, physiology and scanner noise, filtering of outlier tag-control pairs was performed using the protocol of Tan et al. [25], followed by smoothing the scans with AFNI 3dmerge to reduce spatial noise, using a 3D Gaussian kernel with 6 mm isotropic full width at half-maximum. Voxel-wise estimates of CBF were then obtained by taking the mean of the signal difference between all tag-control pairs, rescaled into flow units of mL/100g/min based on M0 values and established kinetic modelling parameters [7].

To compare regional CBF values between study participants, the brain maps were afterwards transformed into a common anatomical space, using the Montreal Neurological Institute MNI152 template as a reference. This was achieved using the FMRIB Software Library (FSL; https://fsl.fmrib.ox.ac.uk) and in-house scripts. For each participant, FSL flirt first computed the rigid-body alignment of their mean ASL volume to their T1-weighted image, along with the affine alignment of their T1 image to the MNI152 template. The net transform of ASL data into MNI space was then calculated with xfm_convert and applied to the CBF maps, resampled at 3 mm isotropic resolution. The study focused on cortical grey matter, to ensure a high signal-to-noise ratio with minimal partial volume effects when analyzing associations with MMPs. This was achieved using the Brainnetome Atlas (BNA V1.0; https://atlas.brainnetome.org) by constructing a binary mask that included all voxels with parcel labels 1 through 210. The CBF values were subsequently analyzed for all voxels in this mask.

Analysis of clinical and demographic data

Participant demographics are listed in Table 1, including age, sex and concussion history. Clinical scores are also reported for SCAT symptoms. A symptom severity score was obtained by summing across a 22-item symptom scale, with each item receiving a 7-point Likert scale rating. A total symptoms score was also obtained by counting all symptoms with non-zero ratings. For symptom scores, non-parametric Wilcoxon tests (1-tail) determined whether post-concussion values were significantly higher than controls. Paired-measures Wilcoxon tests (1-tail) also evaluated whether post-injury values were significantly higher than pre-injury. For all sets of statistical tests, significance was determined after adjusting for multiple comparisons at a False Discovery Rate (FDR) of 0.05.

Table 1

Demographic and clinical data for controls and concussed athletes.

	Control	Concussion
Age (mean ± SD)	20.0 ± 1.7 yrs.	20.5 ± 2.2 yrs.
Female	35/71 (49%)	18/35 (51%)
Previous concussions	29/71 (41%)	18/35 (51%)
Days to RTP	--	24 [13, 60]
Sport	Volleyball (3M/2F)	Volleyball (1F)
	Hockey (8M/17F)	Hockey (5M/5F)
	Soccer (9M/6F)	Soccer (1F)
	Football (7M)	Football (3M)
	Rugby (2M/4F)	Rugby (5M/7F)
	Basketball (4F)	Basketball (1M/1F)
	Lacrosse (6M/2F)	Lacrosse (1M/1F)
	Water polo (1M)
	--	Mountain biking (1F)
		Baseline	SYM	RTP
Total Symptoms	2 [0, 5]	3 [1, 4]	8 [4, 13]**	1 [0, 2]
Symptom Severity	4 [0, 8]	3 [1, 6]	9 [4, 28]**	1 [0, 2]

All athletes were assessed at pre-season baseline, and concussed athletes were further assessed at symptomatic injury (SYM) and return to play (RTP). Clinical scores of total symptoms and symptom severity are summarized by the median and interquartile range [Q1, Q3].

A ‘**’ denotes significant increases in symptom scores relative to baseline and controls.

Analysis of MMPs and CBF

To characterize the athlete cohorts, the mean MMP and global CBF values (i.e., averaged over all grey matter voxels) were calculated for controls and for concussed athletes at symptomatic injury and RTP, along with the bootstrapped 95% confidence intervals of the mean (95%CIs), based on 1,000 bootstrap samples. The mean differences between groups were also reported at each imaging session, along with 95%CIs and p-values, obtained from two-sample bootstrap analyses that compared the mean values of concussed and control groups. To test the main study hypothesis, Spearman correlations between MMP values and global CBF were calculated for controls and concussed athletes at symptomatic injury and at RTP, with bootstrapped 95%CIs and p-values, and significant associations were identified at an FDR of 0.05. For significant MMPs and sessions, the difference in Spearman correlation between concussed and control groups Δρ = ρconcussed - ρcontrol was also reported, with 95%CIs and p-values obtained from two-sample bootstrap analyses. Supplemental analyses examined whether the MMP-CBF correlations were confounded by demographic factors, including age, sex, prior concussion history and time post-injury. This was done by computing the partial Spearman correlation, adjusted for each covariate in turn, and testing whether there were significant changes in correlation strength, based on bootstrapped p-values. For all of the above analyses, missing data were handled using a pairwise deletion approach.

