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. 2019 Sep;39(9):1737-1749.
doi: 10.1177/0271678X18766771. Epub 2018 Mar 21.

Cerebrovascular blood oxygenation level dependent pulsatility at baseline and following acute exercise among healthy adolescents

Affiliations

Cerebrovascular blood oxygenation level dependent pulsatility at baseline and following acute exercise among healthy adolescents

Athena E Theyers et al. J Cereb Blood Flow Metab. 2019 Sep.

Abstract

Arterial stiffness is linked to cerebral small vessel damage and neurodegeneration, but barriers to accessing deep cerebrovascular anatomy limit our ability to assess the brain. This study describes an adaptation of a cardiac-related scrubbing method as a means of generating blood oxygenation level-dependent pulsatility maps based on the cardiac cycle. We examine BOLD pulsatility at rest, based on the non-parametric deviation from null metric, as well as changes following acute physiological stress from 20 min of moderate-intensity cycling in 45 healthy adolescents. We evaluate the influence of repetition time (TR) and echo time (TE) using simulated and multi-echo empirical data, respectively. There were tissue-specific and voxel-wise BOLD pulsatility decreases 20 min following exercise cessation. BOLD pulsatility detection was comparable over a range of TR and TE values when scan volumes were kept constant; however, short TRs (≤500 ms) and TEs (∼14 ms) acquisitions would yield the most efficient detection. Results suggest cardiac-related BOLD pulsatility may represent a robust and easily adopted method of mapping cerebrovascular pulsatility with voxel-wise resolution.

Keywords: Functional magnetic resonance imaging; aerobic exercise; blood oxygenation level dependent signal; cardiac cycle; cerebrovascular pulsatility; physiological fluctuations.

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Figures

Figure 1.
Figure 1.
Schematic diagram of the resorting method with (a) BOLD temporal volumes matched to the pulse oximeter and sorted based on cardiac cycle position. (b) Sample time series resorted by cardiac cycle and fit with a 7-term Fourier series. The R2 of this fit is then (c) compared to a randomly generated distribution of 45,000 null fits. R2 values greater than five standard deviations (green line) from the null distribution mean were considered pulsatile. The black line indicates the R2 value of cardiac cycle position sorted example in (b).
Figure 2.
Figure 2.
Mean pulsatility map from all participants. The scale indicates the number of deviations away from the goodness of fit is for the null fit distribution.
Figure 3.
Figure 3.
Average percentage of pulsatile voxels in each tissue category with standard error and multiple comparison-corrected p-values; *p < 0.05, **p < 0.01; RS: resting state.
Figure 4.
Figure 4.
Permutation-based voxel-wise results comparing of baseline and post-exercise BOLD pulsatility maps, showing a decrease in pulsatility after exercise, using a corrected p-value <0.05 in (a) resting state and (b) task scans.
Figure 5.
Figure 5.
(a) BOLD pulsatility deviations from null for each simulated scenario, plotted according to heart rate, TR and data cleaning method: uncorrected, high-pass filter and RETROICOR. (b) The simulation results show a dramatic TR dependence when the number of volumes at each TR is not held constant. Larger markers indicate means for each scenario.
Figure 6.
Figure 6.
Mean percentage of pulsatile voxels in each of the tissue categories with standard error and p-values that have been adjusted for multiple comparisons using Holm’s method; **p < 0.01.

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