Synopsis

In this study, we identified robust differences in BOLD-CVR across the brain which were explained, in part, by hemodynamic parameters relating to CBF modulation, and resting metabolic and vascular physiology. These results emphasize that while BOLD-CVR offers promises as a surrogate biomarker for cerebrovascular health, following sport-concussion, multiple hemodynamic parameters can affect its relative measurements. Thus, multi-parametric approaches like the one proposed here should be considered, in order to better understand how head injuries can relate to changes in the vascular reactivity of the brain, post-injury, and avoid naïve interpretation of neuroimaging findings.

Introduction

The increasing rates of sport-related concussion (SRC)^1–3 have raised concerns about the long-terms effects of head injuries on brain health. In recent years, neuroimaging research has shown observable changes in network connectivity⁴ and cerebral blood flow (CBF)⁵ associated with a history of concussion reported in active collegiate athletes. Despite these findings however, no study to date has explored the possible effects of SRC history on the neurovascular coupling processes responsible for regulation of flow and proper cerebral functions, which can be probed using cerebrovascular reactivity (CVR; reviewed here^6–8) measurements.

Here, the effects of SRC history on vascular reactivity were explored using BOLD-CVR as a biomarker for vascular health. Additionally, complementary physiological modulators of the BOLD signal were studied in parallel to provide in-depth information about underlying parameters driving differences in BOLD-CVR between the groups.

Methods

A cohort of 63 collegiate football athletes with and without history of SRC were recruited to participate in this study. Of those, 32 (mean age=19±3years) reported a previous history of concussion (“HX”; range=[1,4]), and 31 age-matched athletes (mean age=19±2years) reported no history of head injury (“CTL”).

Subjects completed two 6 minutes boxcar respiratory breathing challenges (one hypercapnia, and one hyperoxia; Figure 1), induced using a feed-forward computerized gas delivery system (RespirAct^TM, Thornhill Research Inc., Toronto, ON). BOLD and CBF data were acquired simultaneously using a dual-echo pseudo-continuous arterial spin labeling sequence (pCASL)⁹: TR=4000ms, TE1/TE2=10/30ms, field of view=250x250mm, flip angle=90°, voxel size=3.9mm isotropic, post-labeling delay=1000ms, tagging duration=1.665s.¹⁰ Perfusion weighted images were extracted from the first echo (TE=10ms) using a linear surround subtraction between the control and tag images,¹¹ and converted to physiological units (mL/100g/min) using the single-blood compartment model.¹²

BOLD- and ASL-based CVR maps were calculated as the percent change in signal from baseline, divided by the magnitude change in P_ETCO₂ recorded during hypercapnia ($$$CVR={\triangle}Signal/{\triangle}P_{ET}CO_{2}$$$). Concurrent changes in BOLD and CBF were also used to model the voxelwise M parameter (representing the maximal percent change in BOLD from complete removal of deoxyhemoglobin), resting oxygen extraction fraction (OEF₀), and cerebral blood volume (CBV₀) maps (Figure 2), using the dual-calibration method^13,14 and Blockley’s model.¹⁵

Subsequent to registration into standard MNI space,¹⁷ BOLD-CVR maps were compared between the groups using an ANCOVA in AFNI.¹⁸ Significant clusters (corrected for family-wise error from multiple comparisons based on Monte Carlo stimulations 10000 iterations)^19,20 were then binarized into a region of interest (ROI), which served to extract several parameters of interest (Figure 3). M was broken down into its constituent parts,²¹ given that

$$M=A{\cdot}TE{\cdot}CBV_{0}{\cdot}B^{\beta}\cdot(1-\gamma_{v})^{\beta}$$

where A and B are constants, $$$\beta$$$ relates to vascular geometry, and $$$\gamma_{v}$$$ represents venous oxygenation. Here, we let OEF₀ equal $$$(1-\gamma_{v})$$$ as an approximation for baseline metabolic effects on M (and BOLD). $$$\kappa$$$ was set to $$$M/(CBV_{0}{\cdot}OEF_{0}^{\beta})$$$ in order to represent a lumped parameter for the effects of [Hb], vessel morphology, and intra- and extra-vascular effects on the BOLD signal.^22,23

Multiple linear and logistic regressions were then used to classify group differences in BOLD-CVR using the proposed parametrization, and identify dominant parameters driving differences in BOLD-based vascular reactivity.

Results & discussion

Voxelwise group analysis showed significantly higher BOLD-CVR (Figure 4; P<0.05 corrected) within the HX group, compared to CTLs. This may be interpreted as alterations in vascular reactivity in response to injury at the arteriolar levels.²⁵

However, follow-up ROI analysis performed using the significant voxels highlighted above (P<0.00001; CTL=0.21±0.03%$$${\triangle}$$$BOLD/mmHg; HX=0.28±0.06%$$${\triangle}$$$BOLD/mmHg) showed significant group differences in ASL-CVR (P = 0.014), and M (P = 0.022), based on SRC history (Figure 5), suggesting that both vascular and hemodynamic factors may be underlying local differences in BOLD-CVR.

Thus, further parametrization of the M parameter is useful is making meaningful interpretation of these results from BOLD-CVR. Linear regressions showed that parameters from Figure 3, along with age,²⁶ explained up to ~62% (P<0.00001) of the variance for BOLD-CVR, written as:

$$ CVR_{BOLD}=i\cdot0.5CVR_{ASL}\cdot0.9CBV_{0}\cdot1.3OEF_{0}\cdot1.0\kappa\cdot0.3age$$

,showing that metabolic effects (OEF₀) may be driving the relationship between M and BOLD, in this sample.

Logistic regressions showed that regional BOLD-CVR alone could classify the groups based on SRC history at a success rate of 74%, while the multi-parametric approach using the proposed parameters in parallel improved the classification results up to 79%. More importantly, the same parameters driving the variance in BOLD-CVR were also driving the group differences identified in the ROI, based on standardized beta coefficients computed from the logistic regressions:

$$ log-odds=i\cdot0.4CVR_{ASL}\cdot0.6CBV_{0}\cdot0.8OEF_{0}\cdot0.7\kappa.$$

These findings provide novel evidence for the effectiveness of using a comprehensive model to study the effects of head injuries on CVR, while avoiding naïve interpretation of neuroimaging findings.

Acknowledgements

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