Influence of probe pressure on the pulsatile diffuse correlation spectroscopy blood flow signal on the forearm and forehead regions

Abstract. In a pilot study of 11 healthy adults (24 to 39 years, all male), we characterize the influence of external probe pressure on optical diffuse correlation spectroscopy (DCS) measurements of pulsatile blood flow obtained on the forearm and forehead. For external probe pressure control, a hand inflatable air balloon is inserted between the tissue and an elastic strap. The air balloon is sequentially inflated to achieve a wide range of external probe pressures between 20 and 250 mmHg on the forearm and forehead, which are measured with a flexible pressure sensor underneath the probe. At each probe pressure, the pulsatility index (PI) of arteriole blood flow on the forehead and forearm is measured with DCS (2.1-cm source-detector separation). We observe a strong correlation between probe pressure and PI on the forearm (R=0.66, p<0.001), but not on the forehead (R=−0.11, p=0.4). The forearm measurements demonstrate the sensitivity of the DCS PI to skeletal muscle tissue pressure, whereas the forehead measurements indicate that DCS PI measurements are not sensitive to scalp tissue pressure. Note, in contrast to pulsatility, the time-averaged DCS blood flow index on the forehead was significantly correlated with probe pressure (R=−0.55, p<0.001). This pilot data appears to support the initiation of more comprehensive clinical studies on DCS to detect trends in internal pressure in brain and skeletal muscle.

Influence of probe pressure on the pulsatile diffuse correlation spectroscopy blood flow signal on the forearm and forehead regions Detian Wang, a,b Wesley B. Baker, c Hui He, b Peng Gao, b Liguo Zhu, b, * Qixian Peng, b Zeren Li, b Fei Li, a Tunan Chen, a and Hua Feng a, *

Introduction
Diffuse correlation spectroscopy (DCS) utilizes photon correlation techniques to noninvasively measure blood flow in deep tissues continuously and at the bedside. [1][2][3][4][5][6] It has been used in a variety of clinical applications, including stroke and muscle disease. Recently, improvements in DCS measurement speed have been exploited to resolve the pulsatile heartbeat fluctuations of arterial blood flow. 7-11 DCS measurement of blood flow pulsatility has generated considerable interest in new clinical applications for the optical technology, which include assessment of intracranial pressure (ICP) 10,12,13 and arterial stiffness. 10,11 To date, Doppler ultrasound techniques have been dominantly used for noninvasive assessment of flow pulsatility in the clinic. Doppler ultrasound measures blood velocity in major feeder arteries of the brain and peripheral tissues. Among numerous applications, the relation of Doppler waveforms to ICP and arterial stenosis has been investigated, and encouraging preliminary results were observed. [14][15][16] In traumatic brain-injured adults, for example, a transcranial Doppler ultrasound (TCD) derived pulsatility index (PI) was proportionally correlated with intrasubject trends in invasively measured ICP (R ¼ 0.61). 17 This TCD PI was defined as the difference of systolic flow velocity (FV sys ) and diastolic flow velocity (FV dia ) divided by mean flow velocity (hFVi), i.e., ðFV sys − FV dia Þ∕hFVi.
Although Doppler ultrasound is routinely used in the clinic, it has limitations. For instance, Doppler ultrasound measurements are sensitive to probe orientation, which makes stable fixation of the probe for continuous monitoring challenging. 18,19 Further, because Doppler ultrasound interrogates large proximal arteries, measurements may be relatively insensitive to localized diseases/injuries. DCS measurement of blood flow pulsatility in the microvasculature is a potential complementary and alternative approach to Doppler ultrasound. DCS is well-suited for continuous monitoring and sensitive to localized microvasculature. 3,[19][20][21][22] To realize the full clinical potential of DCS pulsatility monitoring, however, a better understanding of confounding influences on the DCS pulsatility measurement is needed.
Previous work has shown that time-averaged steady-state DCS measurements of blood flow can be influenced by factors such as external probe pressure against the tissue, superficial tissue layers, and tissue optical properties. 20,[23][24][25][26] This pilot study examines the influence of external probe pressure on DCS measurements of blood flow pulsatility on the forearm and the forehead. The influence on pulsatility is further compared to the influence on steady-state blood flow measurements.

