CBF Methodology

Question 1

Why did the SRC group demonstrate a larger baseline to steady-state increase in HR and MCAv compared to the HC group?

Answer 1

The SRC group showed a larger increase in HR and MCAv from baseline to steady-state because they were exercising at a higher workload. This difference in workload can be attributed to the greater weight of individuals in the SRC group compared to the HC group. The Buffalo Concussion Bike Test (BCBT) prescribes power outputs based on participant weight (Haider et al., 2019). Therefore, the heavier SRC group participants were required to exercise at higher power outputs, leading to a larger increase in HR and MCAv. Furthermore, HR and MCAv have been shown to be reliably related to exercise intensity, demonstrating a dose-dependent relationship (Smith & Ainslie, 2017). This dose-dependent relationship accounts for the observed between-groups difference in HR and MCAv changes.

Question 2

How does the dose-dependent relationship between exercise intensity and HR/MCAv changes relate to the prescribed power outputs in the Buffalo Concussion Bike Test (BCBT)?

Answer 2

The dose-dependent relationship between exercise intensity and HR/MCAv changes is directly related to the prescribed power outputs in the BCBT. The BCBT protocol determines power outputs based on participant weight (Haider et al., 2019). As a result, heavier individuals, such as those in the SRC group, are required to exercise at higher power outputs. These higher power outputs lead to a greater increase in exercise intensity, which, in turn, causes a larger increase in HR and MCAv due to the dose-dependent relationship (Smith & Ainslie, 2017). This relationship has been well-established in the literature, with studies consistently demonstrating that higher exercise intensities elicit greater changes in HR and MCAv (Querido & Sheel, 2007). Therefore, the dose-dependent relationship between exercise intensity and HR/MCAv changes is a key factor in explaining the larger baseline to steady-state increases observed in the SRC group compared to the HC group.

Question 3

You attributed the larger MCAv increase from baseline to steady-state to the SRC group exercising at a higher workload due to their greater weight. Can you discuss any alternative explanations for this finding and how they might be explored in future research?

Answer 3

• While the difference in exercise workload due to the greater weight of the SRC group is a plausible explanation for the larger increase in HR and MCAv, another possibility is that the SRC group may have experienced alterations in autonomic nervous system function due to the concussion, which could have influenced their cardiovascular and cerebrovascular responses to exercise.

To explore these alternative explanations, future research could include measures of baseline fitness (e.g., VO2max) and autonomic function (e.g., heart rate variability) to better understand the factors contributing to the group differences in HR and MCAv responses to exercise.

Question 4

How might future studies control for individual differences in body weight or fitness level to ensure that observed differences in physiological responses are primarily due to concussion status rather than confounding factors?

Answer 4

• To control for individual differences in body weight or fitness level and ensure that observed differences in physiological responses are primarily due to concussion status, future studies could employ several strategies.
• First, researchers could match SRC and HC participants based on body weight, body mass index (BMI), or other anthropometric measures. This approach would help to minimize the influence of body weight on exercise workload and the resulting physiological responses.
• Second, studies could assess participants’ fitness levels using standardized tests, such as graded exercise testing or submaximal fitness tests, and match SRC and HC groups based on their fitness profiles.
• Third, researchers could consider using relative exercise intensities, such as a percentage of maximal heart rate or maximal aerobic capacity (VO2max), rather than absolute workloads. This approach would help to ensure that participants are exercising at similar relative intensities, regardless of their body weight or fitness level.

Finally, statistical analyses could include body weight, BMI, or fitness level as covariates to adjust for their potential confounding effects on physiological responses. By implementing these strategies, future studies can more confidently attribute observed differences in physiological responses to concussion status, rather than confounding factors related to body weight or fitness level

Question 5

You mentioned that the SRC group’s exercise intervention occurred beyond the timeframe of the neurometabolic cascade CBF reduction. How might the timing of the exercise intervention relative to the injury influence the findings, and what are the implications for future research on exercise and concussion recovery?

