Original Paper. Detection of Blood Flow Speed in Shallow and Deep Tissues Using Diffuse Correlation Spectroscopy. Mikie NAKABAYASHI, *, # *, **

Advanced Biomedical Engineering 6: 53 58, 2017. Original Paper DOI:10.14326/abe.6.53 Detection of Blood Flow Speed in Shallow and Deep Tissues Using Diffuse Correlation Spectroscopy Mikie NAKABAYASHI, *, # *, ** Yumie ONO Abstract Diffuse correlation spectroscopy (DCS) is an emerging optical technique for noninvasive measurement of hemodynamics of living tissues. Using emitter and detector optical probes attached to the body surface, DCS estimates the mean speed of blood flow in the tissue, through which the emitted near-infrared light propagates (blood flow index: BFI). The advantage of DCS is that the mean blood flow in deeper tissues such as muscle layers can be measured noninvasively. To investigate the sensitivity of DCS in detecting the physiological changes of blood flow in deep and shallow tissues, we measured the blood flow speed in 14 healthy participants during a reactive hyperemia test and skin temperature changes. In the reactive hyperemia test, blood flow returned to the steady state faster in deep tissues than in shallow tissues, and temperature-dependent reallocation of local blood flow in shallow and deep tissues was clearly observed. These results demonstrate that DCS can measure the differences in physiological blood flow dynamics in deep and shallow tissues, suggesting the potential use of DCS to noninvasively quantify changes at microcirculation level in both shallow and deep tissue layers. Keywords: diffuse correlation spectroscopy, blood flow speed, reactive hyperemia test, thermoregulatory control of blood flow. Adv Biomed Eng. 6: pp. 53 58, 2017. 1. Introduction Patients with diabetes mellitus have a high risk of capillary disorders that could lead to serious complications such as retinopathy and nephropathy. However, the early stages of diabetic capillary disorders usually have no symptoms, making it difficult for patients to be aware of their microcirculation status. An easy-to-use, low-cost, and noninvasive device that allows regular assessment of the microcirculation status is required to motivate the patients to improve their lifestyle and/or receive further medical treatment. Previous studies reported the application of a laser Doppler blood flowmeter to evaluate vascular reactivity and its usefulness in classifying diabetic patients and their risk of developing cardiovascular disease [1]. However, the measurement principle of the laser Doppler flowmeter only allows measurement of blood flow at the surface of the living body. However, capillary disorders occur not only in epidermal tissue, but also in deep tissues such as muscles [2, 3], and diabetic pathophysiology differs depending on the tissue type [4]. Interestingly, a recent epidemiological study has revealed that patients with diabetes have greater arm and leg muscle masses than age- and gender-matched controls without diabetes, but poorer muscle quality defined as the muscle strength per unit regional muscle mass [5]. Since the impaired motor function could further cause inactivity of patients This study was presented at the Symposium on Biomedical Engineering 2016, Asahikawa, September, 2016. Received on July 22, 2016; revised on October 30, 2016 and January 31, 2017; accepted on February 19, 2017. * Graduate School of Science and Technology, Meiji University, Kanagawa, Japan. ** Department of Electronics and Bioinformatics, School of Science and Technology, Meiji University, Kanagawa, Japan. # 1 1 1 Higashi-Mita, Tama-ku, Kawasaki-shi, Kanagawa 214 8571, Japan. E-mail: 2wh3230g3r6285k@gmail.com and worsen the disease condition, monitoring the microcirculatory function in both shallow and deep tissues is beneficial to further understand the pathophysiology of diabetic capillary disorders. Therefore, we apply diffuse correlation spectroscopy (DCS) [6, 7], an optical technique, to measure the blood flow speed with the aim to develop a noninvasive and low-cost system that can evaluate the microcirculation in both shallow and deep tissues. An advantage of DCS over the laser Doppler flowmeter is its capability to detect blood flow in deeper tissues. Since DCS estimates the mean speed of blood flow in the tissue through which the emitted near infrared light propagates, the depth of the target tissue can be adjusted depending on the inter-probe distance. Since the sensitivity volume of the emitted light has a banana shape between the source and detector optical fibers, greater inter-probe distances may contain more blood flow information in deeper tissues [6, 8, 9]. Through numerical simulations and actual bio-optical