Quantitative Analysis of Tissue Oxygenation Variability across Anatomical Landmarks in Healthy Individuals via Near-Infrared Spectroscopy

Quantitative Analysis of Tissue Oxygenation Variability across Anatomical Landmarks in Healthy Individuals via Near-Infrared Spectroscopy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Quantitative Analysis of Tissue Oxygenation Variability across Anatomical Landmarks in Healthy Individuals via Near-Infrared Spectroscopy Amir Parham Pirhadi Rad, Parsa Alizadeh, Mehdi Nourizadeh, Kiana Jahanshahi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7070224/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Near-infrared spectroscopy (NIRS) enables noninvasive assessment of tissue oxygenation, but its broader clinical application is hindered by the absence of standardized reference values across anatomical regions. This study aimed to characterize baseline regional tissue oxygen saturation (rSO₂) across 17 anatomical landmarks in healthy adults and to identify the most consistent and reproducible measurement sites. Seventy-eight healthy participants (mean age 31 ± 13.4 years) underwent rSO₂ assessments using a continuous-wave NIRS system. Demographic and physiological data, including age, sex, skin pigmentation, tissue thickness, and mean arterial pressure, were collected. rSO₂ values ranged from 50.5–86.0%, with most values between 65% and 76%. The temporomandibular joint and mandibular ramus had the highest mean rSO₂ (~ 75.8%), while the thenar eminence and forehead showed the lowest. The quadriceps exhibited the lowest inter-individual variability (2.72 SD), making it the most reliable site for baseline measurements. The sternum also showed low variability (2.96 SD), suggesting its usefulness in dynamic monitoring. Age and sex significantly influenced rSO₂ (p < 0.001), while other variables had limited impact. These findings establish normative rSO₂ values and identify optimal NIRS placement sites, supporting standardization in clinical and research applications to improve detection of tissue hypoxia and perfusion abnormalities. Health sciences/Anatomy Health sciences/Health care Health sciences/Medical research Biological sciences/Physiology Near-Infrared Spectroscopy (NIRS)- Tissue Oxygenation- Regional Saturation (rSO₂) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 INTRODUCTION Near-infrared spectroscopy (NIRS) is an optical technique that enables continuous and noninvasive monitoring of changes in tissue oxygenation and hemodynamics by utilizing light in the near-infrared spectrum (650–1000 nm). NIRS can penetrate several millimetres into biological tissue depending on the sensor size and placement, tissue structure, and skin pigmentation. In recent years, there has been a growing interest within the medical device field regarding non-invasive diagnostic and monitoring methodologies. Notably, NIRS has gained prominence due to its ability to monitor tissue and organ hemodynamics and function in healthy states and disease conditions. 1 – 3 While NIRS is a relatively new technology in medicine, it is commonly used to monitor cerebral oxygenation during and after surgical procedures. 4 NIRS quantifies changes in the concentration of oxygenated hemoglobin (O₂Hb) and deoxygenated hemoglobin (HHb) within the tissue. These chromophores exhibit distinct absorption spectra, enabling NIRS sensors to determine their concentrations in tissue by analyzing near-infrared light absorbance at specific wavelengths. 4 NIRS devices are categorized into three main types: continuous wave (CW), frequency domain (FD), and time domain (TD), with CW-NIRS being the most widely used due to its compact design and cost-effectiveness. 4 Unlike FD and TD systems, CW-NIRS can only measure relative changes in O₂Hb and HHb concentrations. However, these values can be used to compute regional tissue oxygen saturation (rSO₂), which provides an estimate of local tissue oxygenation. 