Additional analyses measured the effects of MMPs on regional CBF values. For the set of MMPs showing significant associations with global CBF, a single composite MMP score was generated by z-scoring and summing these variables. Spearman correlations were calculated between the composite MMP score and CBF for each grey matter voxel, with bootstrapped p-values. Significant brain regions were identified by thresholding at a voxel-wise p = 0.005, followed by cluster-size thresholding at an adjusted p = 0.05, using AFNI 3dFWHMx to estimate spatial smoothness of the CBF maps and running 3dClustSim to obtain the minimum cluster size threshold. This procedure obtained a “consensus map” of the brain regions where MMPs affected regional CBF. The relationship was further quantified by calculating mean CBF values within significant voxels, and regressing mean CBF onto significant MMPs, with reporting of the regression coefficient b, coefficient of determination R2 and bootstrapped 95%CIs. Regression diagnostic checks were also conducted, including plotting residuals against fitted values to assess linearity and homoscedasticity, quantile-quantile plots to assess normality of residuals and Cook’s distance to test for high-leverage outliers, none of which showed evidence of substantial deviations from modelling assumptions.

Supplemental analyses examined whether taking the mean CBF over all significant clusters provided a good representative summary of cluster-specific relationships between CBF and MMP levels. For each cluster of contiguous voxels k, the mean CBF values were taken and separately regressed against MMP values, providing cluster-specific coefficient bk. The difference score Δk = bk−b was then obtained, measuring deviation of the cluster-specific model from the all-clusters model. Bootstrap resampling estimated 95%CIs and p-values of these differences, with significantly different clusters identified at an FDR of 0.05. For cluster-specific models deviating from the all-clusters model, the difference statistics were then reported.

Results

Participant demographics are reported in Table 1 for control and concussed groups. Both cohorts are comparable in age range, proportions of male and female athletes and prior history of concussion. Concussed athletes had a median of 3–4 weeks from injury to medical clearance, which included completion of a graded exercise protocol and cognitive testing, although there was substantial variability in individual recovery times. Concussed athletes had significantly elevated total symptoms and symptom severity at early symptomatic injury, relative to their own baseline and the control cohort (p<0.001, all tests) at an FDR of 0.05. Symptom scores at RTP were no longer significantly elevated (p≥0.991, all tests), but tended to be slightly lower than baseline.

Table 2 summarizes the distributions of MMP and global CBF values for the different groups. For all parameters, the control and concussed groups had similar group means and heavily overlapped 95%CIs. Moreover, the mean differences between groups were small relative to the group means themselves, with 95%CIs that overlapped zero. None of the comparisons attained statistical significance, with all comparisons between concussed athletes and controls having p≥0.257.

Table 2

Average matrix metalloproteinase (MMP) concentrations and global cerebral blood flow (CBF).

	Control	SYM	RTP	SYM—Control	RTP—Control
MMP-1 (pg/mL x104)	3.13 [2.72, 3.56]	3.42 [2.65, 4.26]	3.41 [2.66, 4.20]	0.29 [-0.57, 1.20]	0.28 [-0.57, 1.18]
MMP-2 (pg/mL x 104)	10.65 [10.35, 10.94]	10.38 [10.02, 10.73]	10.60, [10.19, 11.02]	-0.27 [-0.72, 0.19]	-0.05 [-0.55, 0.45]
MMP-3 (pg/mL x104)	1.62 [1.39, 1.86]	1.56 [1.25, 1.91]	1.66 [1.29, 2.07]	-0.06 [-0.47, 0.38]	0.05 [-0.40, 0.53]
MMP-9 (pg/mL x104)	18.93 [16.82, 21.23]	18.98 [15.48, 23.07]	19.47 [15.75, 23.85]	0.06 [-4.12, 4.58]	0.55 [-3.60, 5.58]
MMP-10 (pg/mL x 104)	0.21 [0.19, 0.23]	0.20 [0.17, 0.24]	0.21 [0.18, 0.24]	-0.01 [-0.05, 0.03]	-0.01 [-0.04, 0.03]
CBF (mL/100g/min)	33.65 [31.68, 35.67]	33.72 [31.31, 36.57]	30.74 [27.32, 33.84]	-0.07 [-3.20, 3.46]	-2.91 [-6.67, 1.16]

The mean MMP and CBF values are reported for athletic controls and for concussed athletes at early symptomatic injury (SYM) and return to play (RTP), along with bootstrapped 95%CIs of the mean. The mean differences between concussed and control groups and 95%CIs are also reported for each post-concussion imaging session. None of the comparisons between concussed and control groups attained a value of p<0.05, uncorrected.