measurements, the approximate depth of the DCS measurement below the skin surface is estimated to be one-third to one-half of the inter-probe distance [8, 9]. Furthermore, skin color or melanin is considered to have no effect on DCS signal in theory, because DCS measures the photons that reach the detector and detect the fluctuation of photon number as signal. Although DCS requires a long coherent laser system and does not provide three-dimensional hemodynamic properties of the tissue, it has a unique advantage over other diffuse optical techniques such as near infrared spectroscopy and diffuse optical tomography [6]. DCS allows direct measurement of the blood flow volume (blood flow speed sensitivity volume of the emitted light) instead of provide indirect information on the blood flow indicated by changes in oxy- and deoxy-hemoglobin concentrations. Although DCS has been applied to investigate the hemodynamic response of tumors, muscles, and neonatal brains [6], little attention has been paid to differentiate the vascular response depending on the tissue type [9]. In this study, two types of physiological studies; reactive hyperemia and skin temperature changes,

(54) Advanced Biomedical Engineering. Vol. 6, 2017. were conducted on healthy participants to investigate whether DCS with different inter-probe distances is able to separately measure the hemodynamic changes occurring on the skin surface and in deeper tissues including muscle layers. 2. Materials and methods 2.1 Participants Fourteen healthy young volunteers (seven males and seven females, aged 22.3 ± 0.9 years) without a previous history of peripheral vascular diseases participated in the study. All study procedures were approved by the institutional review board of the Department of Science and Technology, Meiji University. All participants gave written informed consent prior to participation. 2.2 Diffuse Correlation Spectroscopy [6, 7] Incident near infrared light propagates through living tissues, is scattered by blood cells, and then reaches the detector probe at the skin surface. The scattered light is detected by the photon counter as the number of photons. Time course data of the number of photons detected are sent to the computer and analyzed by an inhouse software developed using Laboratory Virtual Instrument Engineering Workbench (Labview). The detected data are represented as a waveform of light intensity with time variation. The time course of changes in light intensity varies depending on the speed of the red blood cells, the object of near infrared light scattering in the body. To calculate the time variability of the detected light intensity, the following normalized intensity autocorrelation function (1) can be obtained: I(r, t) I(r, t + τ) G 2 (r, τ) = (1) I(r, t) 2 Here, G 2 (r, τ) is the autocorrelation function of the normalized light intensity, I(r, τ) is the detected light intensity at position r and time t, τ is the delay from t, and the brackets represent time-averages. When the delay is small, the correlation is high due to small change in light intensity and the value of G 2 (r, τ) is close to one. On the other hand, when the delay is large, the correlation reaches zero due to less similarity in the time-shifted light intensity functions. In other words, when the speed of blood flow is low, the value of G 2 (r, τ) remains high even if τ becomes large. When the speed of blood flow is high, the value of G 2 (r, τ) quickly reaches zero even for smaller τ values. Therefore, the decay ratio of G 2 (r, τ) corresponds to the speed of blood flow. To quantify the speed of blood flow from the measured G 2 (r, τ) data, we conduct an approximation of the measured autocorrelation data using the theoretical autocorrelation function. Considering an analytical solution (Green s function solution) of the diffusion correlation equation for a point light source on the semi-infinite plate medium, the theoretical autocorrelation function (2) [6, 7] can be obtained as: g 2 (r, τ) = 1 + β k D = 3µ s 4π e k D r 1 r 1 e k D r 1 r 2 2 3µ s µ a + 6µ s 2 k 2 0 αd Bτ r 1 = ρ 2 + z 2 0, r 2 = ρ 2 + (z 0 + 2z b ) 2 I(r, t) 2 (2) z 0 = 1 µ s, z b = 2(1 R e f f ) 3µ s (1 + R e f f ) Here, β is a constant, μ s is the reduced scattering coefficient, μ a is the absorption coefficient, α is the fraction of photon scattering events from moving scatterers out of total scatterers, k 0 is the wavenumber of the light in a medium, D B is the effective diffusion coefficient of the scatterers, R eff is the effective reflection coefficient, and ρ is the distance between the source and detector. The outline of Equation (2) is determined by k D. The value obtained from multiplying α and D B in k D represents the