1 This is the primary NIRS metric used in clinical applications. By analyzing the spatial gradient of light attenuation across multiple source-detector distances, CW-NIRS systems in spatially resolved (SR-NIRS) configuration provide more accurate estimation of rSO₂. 5 , 6 Light is emitted at near-infrared wavelengths and detected at several distances from the source, enabling calculation of the slope of optical density (OD) with respect to distance (∂OD/∂ρ). This slope is largely influenced by the tissue absorption coefficient (µₐ) and minimally affected by scattering, thus enhancing measurement accuracy. By acquiring data at two or more wavelengths, typically around 760 nm and 850 nm, where deoxygenated and oxygenated hemoglobin absorb differently, the concentrations of O 2 Hb and HHb are derived through the solution of simultaneous equations based on the modified Beer–Lambert Law and diffusion theory. Finally, rSO₂ is computed as the ratio of O 2 Hb to the total hemoglobin concentration (THb = O 2 Hb + HHb) expressed as a percentage (Eq. 1). This approach allows for more accurate, depth-sensitive, and scattering-independent estimation of tissue oxygenation compared to single-distance NIRS methods. rSO 2 = \(\:\frac{O2Hb}{(O2Hb+HHB)}*100\) Equation 1: rSO2 Calculation Measuring baseline rSO₂ and monitoring its dynamic changes are critical components of clinical decision-making in a variety of settings, including surgery, critical care, tissue monitoring and rehabilitation. A baseline measurement provides a patient-specific reference that reflects individual differences in tissue oxygenation and perfusion. Monitoring deviations from this baseline, rather than relying solely on absolute thresholds, can offer early insight into physiological compromise. For instance, during monitoring a surgical flap hemodynamics, a decline in tissue rSO₂ may indicate impaired oxygen delivery to the reconstructed flap even when systemic parameters such as arterial oxygen saturation or blood pressure remain within normal limits. In other contexts, such as recovery from vascular compromise, changes in regional tissue rSO₂ can help assess the adequacy of perfusion and guide therapeutic decisions. This approach allows clinicians to respond proactively to evolving physiological changes, supporting more tailored and timely interventions. Therefore, insights on normal ranges of tissue rSO 2 in different organs is essential to apply NIRS in clinical settings. NIRS-derived rSO₂ measurements are influenced by several physiological and technical factors, including the assumed arterial-to-venous blood ratio (commonly approximated as 25:75), sensor placement, local tissue composition and thickness, and the configuration and type of NIRS device used. 7 In cerebral applications, particularly within the frontal cortex, normal rSO₂ values typically range from 55–75%, reflecting the venous-weighted nature of the NIRS signal under resting conditions. 7 In contrast, skeletal muscle rSO₂ values are generally higher, ranging from 60–85%, although they can vary substantially in response to changes in perfusion, vascular tone, and physical activity. 8 , 9 In neonatal and critical care populations, renal and splanchnic rSO₂ values are also reported within a similar range of 60–85%. 10,11 Clinically, a cerebral rSO₂ value below 50% or a reduction greater than 20% from baseline is often regarded as significant, potentially indicating cerebral hypoxia or ischemia. 12 The clinical utility of NIRS in monitoring regional tissue and organ oxygenation relies on a thorough understanding of normative rSO₂ values across different tissues and physiological contexts. The primary aim of this study was to characterize the normative ranges of tissue oxygenation across multiple anatomical regions of healthy individuals using NIRS. Additionally, we aimed to identify which anatomical site yields the most consistent and reliable rSO2 measurements under baseline physiological conditions. This information helps clinicians and researchers to recognize deviations indicative of regional tissue hypoxia or ischemia, thereby supporting timely and targeted clinical interventions. MATERIALS AND METHODS Participants A cohort of healthy adults was recruited. The inclusion criteria were being between 19 and 70 years old, having good general health, and having no known neurological disorders or musculoskeletal impairments. Participants were excluded from the study if they had a severe cardiovascular, respiratory, or neurological condition or if they had a severe skin condition at any of the sensor placement sites. Furthermore, participants were excluded if they used medications that affect vascular tone or blood flow; if they had known allergies to materials used in the NIRS sensors or had previous adverse reactions to skin-based measurements. Participants were excluded if they were pregnant or had severe cognitive impairments or communication