Cool head, hot brain: cerebral blood flow distribution during exercise
Bibliographic record
Abstract
Global cerebral blood flow (CBF) increases during moderate exercise intensities, yet despite progressive increases in neuronal activity, CBF declines toward baseline values when exercise intensity is >60%. This reduced CBF is attributed to cerebral vasoconstriction secondary to hyperventilation-induced hypocapnia. Indeed, the cerebral circulation's profound sensitivity to arterial () is well established at ∼3–5%ΔCBF per mmHg Δ. Nonetheless, regional distribution of these CBF changes is scantily described, but is probably affected by regional increases in neuronal metabolism, and consequent elevations in regional brain perfusion (reviewed in Ogoh & Ainslie, 2009). MRI studies in animals following exercise indicate a regional increase in blood flow to active brain areas. No studies have been completed in humans, however. Moreover, it is not possible to measure regional blood flow distribution during exercise using MRI; an alternative method is required to elucidate these questions. In this issue of The Journal of Physiology is an impressive study by Sato et al. (2011), where near-concurrent ultrasound measurements were made of blood flow () through the internal (ICA), external (ECA) and common (CCA) carotid arteries, as well as the vertebral artery (VA) during varying intensities of exercise up to 80% of . This group previously introduced the method of assessing regional CBF distribution via neck-artery flow quantification – a compendious solution for the estimation of regional brain blood flow delivery. The formidably challenging nature of ascertaining these metrics during high-intensity exercise is of itself a meritorious achievement. With this elegant experimental design the authors demonstrated that while the increase in plateaued at 40%, and began to decrease at 60%, rose steadily until 60% followed by an abrupt ∼40% increase. Moreover, this increase in was inversely related to the decrease in flow, and proportional to forehead skin vascular conductance. Also consistent with the authors’ previous study (Sato & Sadamoto, 2010), the plateau of at 40% of was reflected in a continuous elevation in with progressive exercise intensities. These data corroborate the regional differences in brain blood flow with a new modality at a new site of flow measurement (the neck arteries versus brain tissue or intracranial arteries), and importantly also show the dynamic distributive nature of intra- versus extra-cranial blood circulation. In the context of the disproportionate increase in , and the apparent shunting of blood to the extracranial circulation through the ECA, a number of relevant questions arise. (1) Regional CBF during exercise. Sato et al. (2011) have confirmed their earlier report (Sato & Sadamoto, 2010) of a greater flow increase with exercise in the VA than in the ICA, a finding consistent with downstream posterior and anterior cerebral artery velocities during similar exercise tasks (Willie et al. 2011). However, in all these studies, data were presented in terms of their relative (i.e. percentage) change from pre-exercise; when conveyed as absolute increases, it is the ICA and anterior cerebral circulation that exhibits a proportionally larger flow increase with exercise than the VA and posterior cerebral circulation. This raises a fundamental question of how to correctly interpret flow-velocity changes in different vessels with disparate baseline flows or velocities. As resting is approximately one third that of , any increase in flow during exercise will manifest in a disproportionately large percentage increase in compared to . The converse argument is that a larger vessel (i.e. ICA) supplying a larger tissue mass is bound to elicit a bigger absolute increase than its smaller neighbour (i.e. VA). Regardless, this analytical problem remains because depending on interpretation, either the posterior cerebral circulation experiences a larger increase in CBF with exercise (relative data) or the anterior circulation does (absolute data). Notwithstanding their interpretation, these data indicate disparate flow regulation during dynamic exercise within the microvasculature of the brain tissue supplied by the VA and ICA; however, it is not clear why this difference manifests. Indeed, it is equivocal why, when neuronal activation is presumably still increasing (Ogoh & Ainslie, 2009), CBF and begin to decrease back to near-resting levels. Quantification of brain perfusion during exercise, normalized or scaled to regional mass, is needed, but will be difficult to accomplish with current technologies; MRI precludes large-muscle exercise-coincident measures, and PET scanning gives a metric of blood flow only secondary to measures of substrate metabolism. (2) A proximal resistor for the cerebral circulation? It is interesting to note the near-perfect correlation reported by Sato and co-workers between and ICA and middle cerebral artery blood velocity (MCA ) during exercise. In contrast, there was no relationships between with or . Given that there is evidence for similar reactivity between the MCA and posterior cerebral artery, these findings may suggest the ICA as an additional site of CO2 sensitivity. Certainly the inverse relationship between Δ and Δ (R2= 0.59) implicates the extracranial circulation in the attenuated CBF during exercise (Sato et al. 2011); however, that both and MCAv were very closely related to PaCO2 (and ) indicates that ∼40% of the exercise-induced decrease in CBF is not simply due to extracranial steal nor, by extension, to thermoregulation. Indeed, it has been reported that heat removal from the brain is principally facilitated by CBF (Nybo et al. 2002); thus, the shunting of blood away from the brain toward the extracranial circulation to defend head thermoregulation during exercise seems a teleologically untenable explanation. Another possibility, however, is that distinct mechanisms of flow regulation in the brain and extracranial tissues explain the different pattern of flow response in the vessels supplying blood flow to the head. The data presented by Sato et al cannot elucidate the specific site of cerebrovascular resistance, nor explicate the interplay of competing or disparate regulatory mechanisms in the intra- versus extra-cranial circulation. Resistance (R) in every vessel was determined by R= pressure/flow, and because the numerator is the same for each vessel, the ostensible resistance value is dependent only on flow. Dogma contends the site of cerebrovascular resistance lies downstream of the MCA at the arteriolar pial vessels (Kontos et al. 1978). Interestingly, there was a non-significant trend for increasing mean diameter with exercise in all vessels except the ICA. Diameter was caliper-measured only during systole and diastole, and mean-weighted for 1/3-systole and 2/3-diastole over 10–20 cardiac cycles. Perhaps with future advances in ultrasonic vessel diameter measurement, these minute differences can be evaluated, as diameter maintenance in a supposedly pressure-passive vessel could indicate a proximal and – perhaps – CO2-sensitive resistor for the cerebral circulation. Nonetheless, Sato et al have established a technique that will no doubt continue to provide neoteric insight into CBF regulation, particularly in circumstances where other imaging modalities are not practicable. But perhaps future development of these imaging modalities may elucidate mechanisms for a seemingly nonsensical redistribution of blood away from the brain during high-intensity exercise. C.K.W. is funded by an NSERC CGS Doctoral Scholarship; P.N.A. is funded by NSERC and CIHR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".