Using depth-enhanced diffuse correlation spectroscopy and near-infrared spectroscopy to isolate cerebral hemodynamics during transient hypotension

Abstract

Combining diffuse correlation spectroscopy (DCS) and near-infrared spectroscopy (NIRS) permits simultaneous monitoring of multiple cerebral hemodynamic parameters related to cerebral autoregulation; however, interpreting these optical measurements can be confounded by signal contamination from extracerebral tissue.Aim: We aimed to evaluate extracerebral signal contamination in NIRS/DCS data acquired during transient hypotension and assess suitable means of separating scalp and brain signals.Approach: A hybrid time-resolved NIRS/multidistance DCS system was used to simultaneously acquire cerebral oxygenation and blood flow data during transient orthostatic hypotension induced by rapid-onset lower body negative pressure (LBNP) in nine young, healthy adults. Changes in microvascular flow were verified against changes in middle cerebral artery velocity (MCAv) measured by transcranial Doppler ultrasound.Results: LBNP significantly decreased arterial blood pressure (−18% AE 14%), scalp blood flow (>30%), and scalp tissue oxygenation (all p ≤ 0.04 versus baseline). However, implementing depth-sensitive techniques for both DCS and timeresolved NIRS indicated that LBNP did not significantly alter microvascular cerebral blood flow and oxygenation relative to their baseline values (all p ≥ 0.14). In agreement, there was no significant reduction in MCAv (8% AE 16%; p ¼ 0.09). Conclusion:Transient hypotension caused significantly larger blood flow and oxygenation changes in the extracerebral tissue compared to the brain. We demonstrate the importance of accounting for extracerebral signal contamination within optical measures of cerebral hemodynamics during physiological paradigms designed to test cerebral autoregulation.

Characterization of cerebral macro- and microvascular hemodynamics during transient hypotension

“…A second limitation was the DCS modeling analysis relied on assuming the distance from the probe to the brain (e.g., scalp and skull thicknesses). We have clarified the effect of these assumptions on DCS measures of flow in Shoemaker et al( 26 ). Although it may be possible to include the overall thickness as an additional fitting variable ( 28 ), we elected to use measured values from a previous study ( 23 ) since the probe location on the forehead and demographics of the participants were similar between the two studies (i.e., young, healthy adults).…”

“…Data are presented as percent change from the 6 s average immediately before starting LBNP. Further technical details regarding the data analysis for DCS and trNIRS are described elsewhere ( 26 ).…”

“…Analysis of the trNIRS data is detailed in our previous works ( 19 , 23 , 26 , 27 ). Briefly, the background signal was subtracted from each DTOF in a time series.…”

Comparison of diffuse correlation spectroscopy analytical models for measuring cerebral blood flow in adults

“…[6][7][8][9] These multilayered methods have shown promise both in silico simulations, [10][11][12] in phantoms, 7,9 and in vivo studies in animals, 13 with limited application in humans. 3,4,8,14 However, the optimal approach for DCS analysis in adults is unclear, and a rigorous comparison of these multilayer analytical methods in vivo is lacking.…”

“…Alternatively, more complex two- or three-layered models have been introduced to separate extracerebral from cerebral hemodynamics 6 – 9 These multilayered methods have shown promise both in silico simulations, 10 – 12 in phantoms, 7 , 9 and in vivo studies in animals, 13 with limited application in humans 3 , 4 , 8 , 14 . However, the optimal approach for DCS analysis in adults is unclear, and a rigorous comparison of these multilayer analytical methods in vivo is lacking.…”