barriers that could have hindered their ability to provide informed consent. All participants were informed about the experimental procedure, and informed consent was obtained from all participants prior to the experiment. Instrumentation This study used an FDA-approved clinical NIRS device (Masimo Root® with O3® Regional Oximetry, Masimo Corporation, Irvine, USA). An adult (> 40kg) O3 rSO 2 NIRS sensor (source-detector distance of 35mm) was used to measure rSO 2 at each selected anatomical landmark. Based on accessibility and anatomical variations, the sensor was fixed in place by hand or with Velcro straps, continuously collecting data for 30 seconds, depending on the landmark. The sensor setting was adjusted for selected anatomical landmarks based on available categories in the device setting, including forehead, forearm, chest, flank, upper leg, and calf. Measurements were collected with a sampling rate of 0.5 Hz. A standard pulse oximeter (LNCS DCI® Adult Digit Sensor, Masimo Corporation, Irvine, USA) was placed on the left index finger to monitor heart rate and arterial oxygen saturation (SpO 2 ). Subcutaneous tissue thickness was measured using a skinfold calliper (Skyndex LLC, Albuquerque, USA) at each site. The blood pressure was also measured using a digital blood pressure monitor (Omron Healthcare, model: BP769CAN). Skin pigmentation was measured using a Nix Mini 3 Colour Sensor (Nix Sensor Ltd, Canada) at each measurement landmark. Experimental Protocol Sensor Placement The University of British Columbia Research Ethics Board approved the following research protocol (H24-00171), and the research was conducted in accordance with the guidelines outlined in Policy Number LR9 (Research Involving Human Participants), provided by the University of British Columbia. For each participant, rSO 2 was recorded at 17 anatomical landmarks, including eight bilateral sites: the forehead region, the temporal region, the infraorbital rim region, the temporomandibular joint (TMJ) region, the mandibular ramus region, the thenar eminence, the quadriceps muscle (vastus lateralis) region, the tibia region and the sternum (Fig. 1). The locations were chosen to ensure that several body regions were accounted for with unique anatomical and physiological characteristics. To secure the sensors on the lower limb, two Velcro straps were used to affix them at standardized anatomical landmarks: one on the thigh, positioned approximately one-third of the distance from the lateral condyle of the knee to the greater trochanter, and the other on the tibia, midway between the lateral malleolus and the head of the fibula. Bilateral measurements were acquired simultaneously using two NIRS sensors. To evaluate measurement accuracy and repeatability, data collection was repeated three times in fifteen participants, across three separate sessions on different dates and times, yielding a total of 45 individual data sets. This information has been used to confirm the reproducibility of the measurement through the NIRS sensor for each participant. In the head and neck regions, sensors were placed along the length of the tissue segment of interest, as shown in Fig. 2. Data Collection Prior to data collection, participants were instructed to stand barefoot on the digital scale to capture their body composition data. At the anatomical measurement sites, the area was wiped clean to remove any skin or oil residues, thereby reducing interference with the light. Participants were asked to lie down in a supine position with their eyes open for 5 minutes, maintaining stillness, without any posture adjustments or talking during the data collection period. This step is crucial to ensure that stable blood pressure and tissue oxygenation levels are maintained at a stable value. Blood pressure was measured using an Omron blood pressure monitor. Data collection continued if the participants' blood pressure remained within the acceptable range of 120/80 mmHg ± 20 mmHg (Fig. 3). Skin pigmentation was measured using the Nix Mini 3 Colour Sensor, with the measurements reported as HEX colour codes for each landmark. The HEX colour codes were calibrated using the Monk Skin Tone Scale, ranging from 1 (lightest) to 10 (darkest), to ensure consistent and standardized measurement of skin tones. 