Fig 1 depicts correlation analyses of the MMP values against global CBF. For controls, the correlation values were consistently near zero, with relatively wide 95%CIs, indicating inter-subject variations in MMP and CBF values are unrelated within this cohort. In contrast, symptomatic concussed athletes showed larger negative correlations, with significant effects for MMP-2 and MMP-3 at an FDR of 0.05, indicating that higher MMP levels are associated with lower global CBF in this cohort (p = 0.020 and p = 0.009, respectively). Correlations within the symptomatic concussed group were also larger than controls, with MMP-2 having correlation difference Δρ = -0.419 (95%CI: -0.805, -0.011; p = 0.044) and MMP-3 having Δρ = -0.548 (95%CI: -0.879, -0.129; p = 0.008). At RTP, the effect was no longer no longer significant, with diminished correlations and wider 95%CIs (p = 0.256 and p = 0.810, respectively). Similarly, the values did not differ substantially from controls, with MMP-2 having correlation difference Δρ = -0.211 (95%CI: -0.628, 0.223; p = 0.352) and MMP-3 having Δρ = -0.078 (95%CI: -0.529, 0.370; p = 0.710). Supplemental analyses measured the Spearman partial correlations after adjusting for age, sex, concussion history and days post-injury. None of the tested demographic variables significantly altered correlation strength between MMPs and CBF (p≥0.228 for all analyses). These results indicate that associations between MMP expression and CBF are limited to MMP-2 and MMP-3 and to the early symptomatic phase of concussion.

Fig 1

Correlations between matrix metalloproteinase (MMP) concentrations and global cerebral blood flow (CBF).

Results are shown for athletic controls and for concussed athletes at early symptomatic injury (SYM) and return to play (RTP). Bars represent Spearman correlations, with error bars corresponding to bootstrapped 95%CIs. ‘**’ denotes significant correlations (i.e., 95%CIs excluding zero) at a False Discovery Rate threshold of 0.05.

For MMP-2 and MMP-3, which show significant correlations with global CBF, Fig 2 depicts their correlations with regional CBF. Fig 2A displays brain regions that have significant correlations with the (MMP-2 + MMP-3) composite score, with clusters summarized in Table 3. Significant effects were seen for extensive clusters within occipital, parietal and temporal regions. Fig 2B plots MMP-2 concentration against mean CBF, averaged over significant clusters in the brain. Linear regression obtains a coefficient b = -7.60x10-4 (95%CI: -10.48x10-4 to -5.09x10-4; p<0.001) and an R2 of 0.404 (95%CI: 0.189, 0.652), and these clusters have a Spearman correlation of -0.633 (95%CI: -0.800, -0.308). Fig 2C similarly plots MMP-3 concentration against mean CBF. Linear regression obtains a coefficient b = -8.96x10-4 (95%CI: -12.27x10-4 to -6.03x10-4; p<0.001) and an R2 of 0.513 (95%CI: 0.246, 0.729), and these clusters have a Spearman correlation of -0.639 (95%CI: -0.853, -0.283). Thus, associations between MMPs and CBF exhibit some spatial specificity, with greatest effects confined to posterior and dorsal brain regions. Supplemental analyses of cluster-specific CBF found no significant differences in regression coefficient values, compared to the all-cluster results. One cluster (cluster #8 in Table 2, right inferior frontal area) had a slightly higher coefficient value when regressing CBF against MMP-2 (p = 0.016) with a coefficient difference Δk = 3.65x10-4 (95%CI: 0.96x10-4 to 5.75x10-4) but it was non-significant at an FDR of 0.05. All other clusters showed minimal differences for MMP-2 (p≥0.278 for all other clusters), and there were no noteworthy deviations for MMP-3 (p≥0.096 for all clusters).

Fig 2

Correlation between matrix metalloproteinase (MMP) concentrations and regional cerebral blood flow (CBF).

Results are shown for concussed athletes at early symptomatic injury, and for MMPs significantly correlated with global CBF (MMP-2 and MMP-3; see Fig 1). (A) Brain regions that have significant correlations with the (MMP-2 + MMP-3) composite score. Images are maximum intensity projections, centered on MNI coordinates (x = 0, y = 0, z = 0). The average CBF values of significant brain regions are also plotted against (B) MMP-2 and (C) MMP-3 concentrations. For these plots, the regression line of best fit is in solid black, with shaded bands denoting the bootstrapped 95%CIs. The thick dashed line denotes the mean CBF value for controls, with thin dashed lines enclosing the bootstrapped 95%CI of the mean.

Table 3

Cluster report for correlations between matrix metalloproteinase (MMP) concentrations and regional cerebral blood flow (CBF).

Cluster centers of mass are given in MNI coordinates and the brain regions are identified based on the nearest labelled grey matter region in the automated anatomical labelling (AAL) atlas.

cluster	Center of mass			Brain region	Cluster size (mm3)	Peak value (Spearman ρ)
1	-9	-66	18	Calcarine L	24057	-0.73
2	51	-54	21	Middle temporal R	7317	-0.68
3	39	-54	48	Inferior parietal R	5697	-0.70
4	-60	-33	18	Superior temporal L	4860	-0.75
5	-45	-66	12	Middle temporal L	4185	-0.67
6	3	-33	39	Midcingulate R	3375	-0.70
7	-45	-12	48	Postcentral L	2106	-0.63
8	45	21	21	Inferior frontal (triang. part) R	1755	-0.61
9	-36	-45	54	Inferior parietal L	1701	-0.68

Discussion

CBF disturbances are a key component of concussion pathophysiology, however, the underlying mechanisms are incompletely understood in humans. The present study examined whether there was an inverse relationship between peripheral MMPs and global CBF among individuals with sport-related concussion. Consistent with the main study hypothesis, MMPs were negatively correlated with global CBF for recently concussed athletes. The correlations were no longer significant at RTP or for athletic controls, further indicating that the relationship is limited to the early symptomatic phase of injury. The correlations were seen in the absence of group-level differences in mean MMP and global CBF values between concussed athletes and controls, which is consistent with our understanding of concussion as a diffuse, heterogeneous form of brain injury [7, 26]. Previous studies of sport-related concussion have often found spatially limited and study-dependent CBF effects, including increases [4], decreases [6] and non-significant differences [7] relative to controls. The variable CBF response may be due to multiple factors, including differences in time post-injury [7] and in the extent of microvascular injury [19], with the latter interpretation supported by the present MMP-related findings.

