In the Buckley Lab’s latest publication “Stand-alone segmentation of blood flow pulsatility measured with diffuse correlation spectroscopy,” Srinidhi Bharadwaj and Tara Urner et al. present a signal processing method for isolating blood flow pulses from noisy diffuse correlation spectroscopy (DCS) signals. Importantly, this method does not require the use of an exogenous reference signal – such as from a blood pressure or electrocardiography machine – to identify pulse onsets, which has been the standard approach. When compared to blood pressure-based pulse segmentation in a cohort of 30 subjects, the stand-alone method performed comparably, demonstrating the wide applicability of the technique. Removing the need for an exogenous reference signal will allow broader application of promising pulse analysis techniques to DCS-derived blood flow waveforms, which show promise as biomarkers of cerebrovascular health.  MATLAB code implementing the method is available from the Buckley Lab’s public Github page.

Comparison of BFI segmentation with stand-alone vs. reference-based method. A. Representative DCS-derived blood flow signal (blue), filtered waveform used for stand-alone segmentation (green), and time aligned arterial blood pressure (ABP) signals (red). Dashed vertical lines in green and red represent onsets identified from the filtered signal and ABP, respectively. B. Overlaid pulses from the window in A, normalized to unit length. Green denotes individual pulses segmented with the stand-alone method and red denotes pulses segmented using ABP. Vertical lines denote 20 equally sized bins used for averaging. C. Resulting mean (stdev) BFI waveforms obtained using the filtered DCS signal or ABP as a reference signal. D. Schematic depiction of pulse morphology features quantified. Abbreviations: mean flow (MF), peak systolic flow (PSF), end diastolic flow (EDF), amplitude (AMP), and area under the curve (AUC).

The Wallace H. Coulter Department of Biomedical Engineering announced Dr. Erin Buckley will receive a promotion to associate professor with tenure, effective Sept. 1.  Erin first joined the department and founded her lab with a focus on translational diffuse spectroscopies for brain monitoring in 2015. Since then, the lab has published over 20 papers and numerous more conference abstracts and has hosted more than 30 members in total. On behalf of the entire lab – past and present – congratulations Erin!!!!!

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).