Pressure Sensor Calibration
We used a thin and flexible resistive pressure sensor (1 lb, 9.5mm diameter, A201, FlexiForce, Tekscan) for probe pressure measurement [ Fig. 1(a)]. To calibrate the sensor for absolute pressure measurement, we used a series of custom built metal weights with a contact surface area equivalent to effective area of the pressure sensor (i.e., 0.71 cm 2 ), as shown in Fig. 1(b). During calibration, the pressure sensor was sandwiched between metal weights to ensure exact overlap of the sensor area with the weights' contact surface area [ Fig. 1(c)]. The output voltage of the sensor (stabilized with a voltage follower circuit 27 ) was recorded with a DAQ board (USB-6356, NI). Different weights (between 20 and 335 g) were employed to apply 21 pressures against the sensor, which ranged from 20.7 to 346.3 mmHg. Note three repetitions of the calibration measurement were made at each applied pressure, and the mean of the sensor's output voltage across the three repetitions was computed. This mean output voltage from the sensor was plotted against the exact probe pressure exerted by the weights, and a linear best-fit line was computed [ Fig. 1(d)]. This linear best-fit line was then used to convert voltage from the sensor to absolute probe pressure on tissue.

DCS Instrumentation, Probe, and Measurement of Blood Flow
DCS uses rapid speckle intensity fluctuations of multiply scattered light that has traversed tissue to estimate blood flow. Specifically, these intensity fluctuations, which are induced by red blood cell motion, are quantified through measurement of the normalized intensity autocorrelation function, g 2 ðτÞ ¼ hIðtÞIðt þ τÞi∕hIðtÞi 2 at multiple delay-times τ. 28,29 Here IðtÞ is the detected light intensity at time t, and the angular brackets hi denote time averages. A semi-infinite tissue model was employed to derive a DCS blood flow index (BFI) from the decay of g 2 ðτÞ. 1 Note that assumed tissue optical absorption and reduced scattering coefficients of 0.12 and 7.38 cm −1 for the forehead, and 0.16 and 4.32 cm −1 for the forearm were used for deriving BFI. 8 Note further, only values for which g 2 ðτÞ > 1.05 were used in the semi-infinite fit, and a DCS coherence parameter (i.e., β coefficient) 1 was fitted with the BFI for every frame.
Our DCS instrument consists of a long coherence-length, continuous-wave laser (785 nm, 90 mW, DL785-100-3O, Crysta Laser Inc.), two single photon avalanche photodiodes (APDs) (SPCM-AQRH, Excelitas, Canada), and a custom-built software autocorrelator that is described elsewhere. 8,30,31 Briefly, the single-photon APDs convert the detected photons to digital TTL pulses, which are sent to pulse counters in a PCIe-6612 board (National Instruments). The counters continuously accumulate photon counts, and the computation of g 2 ðτÞ is performed by software implementing a multi-tau scheme. 32,33 Specifically, g 2 ðτÞ is computed at 87 delay-times ranging from 0.2 μs to 1.5 ms. The first seven delay-times are given by τ N ¼ P N n¼1 n · Δt ðN ¼ 1; 2; · · · ; 7Þ, and the last 80 delay-times are given by τ 8MþN ¼ P M m¼0 P N n¼0 ð8þnÞ·2 m ·Δt (M ¼ 0; 1; : : : 9; N ¼ 0; 1; : : : 7), where Δt ¼ 0.2 μs. Note, at the i'th delay-time, g 2 ðτ i Þ is computed as NðiÞ · Nð1Þ∕ðNð1Þ · Nð1ÞÞ, where NðiÞ is the photon count in the i'th correlator bin, and Nð1Þ is the photon count in the first correlator bin (here, the first bin is defined to have the same bin width as the i'th bin 33 ). The temporal width of the i'th delay-time bin, i.e., wðiÞ, is given by wði þ 1Þ ¼ τ iþ1 − τ i for 1 ≤ i ≤ 86, and wð1Þ ¼ Δt. For each delay-time, g 2 ðτ i Þ is averaged for 50 ms to obtain normalized autocorrelation values at 20 Hz (the average photon count in the 50-ms interval is also recorded).
The DCS tissue probe acquired data at two source-detector separations (i.e., 2.1 and 3.1 cm) via one multimode source optical fiber (200 μm∕0.22 NA, Nufern) and two single-mode detector fibers (4.4 μm∕0.13 NA, 780-HP, Nufern) embedded in a 100 mm × 40 mm × 10 mm black urethane rubber pad (Fig. 2). Note a glass diffuser with a length of 5 mm was attached to the source fiber to increase the beam diameter to 2.2 mm. Given the output power emerging from the fiber of 70 mW, the irradiant power on the skin was 18 mW∕mm 2 , which is less than the maximum permissible exposure (20 mW∕mm 2 ) allowed by Chinese standard at the 785-nm wavelength (GB/T 7247.9-2016). We found the DCS signal at 3.1 cm was too low to resolve flow pulsatility during the cardiac cycle (discussed further in Sec. 4.4). Accordingly, only steady-state BFI data for the 3.1-cm separation are reported.