Significant negative correlations between global CBF and MMP levels are consistent with the main study hypothesis that MMP expression contributes to reduced CBF post-injury. Interestingly, the effects were not present for all measured MMPs, nor were they confined to a single subtype. MMP-2 (gelatinase) and MMP-3 (stromelysin) showed significant associations with CBF, whereas MMP-1 (collagenase), MMP-9 (gelatinase) and MMP-10 (stromelysin) did not. The identification of MMP-2 is consistent with studies of ischemic injury showing its involvement at acute injury [12, 27, 28]. Conversely, although MMP-9 has been linked with traumatic injury in rodents and is associated with greater lesion volumes and motor deficits [15, 29, 30], it did not show significant associations in this study. The mechanisms by which increased MMP expression may affect CBF are multifactorial, as MMPs are associated with injury to cerebral vasculature [15] and with BBB permeability, where the subsequent release of autocoids and free radicals can impair autoregulation [31]. In terms of secondary mechanisms, injury is followed by metabolic perturbations including ischemia, hypoxia, and vasospasm, all of which may perpetuate BBB dysfunction and edema [32, 33]. In addition, the infiltration of peripheral immune cells across the dysfunctional BBB may contribute to CBF impairments, via ongoing cellular damage and neuroinflammatory response [34, 35].

It is interesting to note diminished correlation strength between MMPs and CBF at medical clearance to RTP, which was a median of 3–4 weeks post-injury. This suggests that the underlying effects of MMPs on CBF are transient in nature, unlike some mechanisms that show persistent effects on cerebral perfusion, such as inflammatory cytokines [36, 37]. These findings are also consistent with previous studies showing dissipation of concussion-related disturbance in CBF following RTP [4, 6]. However, there are also potentially chronic effects of concussion that emerge at longer time intervals post-injury. For example, previous studies of concussion in this cohort found delayed changes in frontal CBF emerging over a year post-RTP that are potentially linked to grey matter volume loss [4, 38]. Moreover, the stroke literature suggests that elevated MMPs may confer beneficial effects chronically [39]. It may therefore be important to further examine the evolution of MMPs and CBF over longer post-concussion time intervals.

Analyses correlating the MMPs with regional CBF show broadly affected territories in the brain, as expected. The most consistently affected regions are predominantly occipital, parietal and temporal. These are brain areas in which resting CBF also tends to be high, suggesting that they may be sensitive targets for investigating associations with MMP expression. However, it is also possible that this spatial distribution reflects brain regions experiencing the greatest MMP-mediated dysregulation of CBF. These regions are implicated in diverse processes including visuo-spatial processing and memory, which frequently show symptom impairments following concussion [22, 40]. Future research should therefore consider the potential links between MMP expression and specific post-concussion impairments.

This study fills a knowledge gap, but there are some limitations that should be acknowledged. One consideration is the post-injury time interval, as initial imaging and blood draws were conducted a median of 5 days post-injury. This enables comparisons with other imaging studies of concussion that are often on a similar timeline, but it is beyond the 24 hour window in which MMP expression is most pronounced [41, 42]. Moderately strong MMP-CBF correlations were identified, but given the rapidly evolving post-concussion neurometabolic cascade [43], these relationships may differ at acute injury. This must be verified before the present findings can be applied to emergency care settings. Another important consideration is demographic variability. Although the study found no significant effect of adjusting for age, sex or concussion history, there may be other factors that contribute to modelling error. For example, athletes with higher training loads and more frequent subconcussive blows may show greater CBF disruptions due to increased BBB permeability [44, 45], the consequences of which should be further investigated. Lastly, the detailed mechanisms linking MMP expression to CBF are not yet fully elucidated. At present, further investigation is required to directly assess the effects of proteolytic activity on cerebrovascular integrity, edema and neuroinflammatory response. The assessment of MMPs in peripheral blood also only indirectly reflects MMP levels in the CNS. Previous literature has established that cytosolic and membrane-bound proteins are released into the CSF and interstitial fluid after injury. From there, they can pass across the disrupted BBB or into the paravenous space, ultimately reaching the peripheral circulation [11, 46–48]. This provides a plausible mechanism relating peripheral MMP levels to brain injury. Nevertheless, the indirect nature of MMP measurements is a potential confound, as systemic vasculature is also a source of MMPs [49, 50], and MMP levels may be increased by, for example, peripheral nerve injury [51]. It is critical to validate the study findings, using animal models of injury or, alternatively, human studies that employ ventriculostomies to directly sample the CSF [52].