13 Subcutaneous tissue thickness was measured at each data collection site using a Skyndex calliper. Each measurement was repeated three times per site to minimize measurement error, and the average of the measurements was reported. The average subcutaneous tissue thickness measurement was calculated by dividing the measurement by two, following the instructions of the calliper. The pulse oximeter was placed on the participant's left index finger to continuously record heart rate and SpO 2 . Although these measurements were not used to calculate rSO 2 , monitoring them throughout data collection was essential. This ensured measurement consistency; in the event of sudden changes in heart rate or SpO 2 , the data collection was repeated on the same or a later day. To collect NIRS data, the Masimo NIRS sensors were positioned at each predefined anatomical location and held in place for approximately 30 seconds to ensure reliable readings. To standardize sensor placement and minimize errors across participants, all measurement sites were pre-marked, and measurements were taken bilaterally, except for the sternum. Troubleshooting Through working with NIRS sensors, we identified several recurring indicators of sensor malfunction, which were resolved through a systematic troubleshooting protocol: 1. Asymmetrical readings (> 5-unit difference between left and right sides): 1.1. Re-adjust sensor placement, clean the sensor surface, and clean the skin area. 1.2. If the discrepancy persists, replace the sensor hardware. 2. Sensor displays “off patient” despite proper placement: 2.1. Adjust sensor placement, clean both the sensor and the skin, and reset the system. 2.2. If unresolved, replace the sensor hardware. 3. Prolonged “initializing values” without data output: 3.1. Re-adjust the sensor, clean the sensor surface and skin area, and perform a system reset. 3.2. Replace the sensor if the issue continues. These errors are often attributable to improper sensor positioning. If such issues arise, the sensor should be removed, the site inspected, and the device repositioned to ensure optimal contact. Equal pressure across bilateral sensors is essential, as unequal pressure can result in data discrepancies. Contaminants such as skin oils or residual debris can interfere with sensor performance. Participants were advised to clean the application site thoroughly with skin wipes. Additionally, sensors were disinfected between uses in different locations to remove any transferred oils or particles before reattachment. Excessive body or facial hair can impede sensor function by obstructing optical contact with the skin. While shaving the area is ideal, it may not always be feasible or acceptable to participants. In such cases, repositioning the sensor slightly to a less hair-dense area improved signal acquisition. RESULTS rSO 2 measurements were obtained from 17 landmark locations (8 bilateral + sternum) on 78 participants (43 male and 35 female). Participants ranged in age from 17 to 45, with an average age of 31 (± 13.4 SD). Table 1 presents the descriptive statistics for all anatomical sites, arranged by increasing standard deviation and a visual summary of rSO₂ values obtained from different landmarks is provided in Fig. 5 . The reproducibility test results for the NIRS measurements indicate that there are no significant differences in rSO2 readings across different sessions (Time-point 1, Time-point 2, and Time-point 3 are relatively consistent) at the same landmark (Fig. 4 ). Descriptive statistics were used to evaluate the reliability of various anatomical landmarks for NIRS sensor placement by assessing the variability in rSO₂ measurements. Inter-participant variability was inferred from the standard deviation (SD), with lower SD values indicating higher reliability. Among the evaluated landmarks, the quadriceps (2.72 SD) and sternum (2.96 SD) demonstrated the highest reliability, suggesting they are the least variable sites for NIRS placement. Conversely, the thenar exhibited the greatest variability (6.31 SD), indicating it is the least reliable landmark in this context. Table 1 Descriptive statistics of rSO 2 across Anatomical Landmarks. Ordered from smallest to largest standard deviation. Utility: To determine baseline rSO 2 ranges at different landmarks in healthy participants. Landmarks N Minimum Maximum Mean Std. Deviation Variance Skewness Kurtosis Shapiro-Wilk p Bilateral Quadriceps 78 68.50 80.00 73.89 2.72 7.41 0.001 -0.74 0.23 Sternum 78 68.00 80.00 74.35 2.96 8.75 -0.05 -0.32 0.13 TMJ 78 69.00 85.50 75.80 3.89 15.11 