Subjects and Experimental Protocol
Eleven healthy adults (11 males and 0 females) were enrolled in this study. The average age across subjects was 31.4 years old (range 24 to 39), distributed per decade as reported in Table 1. All subjects were Chinese. All subjects provided written consent, and all protocols/procedures were approved by the Institutional Review Board at the Army Medical University. Throughout the experiment, subjects sat comfortably upright. For each subject, DCS measurements were sequentially obtained across a wide range of increasing probe pressures on the right forearm flexor and the right forehead. The probe was secured on the forearm or forehead by an elastic strap, with the resistive pressure sensor sandwiched between the probe and the skin. A hand-inflatable air balloon (XL-10, Xintian Liming Medical Device, China) inserted between the strap and the tissue on the opposite side of the probe was used for pressure control (see Fig. 2). Via gradual inflation of the air balloon, DCS measurements were acquired at ∼6 discrete probe pressures between 20 and 250 mmHg for both the forearm and forehead. Approximately 1 min of continuous DCS data was obtained for each probe pressure.
The means of the BFI measurements acquired at 2.1-and 3.1cm separations were calculated for each probe pressure and are denoted as hBFIi 2.1 and hBFIi 3.1 (note probe pressure dependence is implicit). The DCS PI of BFI measurements acquired at 2.1 cm was further determined for each probe pressure. The DCS PI is defined as the Fourier spectral amplitude of BFI at the heart rate frequency divided by the mean BFI, i.e., PI ≡ jBFIðf hr Þj∕hBFIi. The jBFIðf hr Þj amplitude (also known as the first harmonic) of the 1-min time-domain signal acquired at each pressure was computed with a discrete Fourier transform method implemented in MATLAB R2014a (FFT, Mathworks). Note that jBFIðf hr Þj is considerably less than the difference between systolic and diastolic BFI, since the latter difference also reflects higher order harmonics in the signal. We chose to use first harmonic Fourier filtering to calculate PI because this approach is less affected by measurement noise than the   difference between systolic and diastolic BFI. 10,34 Exemplar mean intensity autocorrelation curves at two discrete probe pressures obtained on the forearm, along with their respective temporal BFI traces in the time domain and Fourier spectral amplitudes in the frequency-domain, are shown in Fig. 3.