In sum, this study demonstrates that greater expression of peripheral MMP-2 and MMP-3 are correlated with reduced global CBF during the early symptomatic phase of concussive injury. These findings provide evidence for MMP expression as a potential contributor to the variable CBF response among concussed individuals. Although further research is needed to validate these findings, it is a critical first step that establishes a need for further research into the specific pathways by which MMPs and its natural inhibitors impact CBF regulation. Such research may lead to new insights into the mechanisms of CBF dysregulation and subsequent recovery, which are needed to improve clinical management and to develop targeted interventions, particularly for patients experiencing more severe or prolonged post-concussion impairments.

17 Mar 2021

PONE-D-21-01481

Cerebral blood flow is associated with matrix metalloproteinase levels during the acute phase of concussion

PLOS ONE

Dear Dr. Churchill,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

As editor I support the publication of the manuscript as a full paper, however all the comments of the two reviewers need to be adequately addressed. Clear justification of the selected blood markers and stating the a priori hypothesis of the work should be given.

Since PLOS criteria are explicit on the full description of the methods in such a way that the experiments can be repeated by other groups - no need to shorten the present detailed parts.

Please submit your revised manuscript by Apr 23 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Mária A. Deli, M.D., Ph.D.

Academic Editor

PLOS ONE

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"TAS, NWC and MGH were funded by the Canadian Institutes of Health Research (CIHR) [grant numbers RN356342 – 401065, RN294001–367456]. MGH was funded by the Canadian Institute for Military and Veterans Health Research (CIMVHR) [grant number W7714-145967]. TAS and NWC were funded by Siemens Healthineers Canada."

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I appreciate the time and opportunity to review this manuscript. While I found some interesting information in your manuscript, I do not think the findings are robust enough to warrant publication in this form. Since you have published analyses from this dataset previously, the analysis included in this submission might be better as a letter to the editor or a short report for the journal where the dataset was originally published. I do, however, have some comments and questions that may be helpful to you in the future.

1. In a few areas, you mention "baseline" (line 212) levels or evaluations and "pre-injury" (line 178) values. It is unclear how these are measured given the description of how you enrolled your subjects.

2. There are asterisks for Table 1, and it is unclear what these represent.

3. The use of arterial spin labeling is interesting, but I believe the description in the methods section is very technical and is laborious for the reader. Perhaps using a description that is easier to decipher may help not to lose your audience.

4. There are several "hypotheses" listed in the introduction, and I believe that one should be focused on, using it to power the study according to a primary outcome, as you have both control and treatment groups--otherwise, this is an exploratory observational study. In other words, it was unclear to me what your actual objectives were a priori.

5. line 77: I would not say that "human studies preclude. . ." as patients with severe TBI (not concussion, albeit) may have ventriculostomies and CSF sampling is possible for these biomarkers. More difficult, yes, but possible.

6. line 106: I believe the word "of" after "Imaging" could be omitted.

7. line 107-108: I find that lack of concurrent imaging and blood samples a weakness as well as the timing of the blood sampling. You mention later in the discussion that "hyperacute" sampling showed higher levels of MMP, which I think is quite clinically relevant as usually concussed patients do not stay in the hospital and would just be seen in the ED and then leave.

8. Question: Why did you choose the MMPs you chose? Also, did you consider utilizing other tight junction proteins such as occludin to confirm previously published findings. I believe that the timing of your sampling and perhaps the heterogeneity of the subjects (?) may have contributed to some findings that may lead to erroneous conclusions.

Reviewer #2: Churchill et al. investigate the correlation between concussion and peripheral blood serum MMP levels. Multiple types of MMPs were studied along with CBF imaging in the paper finding some correlation. The authors have wide experience in investigating the effects of concussion on multiple aspects of sport performance and brain functions. The paper is interesting for the field, but needs a few clarifications.

In general the acute phase of TBI is considered to be 24h after the concussion, but maximum 3-4 days in more severe cases. Although literature varies on this aspect. Therefore I would discuss the matter how the classification of acute vs. non-acute TBI is assessed more in-depth. If in this discussion it is confirmed that the used median days of the analysis here (5 days) is not considered to be "acute", I would recommend to eliminate the word "acute" from the title and include this as a limitation of the study.

I would find it necessary to show the CBF of brain regions separately and not merged, in which significant changes were found. To me it seems that in Figure 2B all regions where significant change in CBF was found, MMP and CBF values are merged to one figure. Although since part of the results and the discussion is based on the observation of spatial differences, the authors should really show the different brain regions separately, where significant change is supposed. This would increase the complexity of the paper and also would provide a more wider conclusion.

Additional comments:

- Please comment on why baseline "Symptom severity" and "Total Symptoms" are higher than RTP in Table 1.

- Reference 4 and 6 are the same, or one of them is cited wrong, please correct.

After the requested extra discussion and data analysis is performed, I will recommend the paper for publication.