blood flow index (BFI) of the DCS. In practical DCS measurement, we use the steepest descent method to obtain β and αd B so that the difference between the theoretical and measured autocorrelation function is minimized. Hereinafter, the measurement obtained from DCS is represented by the value of BFI. The DCS system consists of a long coherence, continuous wave laser operating at 690 nm (DL690-050-S, 50 mw, Crysta- Laser, Reno, Nevada, USA) as the source and a photon-counting APD (tau-spad-50, PicoQuant, Berlin, Germany). The entire output is a stream of transistor-transistor logic (TTL) pulses. The light from the source is coupled to a multi-mode optical fiber (FT400EMT, Thorlabs Japan Inc., Tokyo, Japan) to make contact with the skin surface. The detecting fiber is single-mode operated (SM600, Thorlabs Japan Inc., Tokyo, Japan), and located at inter-probe distances away from the source fiber and fed to the APD. The TTL output generated by the APD is collected with a 32-bit counter board (USB-6341, National Instruments, Austin, TX, USA) connected to a PC (CPU: Intel Core i5, RAM: 4-Gbyte). The Labview program collects the light intensity data at a sampling rate of 100 ks/s (kilo-sampling per second) and determines the BFI value for every 1 s. 2.3 Experimental and analysis methods Participants sat comfortably in a chair with their forearm placed on an armrest. In order to prevent extraneous light contaminating the detector probe, experiments were performed in a dark room. 2.3.1 DCS measurement in the reactive hyperemia test The reactive hyperemia test of the forearm is a model commonly used to study microvascular functions [1]. Figure 1 is the representative time course of blood flow changes in healthy blood vessels in the reactive hyperemia test. After measuring the baseline level in the resting state, the forearm blood flow is occluded for several minutes by inflating a cuff wrapped around the upper arm. Reperfusion by releasing the cuff pressure causes a transient overshoot in blood flow due to the ischemia-induced vasodilatory response, which returns to the baseline state in a few minutes. Clinically important indices of vascular reactivity in the reactive hyperemia test are the peak normalized BFI during overshoot (PORHmax), time to reach the peak after release (Tp: time to peak), and decay time from the peak. The participant attached the cuff of a blood pressure meter around the right upper arm and placed the DCS probes on the right forearm (Fig. 2). Vascular measurements were made for three different inter-probe distances of 0.5, 1.5, and 2.5 cm. Each test was 420 s in duration, consisting of 60 s baseline, 180 s occlusion, and 180 s post-occlusion measurements. The cuff pressure was set at 30 mmhg or higher than the individual systolic blood pressure to maintain occlusion. The raw BFI data were first averaged over participants to compare the mean time course of BFI among different inter-probe distances. The raw BFI time courses were manually investigated

Mikie NAKABAYASHI, et al: Detection of Blood Flow Speed in Deep Tissues (55) Fig. 1 Representative blood flow response in reactive hyperemia test. Fig. 2 Schematic representation of DCS measurement in reactive hyperemia test. Fig. 3 DCS measurement at different skin temperatures. and those with serious motion artifacts were excluded from analysis. The individual BFI time courses were then normalized by the mean baseline BFI value to determine the following parameters: (1) peak normalized BFI during overshoot (PORHmax), (2) time to reach the peak after cuff release (Tp: time to peak), (3) 50% decay time from the peak (50% decay time). Statistical differences in the parameters were investigated using one-way repeated measurements analysis of variance (ANOVA) based on the normality of the data. 2.3.2 DCS measurement at different skin temperatures The participant attached a small and flexible water pad (12 and 19 cm in length and width, respectively) over the right forearm, and DCS probes were inserted into the slits of the water pad to make contact with the skin surface (Fig. 3). The water pad was connected to a water reservoir with a thermal regulator. Water was circulated between the reservoir and water pad throughout the experiment to maintain the temperature of the water pad. We measured the BFI under resting condition at three different skin temperatures (43, 25, and 10 C) and two different inter-probe distances (0.5 and 2.5 cm). Measurement was conducted 10 min after changing the water temperature of the water pad. One-minute DCS measurement in the resting state was repeated at two different inter-probe distances for each skin temperature condition. We calculated the average BFI of all