Statistical Analysis
We assembled data sets of external probe pressure (P), hBFIi 2.1 , hBFIi 3.1 , and PI across all 11 subjects on the forearm flexor and on the forehead. In addition to these two absolute data sets, we also assembled corresponding intrasubject normalized relative data sets of ΔP ≡ P − P o , rBFI 2. , and rPI as function of ΔP, for both the forearm and the forehead. The mixed effects models, which were implemented in MATLAB (fitlme), account for each subject contributing multiple data points. In these models, 95% confidence intervals for the slopes are reported, and a type 1 error rate of 0.05 was used.
In linear mixed effects regression analysis, the slopes of the best-fit lines were further significantly different from zero (p < 0.001; see Table 2). The interquartile ranges of the fractional deviation of the measured data from the best-fit lines are reported in Table 2.
Note the DCS data shown in Fig. 4 were acquired at the 2.1cm source-detector separation. DCS measurements were also made at 3.1 cm, but the signal-to-noise was too low to resolve flow pulsatility. Plots of the steady-state data (i.e., hBFIi versus P and rBFI versus P) acquired at the 3.1-cm separation for the forearm are shown in Figs. 5(a) and 5(b). The correlations between hBFIi versus P and rBFI versus P were R ¼ −0.60 and R ¼ −0.75, respectively. In addition, the corresponding slopes of the best-fit lines were not significantly different from the slopes of the best-fit lines at the 2.1-cm separation (Table 2). Finally, the average DCS BFI and DCS intensity across subjects obtained at baseline with the 3.1-cm separation were ð7.4 AE 5.1Þ × 10 −9 cm 2 ∕s and 29 AE 13 kcps, respectively, and the average difference between the blood flow indices acquired at 3.1-and 2.1-cm separations at baseline (i.e., hBFIi 3.1;o − hBFIi 2.1;o ) was ð−1.0 AE 4.4Þ × 10 −9 cm 2 ∕s. Thus the steadystate blood flow indices acquired at 3.1-and 2.1-cm separations on the forearm were not significantly different.

DCS Measurements on Forehead
For the 11 subjects measured, the average (mean AE SD) heart rate, DCS BFI (at 2.1-cm separation), DCS PI, probe pressure, and DCS intensity (at 2.1-cm separation) obtained at baseline on the forehead were (72 AE 12) beats per min, ð1.3 AE 0.7Þ × 10 −8 cm −2 ∕s, 0.16 AE 0.06, 41 AE 19 mmHg, and 140 AE 55 kcps,  respectively. Across all subjects, we observed strong correlations between hBFIi and P (R ¼ −0.55, p < 0.001) and between rBFI and ΔP (R ¼ −0.88, p < 0.001) (Fig. 6). Interestingly, the correlations between PI and P (R ¼ −0.11, p ¼ 0.4) and between rPI and ΔP (R ¼ 0.01, p ¼ 1.0) were not significant (Fig. 6). The slopes and intercepts of the linear best-fit lines obtained from linear mixed effects regression analysis, as well as the interquartile range of the fractional deviations from the best fit, are reported in Table 2. The slopes are significantly different from zero (p < 0.001) for the hBFIi versus P and rBFI versus ΔP comparisons, but not for the PI versus P (p ¼ 0.4) and rPI versus ΔP (p ¼ 1.0) comparisons.   Table 2). hBFIi o and P o denote the subject's mean steady-state blood flow index and probe pressure measured at the baseline (initial) probe pressure. The slopes of the lines on the forearm at 2.1 cm are not significantly different from those at 3.1 cm. On the forehead, in contrast, the magnitudes of the slopes at 3.1 cm are significantly smaller than those at 2.1 cm.
Neurophotonics 035013-5 Jul-Sep 2019 • Vol. 6(3) Plots of the steady-state data (i.e., hBFIi versus P and rBFI versus P) acquired at the 3.1-cm separation on the forehead are shown in Figs. 5(c) and 5(d). The correlations between hBFIi versus P and rBFI versus ΔP were R ¼ −0.37 and R ¼ −0.71, respectively, and the slopes of the corresponding best-fit lines were significantly smaller in magnitude than the analogous slopes at 2.1-cm separation (see Table 2). Contrastingly, the average DCS BFI across subjects obtained at baseline with the 3.1-cm separation, i.e., ð1.4 AE 0.7Þ × 10 −8 cm 2 ∕s, was not significantly different from the corresponding average BFI obtained at 2.1-cm separation. Similarly, the average hBFIi 3.1;o − hBFIi 2.1;o difference at baseline of ð1.4 AE 2.1Þ × 10 −9 cm 2 ∕s was not significantly different from zero. The average DCS signal intensity across subjects at baseline for the 3.1-cm separation was 12 AE 3 kcps.
Finally, we consider the sensitivity of the steady-state measured BFI to scalp flow. We assume that cerebral blood flow remains constant during our measurements at different scalp pressures. Accordingly, we define the sensitivity of BFI to scalp flow (i.e., S) as the fractional change in the measured steady-state BFI divided by the fractional change in scalp flow caused by increased pressure on the scalp:

Discussion
Elevated ICP on brain tissue, which may compromise cerebral blood flow and lead to ischemia and secondary brain injury, is a major complication of traumatic brain injury and hydrocephalus. [35][36][37] Accordingly, invasive ICP monitoring is widely employed in neurocritical care to detect dangerous elevations in ICP, which in turn trigger therapeutic interventions. Invasive ICP monitors, however, carry risks, including intracranial hemorrhage and infection, and these risks may be too great in some brain injured patients. Consequently, noninvasively measured biomarkers of ICP would be greatly beneficial in clinical care.
Previous research based on TCD measurements has shown that the shape of pulsatile arterial blood flow waveforms during the cardiac cycle is influenced by the tissue pressure (e.g., ICP) compressing the arteries. 14,17,38,39 Specifically, the waveform shape was characterized by a PI, which is defined as the systolic minus diastolic blood flow difference divided by the mean blood flow. Numerous comparisons of TCD measurements of PI in the middle cerebral artery with invasive measurements of ICP in brain-injured patients have been performed. 17,36,[39][40][41][42] These comparisons, which are documented in useful reviews, 14,36,[40][41][42] have investigated the ability of PI to monitor trends in ICP, as well as the ability of PI to detect absolute tissue pressure values. Modest to strong Pearson's correlations between PI and ICP were observed for both types of comparisons, depending on the study (e.g., from 0.31 to 0.94 for the absolute comparisons 39,40 and from 0.61 to 0.83 for trend comparisons 17,43,44 ). Overall, the PI reflected trends in ICP more reliably. 14,17,41 The equivocal findings have hindered clinical translation of TCD-measured PI as a tool for noninvasive assessment of ICP. The variation in reported correlations may arise, in part, from intra-and interobserver variations in TCD PI measurements, 42 which are in part a consequence of the sensitivity of the TCD technique to probe orientation and motion artifacts. 18,19,42 Recently, DCS measurement of blood flow pulsatility in the microvasculature has emerged as a potential complementary and alternative approach to Doppler ultrasound. [7][8][9][10][11] The DCS measurement is robust to the particular placement and orientation of its sensors, which can be held on the head with clinical tape or an elastic strap. 11,19 Accordingly, DCS is well-suited for continuous prolonged monitoring. [20][21][22]45 Herein, we investigated the effects of pressure on the DCS blood flow pulsatility signal These data were obtained on the right foreheads of 11 healthy adult volunteers at multiple probe pressures for each subject (i.e., N ¼ 60 probe pressures total; 1-min of DCS data acquired for each pressure, 2.1-cm source-detector separation). hBFIi o , P o , and PI o denote the subject's mean steady-state BFI, probe pressure, and PI measured at the baseline (initial) probe pressure. The solid red lines are the best fits to the data from linear mixed effects regression analysis. The Pearson's correlations and the slopes of the best-fit lines were significantly different from zero for only the hBFIi versus P and rBFI versus ΔP comparisons (see main text and Table 2).
Neurophotonics 035013-6 Jul-Sep 2019 • Vol. 6(3) acquired at the 2.1-cm source-detector separation on both the forehead and forearm. The measured external probe pressures on the forehead and forearm are the scalp and muscle tissue pressures, respectively.