0.43 -0.29 0.08 Bilateral Ramus 78 65.50 85.50 75.76 3.99 15.91 -0.03 0.34 0.67 Bilateral Orbital Rim 78 65.50 86.00 75.53 4.15 17.18 0.09 -0.37 0.88 Temporal 78 64.00 85.00 74.53 4.41 19.41 -0.25 -0.32 0.45 Bilateral Tibia 78 64.00 84.50 74.51 4.62 21.34 -0.07 -0.51 0.78 Bilateral Forehead 78 58.00 77.50 67.99 4.67 21.84 -0.11 -0.47 0.54 Bilateral Thenar 78 50.50 79.00 65.03 6.31 39.84 -0.08 -0.85 0.12 Estimated differences between eight anatomical landmarks and the bilateral quadriceps, displayed (blue markers) with their corresponding 95% confidence intervals (red horizontal bars). All intervals lie wholly to one side of this line, indicating statistically significant deviations at every site. The bilateral thenar (estimate ≈ − 7, 95% CI ≈ − 10 to − 6) and bilateral forehead (estimate ≈ − 5% CI ≈ − 7.5 to − 4) show pronounced negative differences relative to the quadriceps, whereas the remaining cranio-facial, axial, and lower-limb landmarks exhibit modest positive differences (estimates ≈ + 1.5 to + 2.5) (Fig. 6 ). A multiple linear regression analysis was conducted to examine the associations between tissue rSO₂ and its potential predictors, including sex, age, mean arterial pressure, anatomical landmark, skin pigmentation, and tissue thickness. The regression model accounted for a substantial portion of the variability in tissue oxygenation, with an adjusted R² of 55.7%. Multicollinearity among the explanatory variables was assessed using the Variance Inflation Factor (VIF), with all values falling below 5.0 (VIF < 5.0), indicating no evidence of multicollinearity. Predictors Estimate SE Z p Stand. Estimate (β) 95% Confidence Interval (β) Effect size (r) 95% Confidence Interval (r) Lower Upper Lower Upper Intercept 73.301 1.915 38.288 < 0.001 Age -0.096 0.011 -8.607 < 0.001 -0.229 -0.281 -0.177 -0.309 [-0.370, -0.243] Mean Arterial Pressure 0.003 0.019 0.181 0.856 0.005 -0.048 0.058 0.007 [-0.067, 0.081] Gender (M – F) : 3.266 0.308 10.596 < 0.001 0.290 0.236 0.344 0.371 [0.310, 0.428] Tissue Pigmentation : 1–2 -1.184 0.930 -1.274 0.203 -0.379 -0.963 0.205 -0.048 [-0.121, 0.026] 3–2 -0.410 0.422 -0.972 0.331 -0.189 -0.571 0.193 -0.037 [-0.110, 0.037] 4–2 -0.355 0.427 -0.833 0.405 -0.100 -0.335 0.135 -0.031 [-0.105, 0.042] 5–2 -0.182 0.427 -0.425 0.671 -0.433 -2.430 1.565 -0.016 [-0.090, 0.058] 6–2 -0.692 0.593 -1.169 0.243 -0.965 -2.588 0.657 -0.044 [-0.117, 0.030] 7–2 -2.739 0.776 -3.529 < 0.001 -0.243 -0.379 -0.108 -0.132 [-0.203, -0.059] Landmarks : Bilateral Forehead – Bilateral Quadriceps -4.975 0.764 -6.515 < 0.001 -1.592 -2.072 -1.112 -0.239 [-0.305, -0.169] Temporal – Bilateral Quadriceps 1.669 0.798 2.090 0.037 0.769 0.047 1.492 0.079 [0.005, 0.151] Bilateral Orbital Rim –Bilateral Quadriceps 2.488 0.738 3.370 < 0.001 0.698 0.291 1.104 0.126 [0.053, 0.197] TMJ – Bilateral Quadriceps 2.861 0.771 3.712 < 0.001 6.817 3.212 10.423 0.139 [0.066, 0.209] Bilateral Ramus – Bilateral Quadriceps 2.813 0.770 3.655 < 0.001 3.922 1.815 6.028 0.137 [0.064, 0.207] Bilateral Thenar – Bilateral Quadriceps -7.582 0.888 -8.535 < 0.001 -0.673 -0.828 -0.518 -0.307 [-0.368, -0.241] Bilateral Tibia – Bilateral Quadriceps 1.551 0.764 2.031 0.043 0.496 0.017 0.976 0.076 [0.003, 0.149] Sternum – Bilateral Quadriceps 1.591 0.834 1.908 0.057 0.733 -0.021 1.488 0.072 [-0.002, 0.144] Tissue Thickness 0.279 0.143 1.953 0.051 0.078 -4.30e − 4 0.157 0.074 [0.000, 0.146] Dependent variable: SRO2(Mean) Table 2: Regression analysis of predictors associated with rSO 2 among healthy participants. Using the quadricep as the reference landmark, selected for its high reliability, rSO₂ levels showed statistically significant associations with all other landmarks (Table 2), except for the sternum ( p = 0.057), as visualized in Fig. 3 . Most landmarks exhibited a R = 7.2–13.9% increase in rSO₂ compared to the quadricep. In contrast, the forehead and thenar landmarks showed decreases in rSO₂, with reductions of R = 23.9% and 30.7%, respectively. According to the standardized regression coefficients from the multiple regression model (Table 2), tissue rSO₂ was significantly associated with participants' gender and age. Male participants exhibited R = 37.1% higher rSO₂ levels compared to female participants (β = 0.290***, p < 0.001, r = 0.371, 95% CI [0.310, 0.428]) (Table 2). In contrast, older participants showed a R = 30.9% reduction in rSO₂ levels relative to younger participants (β = −0.229***, p < 0.001, r = − 0.309, 95% CI [− 0.370, − 0.243]) (Table 2). This association indicated a negative relationship between age and rSO₂. As illustrated in Fig. 7 , male participants appeared to exhibit higher rSO₂% levels across all anatomical landmarks. However, due to a violation of the homogeneity of variance assumption, statistical comparisons across all landmarks could not be performed to confirm this trend. As a result, this observation will not be analyzed in depth in the current discussion. Future studies should aim to formally investigate sex-related differences in rSO₂ across anatomical landmarks using methods robust to unequal variances. No significant association was found between rSO₂ and mean arterial pressure ( p = .586). A marginal, non-significant association was observed between tissue thickness and rSO₂, with participants having thicker tissue exhibiting R = 7.4% higher rSO₂ levels (β = 0.279***, p = 0.051, 95% CI [0.310, 0.428]) (Table 2). The skin pigmentation category was not significantly associated with rSO₂, except for category 7 compared to category 2. Participants in pigmentation category 7 demonstrated significantly lower rSO₂ levels (β = −0.243***, p < 0.001, r = − 0.132, 95% CI [− 0.203, − 0.059]) (Table 2). All pigmentation categories were compared against category 2, as category 1 had only two participants and was therefore deemed an unreliable reference group (see study limitations). Similar trends in the effects of skin pigmentation on rSO₂ across all anatomical landmarks are visible in Fig. 8 . However, due to a violation of the homogeneity of variance assumption, further statistical analysis could not be performed to validate these observations. As discussed later, future research is needed to better understand how skin pigmentation influences NIR light transmission, with a potential sub-focus on landmark-specific effects. The assumption of multivariate normality was evaluated both statistically and graphically. The Shapiro–Wilk test yielded a non-significant result ( p > 0.05), suggesting that the residuals were normally distributed. Visual inspection further supported this finding, as the residuals were symmetrically distributed around the mean and closely followed the regression line (see Fig. 9 ), consistent with the assumption of normality. DISCUSSION Normative tissue rSO 2 This study aimed to establish normative regional tissue rSO₂% values across 17 anatomical landmarks in healthy adults using NIRS and to identify anatomical regions that offer the most stable and reproducible rSO₂% measurements under resting physiological conditions. A total of 78 participants were assessed, and bilateral rSO₂% values were collected from the head, upper limb, trunk, and lower limb regions. The analysis demonstrated considerable regional variation in mean rSO₂%, with the highest average levels recorded at the temporomandibular joint (75.80% ± 3.89) and mandibular ramus (75.76% ± 3.99), and the lowest at the thenar eminence (65.03% ± 6.31) and forehead (67.99% ± 4.67). Across all landmarks, the overall range of rSO₂ values in this healthy cohort spanned from 50.5–86.0%, with most regions falling between 65% and 76%. Reference landmark The quadriceps muscle (mean rSO₂ = 73.89% ± 2.72) exhibited the lowest inter-participant variability, making it the most reliable anatomical site for rSO₂ measurement under resting conditions. The sternum also showed low variability (mean = 74.35% ± 2.96) and may serve as a suitable reference rSO₂ in applications involving physical movement. In contrast, the thenar region had the highest variability, limiting its utility as a reference site. These results offer baseline reference values and support the standardized use of NIRS in both clinical monitoring and research involving regional tissue oxygenation. Previous studies have identified the sternum as a highly accurate site for detecting changes in tissue oxygen saturation. 14 This reliability has been largely attributed to its high vascularization, supported by multiple blood supplies, which ensures a stable and continuous perfusion. 14 Additional factors that may contribute to the low variability in NIRS readings at the sternum include the minimal subcutaneous fat in this region and the consistent soft tissue overlying the bone, both of which have been associated with improved NIRS accuracy in prior research. 