participants under each skin temperature condition and for each inter-probe distance. We calculated two ratio indices to assess the hemodynamic changes in deep and shallow tissues as skin temperatures varied. First, we defined an allocation ratio by dividing the BFI obtained at 2.5 cm inter-probe distance by that at 0.5 cm inter-probe distance. The allocation ratio indicates the ratio of blood flow in deep tissue to that in shallow tissue. We investigated the change in allocation ratio among the temperature conditions tested. Second, at a fixed inter-probe distance of 0.5 cm or 2.5 cm, we normalized the BFI obtained at cold (10 C) and warm (43 C) skin temperatures by that obtained at 25 C skin temperature. This relative BFI provides information on the relative change in blood flow at different skin temperatures in shallow and deep tissues. Statistical differences in the parameters were investigated using the Friedman test because it was not based on the normality of the data. 3. Results 3.1 DCS measurement in the reactive hyperemia test We excluded unstable data due to motion artifacts or insufficient contact between optical probes and skin surface. Therefore, 11 data sets for inter-probe distances of 0.5 and 1.5 cm, and 9 data sets for an inter-probe distance of 2.5 cm were analyzed and subject to statistical comparisons. Figure 4 shows the result of the time courses of mean BFI for different inter-probe distances. Although the baseline values decreased as the inter-probe distance increased, the time courses of BFI maintained the general characteristics of the reactive hyperemia test. The blood flow was suppressed to zero with occlusion, and the post-occlusion overshoot was clearly observed in all cases. The PORHmax was 356.7 ± 36.7, 341.9 ± 36.8, and 529.7 ±

(56) Advanced Biomedical Engineering. Vol. 6, 2017. 118.3% (Mean ± SE) at inter-probe distances of 0.5, 1.5, and 2.5 cm, respectively. There were no significant differences in PORHmax among the three inter-probe distances. Figures 5 and 6 show the mean Tp and 50% decay times at different inter-probe distances. The Tp were 21.8 ± 3.0, 20.4 ± 2.7, and 19.6 ± 2.4 s at inter-probe distance of 0.5, 1.5, and 2.5 cm, respectively. The corresponding 50% decay times were 38.6 ± 5.7, 32.0 ± 2.8, and 29.0 ± 3.8 s. The Tp and 50% decay time tended to decrease as the inter-probe distance increased, although the differences did not reach statistical significance. 3.2 DCS measurement at different skin temperatures Figure 7 shows the mean BFI at three skin temperature conditions for 0.5 cm and 2.5 cm inter-probe distances. A significant difference in BFI was observed between 43 C and 25 C and between 43 C and 10 C, regardless of the inter-probe distance. Additionally, significant differences in BFI between inter-probe distances of 0.5 cm and 2.5 cm were observed for all three skin temperature conditions. Figure 8 shows the allocation ratio of BFI at 2.5 cm to that at 0.5 cm for each skin temperature. The allocation ratio was larger at 10 C than at 43 C, suggesting an allocation shift of blood flow to deep tissue at lower skin temperature. A significant difference in allocation ratio was observed between the skin temperatures of 10 C and 43 C. Figure 9 shows the relative BFI at 43 C and 10 C (relative to the BFI at 25 C) at inter-probe distances of 0.5 and 2.5 cm. The relative BFI in shallow tissues (0.5 cm inter-probe distance) decreased slightly under cooling condition and increased approximately 5-fold under warming condition. There was a significant difference in relative BFI between 43 C and 10 C in shallow tissues. Although a significant difference in relative BFI between 43 C and 10 C was also observed in deep tissue (2.5 cm inter-probe distance), the response to skin temperature change was different from that seen in shallow tissue. Fig. 4 Mean BFI time-course at three different inter-probe distances (0.5, 1.5, and 2.5 cm). n is the number of participants. The baseline values decrease as the inter-probe distance increases. Fig. 5 Mean time to peak for three different inter-probe distances. Fig. 7 Mean BFI for two inter-probe distances at different skin temperature conditions. The secondary axis (right) only applies to the BFI at 43 C and 0.5 cm inter-probe distance (gray bar). Asterisks indicate significant differences versus 43 C condition for the same inter-probe distance (p < 0.05). Daggers indicate significant differences versus 0.5 cm inter-probe distance for the same temperature condition (p < 0.05). Fig. 6 Mean 50% decay time for three different inter-probe distances. Fig. 8 Temperature-dependent changes in allocation ratio of BFI. The asterisk indicates a significant difference (p < 0.05).

