In our latest work published in Biomedical Optics Express, Tara Urner et al. present quantification of the average morphology of cardiac waveforms in the cerebral blood flow signal measured with diffuse correlation spectroscopy (DCS), and how these waveforms behave in response to vasomotor changes. Pulse waveform analysis has long been used with the current state-of-the-art technique for capturing macrovascular blood flow – transcranial doppler ultrasound (TCD) – but cardiac pulsatility at the microvascular level in the brain has only recently become accessible at the bedside using DCS. Several groups have taken initial steps towards applying waveform analysis to DCS-derived blood flow for clinical applications, but knowledge of what “normal” waveforms should look like has been lacking. This work aims to lay the groundwork for future clinical applications of DCS combined with waveform analysis by presenting typical resting-state values for a variety of morphological features as well as quantify waveform response to a vasoactive stimulus. The authors found that the blood flow waveform exhibited marked changes with vasodilation including increasing pulse amplitude and area under the curve. Additionally, significant sex-based difference were observed in the waveform, consistent with previous findings with TCD. These exciting results set the stage for DCS-derived blood flow waveform morphology to provide much-needed noninvasive biomarkers of cerebrovascular health and disease.

Fig. 1. Estimating microvascular blood flow waveforms. (A) Representative 15s window of pulsatile blood flow index (BFI, blue) and arterial blood pressure (ABP, red) signals. The flow waveform leads pressure. The red shaded box shows the boundaries of an ABP pulse, while the blue box denotes the boundaries of the corresponding BFI pulse. (B) Waveforms extracted from the 15s window are overlayed, preserving the sampling offset between ABP and BFI. Each pulse pair is normalized by the same factor such that ABP pulse length is set to a unit length of 1, then binned and averaged. (C) Final average blood flow and pressure waveforms over the 15s time window, shading shows standard deviation. (D) Schematic diagram of morphological features directly quantified from each pulse: mean flow (MF), peak systolic flow (PSF), end diastolic flow (EDF), amplitude (AMP) and area under the curve (AUC).

Cerebrovascular reactivity (CVR), i.e., the ability of cerebral blood vessels to dilate or constrict in response to changes in blood oxygen content or neuronal demand, is a biomarker of vascular health. CVR assessment usually involves administration of a controlled vasoactive stimulus so the reactive ability of the brain can be easily observed. The traditional way of doing this requires patients to undergo an MRI while performing what is called a “hypercapnia challenge” where they inhale carbon dioxide. In pediatric patients or after sever brain injury, this protocol is often infeasible or contraindicated. In recent work now out in Neurophotonics, Kyle R. Cowdrick et al. explored the application of Diffuse Correlation Spectroscopy (DCS) for CVR assessment in a cohort of healthy adults across multiple, more tolerable, experimental paradigms vs. a gold standard hypercapnia challenge. Specifically, they compared CVR calculated from DCS measurements taken from subjects breathing normally at rest or during a timed breath-hold challenge with hypercapnia. They found that applying general linear models to minimize influence of  systemic hemodynamics on the brain signal measured with DCS improved the agreement between these more tolerable assessment methods and the gold-standard. This promising result suggests that DCS coupled with a milder vasoactive stimulus can allow CVR assessment in previously inaccessible patient groups.

In our latest work in Neurophotonics, Hongting Zhao determines the influence of oversimplifying the head geometry on brain blood flow estimated with diffuse correlation spectroscopy (DCS). Due to the noninvasive nature of  DCS measurements, light must pass through extracerebral layers (i.e., skull, scalp, and cerebral spinal fluid) before detection at the tissue surface. To minimize the contribution of these extracerebral layers to the measured signal, an analytical model has been developed that treats the head as a series of three parallel and infinitely extending slabs (mimicking scalp, skull, and brain). The three-layer model has been shown to provide a significant improvement in cerebral blood flow estimation over the typically used model that treats the head as a bulk homogenous medium. However, the three-layer model is still a gross oversimplification of the head geometry that ignores head curvature, the presence of cerebrospinal fluid (CSF), and heterogeneity in layer thickness. Using Monte Carlo modeling in a four-layer slab medium and a three-layer sphere medium to isolate the influence of CSF and curvature, respectively, we found both head curvature and failing to account for CSF lead to significant errors in the estimation of cerebral blood flow. However, the effect of curvature and CSF on relative changes in blood flow is minimal. In sum, these findings suggest that the three-layer model holds promise for improving estimation of relative changes in cerebral blood flow; however, estimations of absolute cerebral blood flow with the approach should be viewed with caution given that it is difficult to account for appreciable sources of error, such as curvature and CSF.