Cerebral blood flow dynamics: Is there more to the story at exercise onset?

Author:

Ashley John123ORCID,Shelley Joe3,Song Jiwon3,Sun Jongjoo3,Larson Rebecca D.4,Larson Daniel J.5ORCID,Berkowitz Ari6ORCID,Yabluchanskiy Andriy7ORCID,Kellawan J. Mikhail3ORCID

Affiliation:

1. Institute for Exercise and Environmental Medicine Texas Health Presbyterian Hospital Dallas Texas USA

2. Department of Neurology and Neurotherapeutics University of Texas Southwestern Medical Center Dallas Texas USA

3. Department of Health and Exercise Science, Human Circulation Research Laboratory University of Oklahoma Norman Oklahoma USA

4. Department of Health and Exercise Science, Body Composition and Physical Performance Research Laboratory University of Oklahoma Norman Oklahoma USA

5. Department of Health and Exercise Science, Sport, Health, and Exercise Data Analytics Laboratory University of Oklahoma Norman Oklahoma USA

6. Department of Biology and Cellular and Behavioral Neurobiology Graduate Program University of Oklahoma Norman Oklahoma USA

7. Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Neurosurgery University of Oklahoma Health Sciences Center Oklahoma City Oklahoma USA

Abstract

AbstractA monoexponential model characterizing cerebral blood velocity dynamics at the onset of exercise may mask dynamic responses by the cerebrovasculature countering large fluctuations of middle cerebral artery blood velocity (MCAv) and cerebral perfusion pressure (CPP) oscillations. Therefore, the purpose of this study was to determine whether the use of a monoexponential model attributes initial fluctuations of MCAv at the start of exercise as a time delay (TD). Twenty‐three adults (10 women, 23.9 ± 3.3 yrs; 23.7 ± 2.4 kg/m2) completed 2 min of rest followed by 3 mins of recumbent cycling at 50 W. MCAv, CPP, and Cerebrovascular Conductance index (CVCi), calculated as CVCi = MCAv/MAP × 100 mmHg, were collected, a lowpass filter (0.2 Hz) was applied, and averaged into 3‐second bins. MCAv data were then fit to a monoexponential model [ΔMCAv(t) = Amp(1 – e−(t−TD)/τ)]. TD, tau (τ), and mean response time (MRT = TD + τ) were obtained from the model. Subjects exhibited a TD of 20.2 ± 18.1 s. TD was directly correlated with MCAv nadir (MCAvN), r = −0.560, p = 0.007, which occurred at similar times (16.5 ± 15.3 vs. 20.2 ± 18.1 s, p = 0.967). Regressions indicated CPP as the strongest predictor of MCAvN ( = 0.36). Fluctuations in MCAv were masked using a monoexponential model. To adequately understand cerebrovascular mechanisms during the transition from rest to exercise, CPP and CVCi must also be analyzed. A concurrent drop in cerebral perfusion pressure and middle cerebral artery blood velocity at the start of exercise forces the cerebrovasculature to respond to maintain cerebral blood flow. The use of a monoexponential model characterizes this initial phase as a time delay and masks this large important response.

Publisher

Wiley

Subject

Physiology (medical),Physiology

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