14 The low inter-participant variability observed in our study may similarly be explained by these anatomical and physiological characteristics. The thenar region exhibited the highest standard deviation in tissue oxygen saturation, indicating it is the least reliable landmark for use as a control site. Although the thenar has been widely used in NIRS research as a key site 15 the high variability observed in our data raises important concerns regarding its reliability for rSO₂ measurement. Due to its small surface area, the thenar region is likely more vulnerable to variations in sensor placement, contributing to the observed inconsistency in measurements. This issue is discussed further in the study limitations section. rSO 2 baseline ranges Regional differences in rSO₂ levels were observed across all anatomical landmarks when compared to the quadriceps (73.89% ±2.72) , except for the sternum (74.35% ±2.96) . This finding emphasizes the importance of the study’s secondary aim: to establish baseline rSO₂% values in healthy individuals across different anatomical regions. These reference values are critical for identifying deviations in pathological tissues and enhancing the diagnostic and monitoring utility of NIRS in clinical settings. 16 Detailed descriptive statistics on each landmark are available in Table 1 . Further discussion of the limitations of these baseline values, particularly their failure to account for age and sex differences, is provided below. Predictor effects on rSO2 Previous studies have reported various physiological and anatomical factors that influence rSO₂, including sex differences 16 – 18 , a negative correlation with age 13 , a positive correlation with mean arterial pressure (MAP) 19 , 20 , as well as interference from subcutaneous fat and skin pigmentation. 21 , 22 In our dataset, multiple predictors contributed to the observed variability in rSO₂ measurements. The influence of each individual factor is discussed in detail below. Sex The data from this study indicate that male participants exhibited higher average rSO₂ levels than females (Male rSO 2 ≥ +5% compared to female) (Fig. 7 ). While some studies have reported higher rSO₂ values in men across several muscle groups at rest, 16 the majority of the literature suggests no significant sex differences across most anatomical landmarks, 17 , 23 with a few studies reporting higher rSO₂ values in women at specific sites. 17 In the context of cerebral oxygenation, evidence consistently shows higher rSO₂ levels in female participants. 18 These sex-based differences in rSO₂ may be explained by a combination of physiological, hormonal, and metabolic factors, including variations in hemoglobin levels, tissue perfusion, and oxygen utilization. 17 Age The results of this study demonstrated a negative relationship between age and rSO₂ levels, aligning with prior findings using NIRS technology and with previous research showing that gas exchange efficiency and oxyhemoglobin saturation decline with physiological aging. 13 , 19 These observations underscore the importance of accounting for both age and sex when establishing reference ranges for rSO₂ to ensure greater accuracy and clinical relevance across diverse populations. It is important to note that this study was conducted on 78 participants (43 male and 35 female). Participants ranged in age from 17 to 45, with an average age of 31 (± 13.4 SD). Mean Arterial Pressure No association was found between MAP and rSO₂ in our study, which contrasts with previous findings that reported a positive correlation between the two variables. 20 , 24 However, those studies induced MAP increases pharmacologically using epinephrine and measured rSO₂ changes before and after drug administration. This suggests that acute elevations in MAP may influence rSO₂, whereas baseline variations in MAP may not have a direct impact on Tissue Thickness A marginal association was observed between tissue thickness and rSO₂ in our study; however, this finding did not reach statistical significance (p = 0.051). In contrast, previous studies have reported reduced accuracy of rSO₂ measurements due to the interference of subcutaneous fat with near-infrared (NIR) light transmission. 