DCS PI Measurements on Forearm Are Sensitive to Muscle Tissue Pressure
The strong correlation between DCS-measured PI and muscle tissue pressure (R ¼ 0.66; Fig. 4, Table 2) shows the sensitivity of PI to pressure, which is consistent with TCD-measured PI. As with TCD, caution is warranted in the use of PI as a one-to-one measure of pressure. The interquartile range of the fractional deviation of the DCS PI measurements from the linear mixed effects regression of PI versus pressure is 64% ( Table 2, Fig. 4). A similar interquartile range of the fractional deviation (i.e., 46%) was observed in the comparison of relative changes in PI to relative changes in pressure ( Table 2, Fig. 4). In future work, arterial blood pressure measurements and DCS PI measurements utilized in combination with a model of the arterial vascular tree may be more predictive of tissue pressure. 10 Steady-state BFI was modestly less correlated with muscle tissue pressure than the PI (i.e., R ¼ −0.57). Thus the BFI can also potentially be used as a biomarker of tissue pressure. Previous work has shown that errors in the absolute tissue optical properties substantially influence the BFI measurement. 23,25,26 In clinical patient populations wherein optical properties can be substantially different from healthy controls, this source of error may be important. The DCS PI measurements, however, are based on relative changes in BFI, which are more robust to errors in absolute optical properties. 46 These results suggest the promise of using DCS PI measurements in the future, potentially in conjunction with absolute BFI, to aid noninvasive diagnosis of compartment syndrome. Compartment syndrome is a condition wherein increased tissue pressure within a localized space in the limbs compromises blood supply to, and ultimately the function of, tissues (especially nerve and muscle) within that space. [47][48][49] It is presently diagnosed with invasive pressure monitors.

DCS PI Measurements on Forehead Are not Sensitive to Scalp Tissue Pressure
Interestingly, on the forehead, we found that DCS PI measurements were not correlated with scalp tissue pressure (R ¼ −0.11, p ¼ 0.4; Fig. 6, Table 2). Relative changes in PI were also not sensitive to relative changes in scalp tissue pressure (R ¼ 0.01, p ¼ 1.0; Fig. 6, Table 2). Contrastingly, the absolute steady-state BFI, as well as relative changes in the BFI, were correlated with scalp tissue pressure (i.e., R ¼ −0.55 and R ¼ −0.88, respectively; Table 2). The observed sensitivity of the DCS steady-state BFI measurement to scalp pressure is consistent with previous work and is indicative of scalp contamination in the DCS BFI measurement. 24,50 The insensitivity of the DCS PI measurement to scalp pressure, however, shows that scalp contamination is substantially less severe in the PI measurement. Future work is needed to better understand the theoretical explanation of this finding. The result is likely in part a consequence of the brain tissue being less compliant than peripheral tissues (e.g., scalp). 38 The lower compliance in the brain translates to less attenuation of the pulsatility in the microvasculature of the brain tissue compared to that of the scalp tissue.
It is important to note that for some of the subjects, PI changed considerably with changes in probe pressure (Fig. 6). In the comparison of relative changes in PI to relative changes in probe pressure across all subjects, the slope of the best-fit line was nearly zero, but the interquartile range of fractional deviation in the rPI measurements from the best-fit line was 39% ( Table 2). Some of this heterogeneity likely arose from confounds that can be addressed in future work, such as low signal-to-noise, our assumption of constant tissue optical properties, and intersubject variations in scalp/skull thickness (see Sec. 4.4).
Overall, the low sensitivity of the DCS PI to scalp contamination has exciting implications for the ability of DCS to accurately monitor PI in the brain. Future work is needed to compare prolonged DCS PI monitoring to invasive ICP monitoring.