Answer 5

• The timing of the exercise intervention relative to the injury is an important consideration when interpreting the findings of this study. By conducting the exercise intervention beyond the typical timeframe of the neurometabolic cascade CBF reduction (6-10 days post-injury), we were able to observe that the SRC group exhibited baseline CBF levels similar to the HC group. This finding suggests that the timing of the exercise intervention may be crucial in determining its effects on CBF and concussion recovery.

Future research should explore the impact of exercise interventions at different time points post-injury, including the acute (within 24 hours), subacute (1-10 days), and chronic (>10 days) phases of concussion recovery. By systematically varying the timing of the exercise intervention, researchers can gain a better understanding of the optimal window for exercise-based interventions in concussion management and recovery.

Question 6

The study used transcranial Doppler ultrasound (TCD) to assess changes in MCAv, which does not quantify vessel diameter. You acknowledged this as a potential limitation given that the MCA can dilate and constrict in response to hypercapnic environments. How might future studies address this limitation, and what are the potential implications for understanding the relationship between exercise and cerebral blood flow in concussion recovery?

Answer 6

• The use of TCD to assess changes in MCAv without quantifying vessel diameter is a potential limitation of this study, as changes in vessel diameter could influence the interpretation of the findings.
• To address this limitation, future studies could employ additional imaging techniques that allow for the assessment of vessel diameter, such as magnetic resonance angiography (MRA) or computed tomography angiography (CTA). By combining TCD with these imaging techniques, researchers can gain a more comprehensive understanding of the cerebrovascular responses to exercise in the context of concussion recovery.
• Additionally, future studies could explore the use of hypercapnic challenges (e.g., breath-holding or CO2 inhalation) in conjunction with exercise interventions to assess the reactivity of the cerebral vasculature in individuals with concussion.

Understanding the relationship between exercise, cerebral blood flow, and vessel diameter in concussion recovery could inform the development of targeted exercise-based interventions that optimize cerebrovascular function and promote recovery following concussion

Question 7

How might the findings have differed if the exercise intervention was within the neurometabolic cascade (i.e., within 24 hours or 1-5 days post-injury), and what are the potential implications for concussion management?

Answer 7

• If the exercise intervention had been conducted earlier, such as within 24 hours or 1-5 days post-injury, the findings may have differed significantly. During the acute phase of concussion (within 24 hours), the neurometabolic cascade is characterized by a reduction in CBF, which may have resulted in lower baseline MCAv values for the SRC group compared to the HC group.
• Similarly, if the intervention was conducted within 1-5 days post-injury, the SRC group may have still exhibited lower baseline MCAv due to the persisting effects of the neurometabolic cascade. These potential differences in baseline MCAv could have influenced the magnitude of the exercise-induced changes in CBF and the subsequent effects on executive function.
• The implications for concussion management are that the timing of exercise interventions should be carefully considered, as introducing exercise too early may exacerbate the neurometabolic disturbances associated with concussion. Future research should investigate the optimal timing of exercise interventions post-injury to maximize the potential benefits while minimizing the risk of adverse effects.

Question 8

The study found that baseline to steady-state changes in MCAv did not impact the magnitude of postexercise executive function in either the SRC or HC groups. You proposed two possible explanations: (1) the relationship between CBF and executive function is not dose-dependent, and (2) postexercise executive function benefits are accrued from interdependent processes beyond just CBF changes. How might future studies be designed to test these hypotheses and further elucidate the mechanisms underlying the relationship between exercise and executive function in the context of concussion recovery?

Answer 8

• To test the hypotheses and further elucidate the mechanisms underlying the relationship between exercise and executive function in concussion recovery, future studies could employ a multi-modal approach that combines neuroimaging, physiological, and cognitive assessments.
• For the first hypothesis, researchers could design a study with multiple exercise intensity levels (e.g., low, moderate, and high) and assess the dose-response relationship between exercise-induced changes in CBF (using TCD or other imaging techniques) and executive function performance. If the relationship is not dose-dependent, similar executive function benefits should be observed across the different exercise intensities, despite varying magnitudes of CBF change.
• For the second hypothesis, future studies could simultaneously measure a range of physiological parameters, such as CBF, pressor responses, and biomolecule levels (e.g., nitric oxide, brain-derived neurotrophic factor, catecholamines), alongside executive function performance.
• Additionally, functional neuroimaging techniques (e.g., fMRI) could be used to assess changes in functional connectivity within executive function networks following exercise.