Mikie NAKABAYASHI, et al: Detection of Blood Flow Speed in Deep Tissues (57) Fig. 9 Temperature-dependent changes in relative BFI at two inter-probe distances. Asterisks indicate significant differences versus 43 C condition for the same inter-probe distance (p < 0.05). In deep tissue (inter-probe distance 2.5 cm), the relative BFI showed a slight increase under cooling condition and the increase in relative BFI under warming condition was limited to just above 2-fold. Although there were no significant differences in relative BFI between inter-probe distances of 0.5 cm and 2.5 cm under both skin temperature conditions, the increase was much smaller for deep tissue. The relative BFI increased slightly under cooling condition and decreased under warming condition as the inter-probe distance increased. 4. Discussion We analyzed the blood flow responses to reactive hyperemia and skin temperature changes using a DCS with short and long inter-probe distances to investigate the sensitivity of DCS in detecting physiological changes of blood flow in deep and shallow tissues. The absolute BFI value decreased with increasing inter-probe distance, possibly due to the difference in optical path length for different inter-probe distances. Therefore, we calculated the baseline-normalized BFI to assess the peak values in the reactive hyperemia test, and found that they were comparable regardless of the inter-probe distance. In contrast, Tp and 50% decay time tended to decrease with increase in inter-probe distance. Since deep tissues that contain muscle have more capillaries than epidermal tissue [10, 11], more vasodilator metabolites accumulate and dilate blood vessels during occlusion [12, 13]. This is supported by the simultaneous DCS and tissue oxygen saturation measurements performed by Yu et al [9], showing that deeper tissues consume more oxygen than the shallow tissues during cuff occlusion. In other words, deeper tissues have a larger capacity to accept the re-perfused blood flow stream in a short time than epidermal tissues after releasing the upper arm from occlusion. The shorter Tp obtained with longer inter-probe distance well supports this physiological hypothesis. The higher density of capillaries in deep tissues is also beneficial to quickly supply blood flow to the surrounding tissues and return to a steady state, which was confirmed by the shorter 50% decay time with increasing inter-probe distance. These results suggest the possibility of using DCS to noninvasively monitor the vascular responses from both shallow and deep tissues using short and long inter-probe distances. The experiment of skin temperature change was conducted to further confirm the capability of DCS to detect physiological blood allocation between the skin surface and deeper tissues. Body temperature control is one of the human homeostatic functions. When the body is exposed to a warm state, blood flow increases at the skin surface and decreases in deep tissues for heat dissipation. When the body is exposed to a cold state, blood flow decreases at the skin surface and increases in deep tissue increases to retain heat [14]. In accordance with these physiological phenomena, the allocation ratio of deep to shallow tissues showed an increase at lower skin temperature, suggesting that more blood flow is preserved in deeper tissue. Warming the skin surface lowers the allocation ratio, which explains the marked and moderate increases in relative BFI at short and long inter-probe distances, respectively. The decrease in relative BFI under warming condition suggests that more blood moves to the skin surface in order to dissipate the heat. These results clearly demonstrate the ability of DCS to detect physiologically induced blood flow changes in living human tissues. Although the relative BFI tended to decrease at short inter-probe distance and increase at long inter-probe distance, the change in relative BFI under cooling condition was very small compared to that under warming condition. The asymmetric tendency of thermal response in blood circulation to cooling and warming of skin temperature suggests a possibility that cooling is not sufficient to effectively cause vascular constriction in the superficial tissues. It is supported by the smaller difference in allocation ratio between cooling and room temperature conditions, compared to that between warming and room temperature conditions. Further experiments with more different temperature conditions may clarify this point. These two studies of blood flow responses demonstrated that DCS was capable of detecting different hemodynamics in both the skin surface and deep tissue. In these experiments, we set the shortest and longest inter-probe distances at 0.5 cm and 2.5 cm, respectively. Our preliminary study using ultrasound tomography of the forearms in eleven age- and gender-matched young healthy participants showed that the thickness of the epidermal tissue above the muscle layers was 3.77 ± 0.23 mm. Therefore, the inter-probe distances of 0.5 cm and 2.5 cm used in DCS correspond to measurement of photon diffusion within the epidermal and muscle tissues, respectively. However, photons that reach the longer detector probe also pass through the epidermal layer, which may affect the detected diffusion characteristics. Numerical simulations and phantom experiments that individually control the shallow and deep blood flow would help further confirm the origin of the DCS signals. 5. Conclusion We demonstrated the use of DCS to monitor the differences in physiological blood flow dynamics, suggesting the potential use of DCS to noninvasively quantify the microcirculation status in both shallow and deep tissue layers. Future research combining the evaluation of vascular reactivity and muscle quality would further contribute to the understanding of the pathophysiology of diabetic capillary disorders in skeletal muscles, which may lead to early detection in diabetic patients with a high risk of motor function impairment.