**********

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Reviewer #1: No

Reviewer #2: No

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26 Apr 2021

Overall response: we would like to thank the reviewers for their helpful comments and suggestions. In addressing these points, we believe the clarity and scientific value of the manuscript to be significantly improved. Responses to individual reviewer points are provided below, and major changes to the manuscript text are highlighted in yellow.

REVIEWER#1

Comment 1.1: In a few areas, you mention "baseline" (line 212) levels or evaluations and "pre-injury" (line 178) values. It is unclear how these are measured given the description of how you enrolled your subjects.

Response: We apologize for the lack of clarity. All athletes participating in the varsity program had mandatory pre-season baseline clinical assessments which included administration of the SCAT. We have revised the Methods section to clarify this (p. 5, ln.107-110).

Comment 1.2: There are asterisks for Table 1, and it is unclear what these represent.

Response: This indicates post-concussion time points where symptom scores are significantly elevated relative to both controls and concussed athletes’ own baseline. We have amended the caption to state this.

Comment 1.3: The use of arterial spin labeling is interesting, but I believe the description in the methods section is very technical and is laborious for the reader. Perhaps using a description that is easier to decipher may help not to lose your audience.

Response: We have revised this section substantially to make the steps in ASL acquisition and processing clearer to a non-expert reader (p. 7-8, ln. 153-180).

Comment 1.4: There are several "hypotheses" listed in the introduction, and I believe that one should be focused on, using it to power the study according to a primary outcome, as you have both control and treatment groups--otherwise, this is an exploratory observational study. In other words, it was unclear to me what your actual objectives were a priori.

Response: Our main hypothesis is that among recently concussed individuals, those with higher MMP values will have lower global CBF values (i.e., a negative inter-subject correlation), indicating that MMP levels are related to cerebrovascular function. As a secondary hypothesis, it was predicted that the effect would be larger at early symptomatic injury, compared to both concussed athletes at RTP and uninjured controls, indicating that the effect is specific to the early phase of injury. We have revised the Introduction to focus on the main study hypothesis, as suggested (p. 4, ln. 87-91); we have also modified the Abstract (p. 2) and Discussion (p. 16, ln. 324-329; p. 17, ln. 344-345) to reinforce to the reader that this is the primary hypothesis.

Comment 1.5: line 77: I would not say that "human studies preclude. . ." as patients with severe TBI (not concussion, albeit) may have ventriculostomies and CSF sampling is possible for these biomarkers. More difficult, yes, but possible.

Response: This is an excellent point. We have amended the introduction to note that this is challenging, not impossible (p. 4, ln. 74-75). We have also added Discussion text (p. 20, ln.409-411) noting that this is a potential avenue for follow-up studies seeking to validate the present findings.

Comment 1.6: line 106: I believe the word "of" after "Imaging" could be omitted.

Response: This has been corrected.

Comment 1.7: line 107-108: I find that lack of concurrent imaging and blood samples a weakness as well as the timing of the blood sampling. You mention later in the discussion that "hyperacute" sampling showed higher levels of MMP, which I think is quite clinically relevant as usually concussed patients do not stay in the hospital and would just be seen in the ED and then leave.

Response: We agree that time of imaging and blood draws are important considerations. The present timeline is consistent with neuroimaging studies of sport-related concussion, allowing us to compare findings. As a study strength, the presence of significant correlations between MMPs and CBF indicates a relatively robust relationship that lasts beyond the acute window of injury. However, this may lead to under-estimation of effect sizes. Furthermore, generalization of these findings to the ER setting and early acute injury should be done with caution until replicated with data collected in this patient cohort. We have added these points to the discussion (p. 19, ln. 381-396).

Comment 1.8: Why did you choose the MMPs you chose? Also, did you consider utilizing other tight junction proteins such as occludin to confirm previously published findings. I believe that the timing of your sampling and perhaps the heterogeneity of the subjects (?) may have contributed to some findings that may lead to erroneous conclusions.

Response: The panel was selected, as it includes MMPs localized mainly to the vascular endothelium and previously identified as being relevant to vascular injury and TBI pathogenesis. This has been added into the Introduction text (p. 4, ln. 84-86). Regarding the potential investigation of other tight junction proteins, we now note in the Discussion that this will be an important next step in future work to better understand the mechanistic pathways (p. 19, ln.400-401). We have also added text discussing the limitations of our imaging timeline, and potential sources of demographic heterogeneity (p. 19, ln. 381-396). Although the present study found no significant impact of adjusting for age, sex and concussion history, there are likely other unmodeled sources of heterogeneity contributing to MMP and CBF variations, such as training load and recent subconcussive blows.

REVIEWER#2

Comment 2.1: In general the acute phase of TBI is considered to be 24h after the concussion, but maximum 3-4 days in more severe cases. Although literature varies on this aspect. Therefore I would discuss the matter how the classification of acute vs. non-acute TBI is assessed more in-depth. If in this discussion it is confirmed that the used median days of the analysis here (5 days) is not considered to be "acute", I would recommend to eliminate the word "acute" from the title and include this as a limitation of the study.