Steady-State BFI Measurements at 3.1-cm Source-Detector Separation
Although signal-to-noise was too low to resolve flow pulsatility at 3.1-cm source-detector separation, measurement of steadystate BFI was still feasible. For the forearm, the slopes of BFI versus muscle tissue pressure for the data obtained at 3.1-and 2.1-cm separations were the same (Fig. 5, Table 2), as well as the absolute BFI measurements at baseline. This result is reasonable since the forearm is, to a good approximation, homogenous tissue. For the forehead, the attenuation in BFI with increasing scalp pressure was less severe in the 3.1-cm separation data than the 2.1-cm separation data (Fig. 5, Table 2). At a probe pressure of 200 mmHg, on average, the attenuation in the BFI at 2.1-cm separation was ∼70% lower than the initial baseline pressure, whereas the attenuation in the BFI at 3.1-cm separation was ∼35% lower (Fig. 5). Accordingly, the steadystate BFI at 2.1-cm source-detector separation was substantially more sensitive to scalp changes than the BFI at 3.1-cm separation (i.e., the median scalp sensitivity at 2.1-cm was roughly 1.5 times higher than the scalp sensitivity at 3.1 cm, see Sec. 3.2). Our latter finding on scalp sensitivity is roughly consistent with a previously published simulation of healthy adults, wherein scalp sensitivity at 2 cm was reported to be ∼1.3 times higher than scalp sensitivity at 3 cm. 51 The absolute BFI at baseline acquired at 3.1-cm separation, however, was not significantly different from the absolute BFI acquired at 2.1-cm separation (Sec. 3.2). This last result is unexpected, since in healthy adults, cerebral blood flow is roughly a factor of 6 higher than scalp flow. 51,52 Consequently, if the 3.1-cm separation was substantially more sensitive to the brain, a higher BFI at 3.1-cm separation is expected. Indeed, the BFI at 3.1-cm trended higher than 2.1, but the trend was not significant because of high variability across subjects. We note the high variability in absolute BFI across subjects could in part be an artifact from our assumption of equal optical properties for all subjects.
Finally, our experimental results provide direct evidence only about scalp sensitivity, not brain sensitivity. Specifically, the variation in scalp tissue pressure will influence scalp blood flow, but it would not influence the skull.

Limitations
A well-known limitation of DCS is low signal-to-noise ratio, which prevented accurate PI measurements at 3.1-cm sourcedetector separation. Specifically, in the Fourier transform of the temporal BFI data acquired at 3.1-cm separation, there was not a clear peak at the heart rate frequency above the noise floor. In our previous work, we found that for a single-detection channel, DCS signal intensities of >50 kcps were optimal for fast DCS measurement of blood flow pulsatility at the heart rate. 8 The DCS signal intensities were less than this on both the forearm (i.e., 29 AE 13 kcps) and forehead (i.e., 12 AE 3 kcps) at 3.1-cm separation. It will be important future work to implement strategies to boost signal-to-noise in DCS to achieve greater depth sensitivity to the brain. 4,[53][54][55][56][57] Notably, in this study, there was only one single mode DCS detection fiber at each separation.
With instrumentation that has more DCS detection channels, it is possible to measure PI at longer separations beyond 2.1 cm to increase sensitivity to the brain. Increased brain sensitivity will in turn mitigate confounds in DCS PI measurements. For instance, though our measurements at 2.1 cm provide clear evidence of DCS sensing arterial flow beyond the scalp on the forehead, variations in brain sensitivity arising from variations in skull thickness (and potentially skull vascularization) may confound comparisons across subjects. DCS measurements acquired at longer separations are less sensitive to this source of error.
This study also assumed constant tissue optical properties in deriving BFI. The DCS PI calculation, which is based on relative changes in BFI, is expected to be relatively robust to errors in the assumed tissue optical properties, 46 though in the future, accurate measurement of absolute optical properties is desirable. Errors in the optical properties will be more pronounced in the steady-state BFI measurements. 23,25,26 For this reason, caution is warranted for quantitative interpretation of the absolute BFI versus pressure slopes reported.

Conclusion
We investigated the influence of pressure on the DCS PI measurements on the forearm and forehead acquired at 2.1-cm source-detector separation on eleven healthy adults. The strong correlation between DCS PI and muscle tissue pressure demonstrates the sensitivity of PI to the tissue pressure compressing the arterial microvasculature. The lack of correlation between DCS PI measurements on the forehead and scalp tissue pressure indicates that DCS senses arterial flow beyond the scalp on the forehead. Together, these results suggest the promise of using DCS PI measurements to aid timely noninvasive detection of trends in ICP in the clinic.

Disclosures
No conflicts of interest, financial or otherwise, are declared by the authors.