By integrating these multi-modal assessments, researchers can gain a more comprehensive understanding of the interdependent processes that contribute to postexercise executive function benefits in the context of concussion recovery. This knowledge could inform the development of targeted exercise interventions that optimize these underlying mechanisms to promote cognitive recovery following concussion.

Question 9

What are the advantages and limitations of using TCD to measure MCAv and estimate CBF and how might future studies complement TCD with other neuroimaging techniques to gain a more comprehensive understanding of exercise-induced changes in cerebral hemodynamics following concussion?

Answer 9

• Transcranial Doppler (TCD) offers several advantages in assessing exercise-induced changes in cerebral hemodynamics following concussion. TCD is non-invasive, relatively inexpensive, and provides high temporal resolution, allowing for continuous monitoring of MCAv during exercise.
• Additionally, TCD is portable and can be easily integrated into exercise protocols. However, TCD has limitations, such as its inability to quantify vessel diameter changes, which may occur in response to hypercapnic environments during exercise.
• While this limitation has not been shown to significantly influence the validity of TCD in evaluating exercise-related changes in MCAv, future studies could complement TCD with other neuroimaging techniques to gain a more comprehensive understanding of cerebral hemodynamics.
• For example, functional near-infrared spectroscopy (fNIRS) could be used to assess regional changes in cerebral oxygenation and blood volume, while arterial spin labeling (ASL) MRI could provide quantitative measures of CBF. Combining these techniques with TCD would allow researchers to assess exercise-induced changes in cerebral hemodynamics at different spatial scales and to cross-validate findings.

Moreover, integrating neuroimaging with other physiological measures (e.g., blood pressure, heart rate variability) and cognitive assessments could provide a more holistic understanding of the complex interplay between exercise, cerebral hemodynamics, and cognitive function in the context of concussion recovery.

Question 10

The study included a 20-minute rest period to ensure that physiological measures (HR, BP, and MCAv) were not elevated due to the locomotor demands of arriving at the lab. How did you determine the duration of this rest period, and what evidence supports its sufficiency for stabilizing these physiological measures?

Answer 10

• The 20-minute rest period before the pre-exercise oculomotor assessment was chosen based on previous studies demonstrating that this duration is sufficient for physiological measures such as HR, BP, and MCAv to return to baseline levels following locomotor activity. This rest period ensures that any changes in oculomotor performance following the exercise intervention can be attributed to the effects of exercise itself, rather than residual physiological arousal from arriving at the lab.

Question 11

Why did the baseline to steady changes in MCAv not impact the magnitude of postexercise EF in either the SRC or HC groups?

Answer 11

There are two possible explanations for the lack of impact of baseline to steady changes in MCAv on the magnitude of postexercise EF in both groups. First, the relationship between CBF and EF may not be dose-dependent. According to the hemo-neural hypothesis (Moore & Cao, 2008), only a small change in CBF is necessary to induce an EF benefit. This view is supported by studies showing equivalent magnitude postexercise EF benefits across a range of metabolically sustainable power outputs (Petrella et al., 2019; Tari et al., 2021).

Second, a postexercise EF benefit may result from interdependent processes beyond just CBF changes (Shirzad et al., 2022). These processes include pressor response changes (Washio & Ogoh, 2023), increased availability of biomolecules such as nitric oxide, brain-derived neurotrophic factor, and catecholamines (Knaepen et al., 2010; Maiorana et al., 2003; Zouhal et al., 2008), and enhanced functional connectivity within EF networks (Schmitt et al., 2019). The complex interplay of these factors may contribute to the observed postexercise EF benefits, rather than a simple dose-dependent relationship with CBF changes alone.