Response: Since clinical and biomarker literature adheres more consistently to the 24-48 hr definition of acute injury, and our median time post-injury is 5 days, we have changed the terminology to “symptomatic injury” throughout the manuscript to avoid any potential confusion. We have also amended the discussion to note that different MMP-CBF relationships may be identified in the acute 24hr post-injury window. This warrants further investigation, particularly if the goal is to translate the present findings into emergency care settings (p. 19, ln. 381-396).

Comment 2.2: I would find it necessary to show the CBF of brain regions separately and not merged, in which significant changes were found. To me it seems that in Figure 2B all regions where significant change in CBF was found, MMP and CBF values are merged to one figure. Although since part of the results and the discussion is based on the observation of spatial differences, the authors should really show the different brain regions separately, where significant change is supposed. This would increase the complexity of the paper and also would provide a more wider conclusion.

Response: We have conducted supplemental analyses examining whether taking the mean CBF value over all clusters yielded a good representative summary of cluster-specific associations between CBF and MMPs. For each of the contiguous clusters, we compared the cluster-specific regression coefficients to the all-clusters regression coefficient within a bootstrap resampling framework. None of the clusters deviated significantly at an FDR of 0.05, indicating that the overall mean was a good representation. One cluster did deviate at a nominal p<.05 uncorrected, and for completeness these results are reported in text. We have modified the Methods (p. 10, ln. 219-226) and Results (p. 14, ln. 291-297) to provide these details.

Comment 2.3: Please comment on why baseline "Symptom severity" and "Total Symptoms" are higher than RTP in Table 1.

Response: Although the difference is modest, this is consistent with previous publications in this cohort and prior reporting trends in the clinic. We have previously shown that this trend is correlated with improved mood state and reduced fatigue, likely due to a combination of anticipating return to play and being more physically rested at this time [Hutchison et al. (2017). JHTR, 32(3), E38-E48.] This has been noted in the Results text (p. 11, ln. 235-238).

Comment 2.4: Reference 4 and 6 are the same, or one of them is cited wrong, please correct.

Response: We have corrected the error.

Submitted filename: reviewer_response_1.5.docx

Click here for additional data file.

19 May 2021

PONE-D-21-01481R1

Cerebral blood flow is associated with matrix metalloproteinase levels during the early symptomatic phase of concussion

PLOS ONE

Dear Dr. Churchill,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

The manuscript has been greatly amended, there are two question related to the  scientific part which should be answered. All the other changes suggested by the reviewer are related to the structure and style of the manuscript that still need to be improved.

==============================

Please submit your revised manuscript by Jul 03 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Mária A. Deli, M.D., Ph.D.

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I appreciate your attention to addressing the original comments in this manuscript. I still feel there are some style points and improvements that can be made to help this manuscript with its legibility.

I think the most significant revision in this manuscript needed is with regard to wordsmithing and I only have two questions with regard to the science.

Science questions:

1. I just don't love Figure 2B and Figure 2C. Visually there does not appear to be good correlation. I also wonder if you think there is a possibility of ASL not being a good test to use in this setting? Is there a possibly limitation to the method by which you calculated CBF and could that be a reason there is not great correlation?

2. It was unclear to me how the two MRIs had by the subjects were used together to get the data. Did all subjects have two MRIs?

Most of these are style points which I believe will make this paper more clear and of a higher quality for potential readers.

-Paragraph 3 may not need to be in the introduction; this information may need to be only in the discussion or be omitted

-Lines 83-87 likely belong in the methods.

-I know you improved the jargon with regard to ASL, but I believe it is only mentioned in the introduction now in the lines listed above, and not really explained prior to being mentioned.

-Having said that, the entirety of the methods if prone to technical jargon, and I understand that in some ways this cannot be avoided, but if possible, if you can simplify ANY of it, it will be a better read manuscript.

-Line 232: I recommend to remove "As anticipated" as this expresses conjecture, which usually is reserved for the discussion portion of the paper

-Line 234: similarly, I recommend removing "In contrast" simply because it is unnecessary.

-Lines 236-238 likely belong in the discussion as to "why?".

-Lines 249-250: there are points that belong in the discussion here

-Lines 287, 289: remove the word "moderate." It provides interpretation of results which belong in the discussion.

-Finally, in general, the discussion is very long and wordy. I would try to limit it to 5 paragraphs (currently 8), and leave out anything not pertinent to discussing your findings, with the last two paragraphs reserved for limitations and conclusions.

Reviewer #2: Authors have addressed all my comments and concerns. They modified the title according to my recommendation, that 5-days post-concussion should not be addressed as an "acute injury". I think this significantly helps readers to orientate which period after TBI was investigated. Methods were described more in detail to provide a more clear understanding of the data.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

22 May 2021

Overall response: we would again like to thank the reviewers for their detailed and helpful commentary, which we believe to have significantly improved the quality of the submitted manuscript. Specific responses to the remaining reviewer comments are provided below, and major changes in the manuscript text are highlighted in yellow.

