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Squat-stand maneuvers (SSMs) have been used to improve the coherence of transfer function analysis (TFA) estimates during the assessment of dynamic cerebral autoregulation (dCA). There is a need to understand the influence of peripheral changes resulting from SSMs on cerebral blood flow, which might confound estimates of dCA. Healthy subjects ( n = 29) underwent recordings at rest (5-min standing) and 15 SSMs (0.05 Hz). Heart rate (three-lead ECG), end-tidal CO 2 (capnography), blood pressure (Finometer), cerebral blood velocity (CBV; transcranial Doppler, middle cerebral artery), and the angle of the thigh (tilt sensor) were measured continuously. The response of CBV to SSMs was decomposed into the relative contributions of mean arterial pressure (MAP), resistance-area product (RAP), and critical closing pressure (CrCP). Upon squatting, a rise in MAP (83.6 ± 21.1% contribution) was followed by increased CBV. A dCA response could be detected, determined by adjustments in RAP and CrCP (left hemisphere) with peak contributions of 24.8 ± 12.7% and 27.4 ± 22.8%, respectively, at different times during SSMs. No interhemispheric differences were detected. During standing, the contributions of MAP, RAP, and CrCP changed considerably. In conclusion, the changes of CBV subcomponents during repeated SSMs indicate a complex response of CBV to SSMs that can only be partially explained by myogenic mechanisms. More work is needed to clarify the potential contribution of other cofactors, such as breath-to-breath changes in Pco 2 , heart rate, stroke volume, and the neurogenic component of dCA. NEW &amp; NOTEWORTHY Here, we describe the different contributions to the cerebral blood flow response after squat-stand maneuvers. Furthermore, we demonstrate the complex interaction of peripheral and cerebral parameters for the first time. Moreover, we show that the cerebral blood velocity response to squatting is likely to include a significant metabolic component.

The cerebrocardiovascular response to periodic squat-stand maneuvers in healthy subjects: a time-domain analysis