Question 12

Given that there was no correlation between MCAv increase and antisaccade RT reduction, what other factors could potentially contribute to the postexercise EF benefit?

Answer 12

• Several alternative factors could be associated with the postexercise EF benefit, even in the absence of a correlation between MCAv increase and antisaccade RT reduction.
• One such factor is the pressor response, which refers to the increase in blood pressure during exercise. Recent research by Washio and Ogoh (2023) suggests that the pressor response may play a role in postexercise cognitive improvements. They proposed that exercise-induced increases in blood pressure could lead to better perfusion of the brain, supporting cognitive function. This improved perfusion may occur even without detectable changes in MCAv, as blood flow distribution can be altered within the brain.
• Another factor to consider is the increased availability of biomolecules, such as nitric oxide, brain-derived neurotrophic factor (BDNF), and catecholamines, following exercise. Nitric oxide is a vasodilator that can improve cerebral perfusion and oxygenation (Maiorana et al., 2003). BDNF is a neurotrophin that supports neuronal growth, plasticity, and survival, and its levels have been shown to increase after exercise (Knaepen et al., 2010). Catecholamines, such as dopamine and norepinephrine, are neurotransmitters that can modulate cognitive function and are also released during exercise (Zouhal et al., 2008). The increased availability of these biomolecules may contribute to the postexercise EF benefit by enhancing neuronal function and plasticity, even in the absence of a direct correlation with MCAv changes.
• Lastly, Recent research suggests that enhanced functional connectivity within EF networks may contribute to postexercise EF benefits (Schmitt et al., 2019). Functional connectivity refers to the temporal correlation of neuronal activity patterns between anatomically distinct brain regions (van den Heuvel & Hulshoff Pol, 2010). Exercise has been shown to modulate functional connectivity in various brain networks, including those associated with EF (Voss et al., 2010). For example, a study by Weng et al. (2017) found that acute exercise increased functional connectivity between the dorsolateral prefrontal cortex (DLPFC) and other brain regions involved in EF, such as the anterior cingulate cortex (ACC) and the parietal cortex. The DLPFC is a key region for EF processes, including working memory, cognitive flexibility, and inhibitory control (Mansouri et al., 2009).

Enhanced functional connectivity between the DLPFC and other EF-related regions may facilitate information processing and improve EF performance following exercise. While the exact mechanisms underlying exercise-induced changes in functional connectivity are not fully understood, it is thought that increased CBF, neurotransmitter release, and neurotrophin production may play a role (Voss et al., 2013). As such, the modulation of functional connectivity within EF networks may be one of the interdependent processes contributing to postexercise EF benefits, alongside changes in CBF, pressor response, and biomolecule availability.

Question 13

Why was the TCD probe positioned over the temporal window, and what were the specific settings for depth, power, and gain?

Answer 13

• We positioned the TCD probe over the temporal window to measure changes in cerebral blood flow velocity (CBFV) during the experimental protocol. The temporal window is a preferred location for TCD measurements as it provides an acoustic window through the thin, translucent area of the temporal bone, allowing for reliable detection of MCAv
• Specifically, the TCD settings were as follows:
• 1. Depth:
• The TCD probe depth was set between 50-60 mm.
• This depth range is typical for accessing the MCA, which is the most commonly monitored cerebral artery using TCD.
• 2. Power:
• The power setting was maintained at 100%.
• Using maximum power helps to optimize the Doppler signal and ensure adequate penetration through the temporal bone.
• 3. Gain:
• The gain was adjusted between 30-40%.
• The gain setting controls the amplification of the Doppler signal and is typically adjusted to achieve a clear, well-defined waveform.
• 4. Velocity Range:
• The y-axis scale was set to -80 to 320 cm/s.
  This velocity range is suitable for capturing the typical blood flow velocities observed in the MCA during rest and exercise conditions. The negative value was selected to differentiate between the artery and surrounding veins.

Question 14

14. Why did you choose to measure peak MCAV instead of mean MCAV to assess CBF changes between SRC participants and healthy controls?