REVIEWER #1

Comment 1: I just don't love Figure 2B and Figure 2C. Visually there does not appear to be good correlation. I also wonder if you think there is a possibility of ASL not being a good test to use in this setting? Is there a possibly limitation to the method by which you calculated CBF and could that be a reason there is not great correlation?

Response: Based on detailed investigation, the relationships plotted in Fig. 2B-C appear to be statistically valid. Rank-based spearman correlation with bootstrapped CIs found stable, moderately high correlations of approximately -0.63 for both MMP-2 and MMP3; this information has been added to the Results text (p. 14). Although there has been limited examination of associations between neuroimaging and serum biomarkers in concussion to date, these correlations are comparable in strength to the few extant ones (e.g., Kawata et al. (2020). Frontiers in neurology, 11; Marchi et al. (2013). PloS one, 8(3), e56805). In addition, regression diagnostics find no evidence of deviations from linearity or normality based on residual and QQ plots, nor evidence of high-leverage outliers based on the cook’s distance criterion (see figure R1 in the appended .pdf version of our response). We have added Methods text noting that these tests were performed (p. 10).

Regarding the use of ASL to quantify CBF, it is a recognized protocol that has been validated against the “gold standard” of 15O-water PET. It shows good validity, with the added benefit that it uses arterial blood as an endogenous contrast agent and does not require any injections or exposing patients to ionizing radiation [see, e.g., Alsop, et al. (2015). Magnetic resonance in medicine, 73(1), 102-116.]. Although there are trade-offs with alternative CBF techniques (PET, SPECT, FNIRS) a detailed review is beyond the scope of this paper, particularly given the reviewer’s request to keep the discussion as concise as possible.

Comment 2: It was unclear to me how the two MRIs had by the subjects were used together to get the data. Did all subjects have two MRIs?

Response: We apologize for the lack of clarity. At a given imaging session, in addition to the ASL scan, all participants had a series of structural scans that served to characterize brain anatomy and rule out abnormalities that would complicate analysis or indicate more severe pathology. This included T1-weighted imaging to evaluate neuroanatomy, fluid attenuated inversion recovery (FLAIR) to assess for lesions and edema (appearing as hyperintensities) and susceptibility-weighted imaging (SWI) to check for signs of microhemorrhage (appearing as hypo-intense spots). This has been clarified in the Methods text (p. 7).

Comment 3: Paragraph 3 may not need to be in the introduction; this information may need to be only in the discussion or be omitted

Response: We have removed this paragraph from the Introduction text.

Comment 4: Lines 83-87 likely belong in the methods.

Response: We have moved this text to the appropriate Methods section (p. 5).

Comment 5: I know you improved the jargon with regard to ASL, but I believe it is only mentioned in the introduction now in the lines listed above, and not really explained prior to being mentioned.

Response: We have modified the Introduction to clarify what ASL is measuring (p. 4).

Comment 6: Having said that, the entirety of the methods if prone to technical jargon, and I understand that in some ways this cannot be avoided, but if possible, if you can simplify ANY of it, it will be a better read manuscript.

Response: We have made further minor edits to the Methods imaging section for clarity (p. 7-8), but we are somewhat limited by the need to provide sufficient detail about imaging and data processing that study findings can be replicated in future research.

Comment 7: Line 232: I recommend to remove "As anticipated" as this expresses conjecture, which usually is reserved for the discussion portion of the paper

Response: This line has been removed from the text

Comment 8: Line 234: similarly, I recommend removing "In contrast" simply because it is unnecessary.

Response: This line has also been removed from the text.

Comment 9: Lines 236-238 likely belong in the discussion as to "why?".

Response: This has been removed from the text, as it is not sufficiently important to justify expanding the discussion text.

Comment 10: Lines 249-250: there are points that belong in the discussion here

Response: This has been removed from the text, as it is not sufficiently important to justify expanding the discussion text.

Comment 11: Lines 287, 289: remove the word "moderate." It provides interpretation of results which belong in the discussion.

Response: This term has been removed from the Results text.

Comment 12: Finally, in general, the discussion is very long and wordy. I would try to limit it to 5 paragraphs (currently 8), and leave out anything not pertinent to discussing your findings, with the last two paragraphs reserved for limitations and conclusions.

Response: We have made significant efforts to reduce text in this section, by condensing and combining of the first 2 paragraphs (p. 16), and also by condensing and combining the 2 limitations paragraphs (p. 18), reducing the size of the discussion substantially.

Submitted filename: reviewer_response2_1.0.docx

Click here for additional data file.

31 May 2021

Cerebral blood flow is associated with matrix metalloproteinase levels during the early symptomatic phase of concussion

PONE-D-21-01481R2

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Reviewer 2 was not available to re-review the manuscript the second time. Therefore I have checked the review and the  second revision of the manuscript. All the remaining comments were addressed and the manuscript changed as suggested.

Reviewers' comments:

7 Jun 2021

PONE-D-21-01481R2

Cerebral blood flow is associated with matrix metalloproteinase levels during the early symptomatic phase of concussion

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