(58) Acknowledgement Advanced Biomedical Engineering. Vol. 6, 2017. pp. 282 286, 2009. The authors thank Professor Kijoon Lee (Daegu Gyeongbuk Institute of Science and Technology, Korea) and Professor Masashi Ichinose (Meiji University, Japan) for their valuable comments on data collection. Conflict of Interest This research was supported by JSPS KAKENHI Grant Number JP 16H03242. References 1. Yamamoto-Suganuma R, Aso Y: Relationship between post-occlusive forearm skin reactive hyperemia and vascular disease in patients with Type 2 diabetes a novel index for detecting microand macrovascular dysfunction using laser Doppler flowmetry. Diabet Med. 26, pp. 83 88, 2009. 2. Womack L, Peters D, Barrett EJ, Kaul S, Price W, Lindner JR: Abnormal skeletal muscle capillary recruitment during exercise in patients with type 2 diabetes mellitus and microvascular complications. J Am Coll Cardiol. 53(23), pp. 2175 2183, 2009. 3. Mohler ER 3rd, Lech G, Supple GE, Wang H, Chance B: Impaired exercise-induced blood volume in type 2 diabetes with or without peripheral arterial disease measured by continuous-wave near-infrared spectroscopy. Diabetes Care. 29(8), pp. 1856 1859, 2006. 4. Malik RA: Pathology of human diabetic neuropathy. Handb Clin Neurol. 126, pp. 249 259, 2014. 5. Park SW, Goodpaster BH, Strotmeyer ES, de Rekeneire N, Harris TB, Schwartz AV, Tylavsky FA, Newman AB: Decreased muscle strength and quality in older adults with type 2 diabetes: the health, aging, and body composition study. Diabetes. 55(6), pp. 1813 1818, 2006. 6. Durduran T, Choe R, Baker WB, Yodh AG: Diffuse optics for tissue monitoring and tomography. Reports on Progress in Physics. 73, 076701, 2010. 7. Dong J, Bi R, Ho JH, Thong PS, Soo KC, Lee K: Diffuse correlation spectroscopy with a fast Fourier transform-based software autocorrelator. J Biomed Opt. 17(9), 97004 1, 2012. 8. Patterson MS, Andersson-Engels S, Wilson BC, Osei EK: Absorption spectroscopy in tissue-simulating materials: a theoretical and experimental study of photon paths. Appl Opt. 34(1), pp. 22 30, 1995. 9. Yu G, Durduran T, Lech G, Zhou C, Chance B, Mohler ER 3 rd, Yodh AG: Time-dependent blood flow and oxygenation in human skeletal muscles measured with noninvasive near-infrared diffuse optical spectroscopies. J Biomed Opt. 10(2), 024027, 2005 10. Hermansen L, Wachtlova M. Capillary density of skeletal muscle in well-trained and untrained men. J Appl Physiol. 30(6), pp. 860 863, 1971. 11. Altintas AA, Vogt PM, Altintas MA. Acute effects of splint immobilization of the forearm on in vivo microcirculation and histomorphology of the human skin. Microsc Res Tech. 77(1), pp. 99 103, 2014. 12. Carlsson I, Sollevi A, Wennmalm A. The role of myogenic relaxation, adenosine and prostaglandins in human forearm reactive hyperemia. J Physiol. 389, pp. 147 161, 1987. 13. Joyner MJ, Dietz NM. Nitric oxide and vasodilation in human limbs. J Appl Physiol. 83(6), pp. 1785 1796, 1997. 14. Barrett K, Brooks H, Boitano S, Barman S: Ganong s Review of Medical Physiology 23 rd Ed. McGraw-Hill Medical, New York, Mikie NAKABAYASHI Mikie NAKABAYASHI received her B.E. degree from the Meiji University, Kawasaki, Japan in 2016. She is currently a Master course student in the Health Science and Medical Engineering laboratory of Electrical Engineering Program, Graduate School of Science and Technology, Meiji University since 2016. Her research interests are on biooptics and biomedical devices. Yumie ONO Yumie ONO received her PhD in Electrical Engineering in 2004 from the Waseda University. After her postdoctoral experience at the Waseda-Olympus Bioscience Research Institute in Singapore, she joined faculty of the Kanagawa Dental College as an Associate Professor of Physiology until she moved to the Department of Electronics and Bioinformatics, School of Science and Technology at Meiji University as an Associate Professor in 2011. Her work focuses on the application of noninvasive brain imaging and biomedical measurement techniques on medicine and rehabilitation. She is a member of Japanese Society for Medical and Biological Engineering, the Japan Neuroscience Society, and Japan Society of Neurovegetative Research.