Answer 14

• The decision to focus the analysis on peak systolic MCAv, rather than mean MCAv, was based on several considerations that are well-supported in the cerebrovascular physiology literature.
• Firstly, peak systolic MCAv has been established as a more reliable and sensitive indicator of exercise-induced changes in CBF compared to mean MCAv. During dynamic exercise, the pulsatile nature of blood flow results in distinct systolic and diastolic phases in the MCA velocity waveform. The peak systolic velocity represents the maximum blood flow achieved with each cardiac cycle, and this metric has been shown to correlate more strongly with direct measures of CBF, such as those obtained via Xenon-133 clearance or arterial spin labeling MRI (Clyde et al., 1996; Duschek et al., 2018; Rosengarten & Kaps, 2002).

PSMCAV-> The highest blood flow velocity in the middle cerebral artery during the systolic phase of the cardiac cycle.

• In contrast, mean MCAv can be influenced by changes in both the systolic and diastolic components of the waveform, as well as potential alterations in the waveform shape. This can introduce more variability and potentially mask the true magnitude of exercise-induced CBF changes, which are primarily reflected in the peak systolic velocity.
• Additionally, previous research has demonstrated that peak systolic MCAv is a more sensitive marker of cerebrovascular function in clinical populations, such as those with traumatic brain injury or concussion (Len & Neary, 2011; Len et al., 2011). In these conditions, the autoregulatory mechanisms governing cerebral perfusion may be impaired, leading to greater changes in the systolic component of the MCA velocity waveform.
• By focusing the analysis on peak systolic MCAv, the current study was able to more accurately capture the exercise-induced changes in CBF in both the SRC and HC groups, allowing for a more robust comparison of the cerebrovascular responses between these populations. This approach is consistent with the recommendations in the cerebrovascular physiology literature and provides a solid foundation for interpreting the study’s findings within the context of executive function and concussion recovery.

Question 15

At which timepoint and exercise intensity in terms of watts, does CBF plateu and decrease instead of continue to increase? How do you know your participants didn’t reach this threshold?

Answer 15

• Literature on the CBF response to incremental exercise provides some insights into when a plateau or decrease in CBF may occur. Research has shown that CBF increases linearly with exercise intensity up to approximately 50-60% of maximal exercise capacity (VO2max or peak power output). Beyond this intensity threshold, further increases in exercise workload result in a plateau or even a decrease in CBF, despite continued increases in systemic cardiovascular parameters like heart rate and cardiac output.
• This CBF response pattern is thought to be mediated by the competing influences of vasodilatory and vasoconstrictor mechanisms. At lower exercise intensities, the vasodilatory effects of metabolic byproducts (e.g., CO2, H+) predominate, leading to the linear increase in CBF. However, at higher intensities, sympathetically-mediated vasoconstriction of cerebral arterioles begins to counteract the vasodilatory forces, resulting in the CBF plateau or decline.
• Given that the current study employed a sub-symptom threshold exercise intensity, corresponding to approximately 80% of the participants’ heart rate threshold, the exercise workload was likely below the intensity at which the CBF response would begin to plateau or decrease.

Question 16

What is Xenon 133 clearance and ASL and how do they measure CBF directly?

Answer 16

· Xenon 133: Radioactive Isotope: Emits gamma rays, used in medical imaging.
· Half-Life: About 5.3 days.
· Medical Use: Commonly used for lung and brain imaging to assess ventilation and blood flow.
· Administration: Inhaled by the patient.
· Brain Distribution: Crosses the blood-brain barrier and distributes proportionally to regional blood flow.
· Imaging: Gamma rays emitted by Xe-133 are detected by a gamma camera.
· Clearance: Monitored over time; quicker clearance indicates higher blood flow.
· Analysis: Regions with abnormal blood flow patterns can indicate conditions like stroke or dementia.
ASL: magnetically labels blood, tracks its flow into the brain, and calculates CBF by comparing labeled and unlabeled images, offering a safe, repeatable method for brain perfusion assessment.