21 As a result, these studies recommend accounting for subcutaneous fat levels when interpreting rSO₂ values. Given the variability in subcutaneous tissue across anatomical landmarks, further research is warranted to explore how regional differences in fat thickness may influence rSO₂ measurements. Skin Pigmentation No significant differences in rSO₂ levels were observed across most pigmentation levels, with the exception of a comparison between levels 2 and 7. Previous research has shown that darker skin pigmentation can attenuate NIRS signals, potentially leading to the underestimation of oxygen saturation. 22 The lack of significant differences in our findings may be attributed to several limitations. Notably, some pigmentation groups were underrepresented, resulting in limited statistical power. Additionally, pigmentation data were collected from a single location, while NIRS measurements were taken from multiple anatomical landmarks, each potentially exhibiting different skin tones. Further studies with larger, more diverse samples and site-specific pigmentation assessments are needed to fully understand the impact of pigmentation on rSO₂ measurements. Study Limitations One of the primary limitations of this study was the lack of representativeness across pigmentation categories. The majority of participants fell into category 2, while several other categories were underrepresented, limiting the generalizability of findings related to pigmentation. Additionally, the method used to assess pigmentation had a greater depth of penetration than the NIRS sensor and may have captured characteristics beyond superficial skin pigmentation. For analyses examining the effects of pigmentation, rSO₂ values from all anatomical landmarks were used; however, pigmentation data were collected solely from the forehead. This is problematic, as pigmentation can vary across body regions and may not accurately reflect the characteristics of other landmarks. In the measurement of rSO₂ at the thenar region, achieving consistent sensor placement proved challenging. The curvature of the thenar, the large size of the sensor, and the lack of a strong anatomical anchor for applying uniform pressure all contributed to high variability in the measurements from this site. Furthermore, the baseline rSO₂ reference ranges reported in this study were determined solely by anatomical landmarks and did not account for the effects of sex or age, despite these factors showing significant associations with rSO₂. Moreover, significant involuntary movements were observed at this site, introducing motion artifacts that compromised the accuracy of the readings. Future Studies Future research should focus on establishing individualized reference ranges that incorporate multiple participant characteristics, including age, sex, and skin pigmentation, to improve the clinical utility and accuracy of NIRS-based monitoring. Moreover, using a colour sensor that can measure skin pigmentation with a lower depth of penetration is recommended to minimize artifacts from deeper tissue layers. CONCLUSION This study provides a comprehensive characterization of normative regional tissue rSO₂ values across 17 anatomical landmarks in healthy adults using continuous-wave NIRS. Among all evaluated sites, the quadriceps muscle exhibited the lowest inter-individual variability, establishing it as the most reliable control landmark for resting-state NIRS measurements. The sternum also demonstrated low variability, supporting its use in dynamic or exercise-based applications particularly when lower limb muscles may be physiologically altered. Across all anatomical regions and participants, rSO₂ values ranged from 50.5–86.0%, with the majority of values clustered between 65% and 76%, providing a practical reference range for healthy tissue oxygenation. Multivariate regression analysis further revealed that sex and age were significant predictors of rSO₂, with males exhibiting higher values than females, and older individuals showing a decline in tissue oxygenation. In contrast, mean arterial pressure, subcutaneous tissue thickness, and skin pigmentation showed minimal or inconsistent associations. Together, these findings offer a foundational reference for interpreting rSO₂ measurements in both clinical and research settings, improving the ability to detect early signs of tissue hypoxia or perfusion compromise. Declarations Ethics Approval The University of British Columbia Research Ethics Board approved the following research protocol. Competing interests The authors declare no competing interests. Author Contributions Statement AR, BS, and MN contributed to the study design. Data collection was conducted by AR, VL, KJ, PA, and MN. AR, BS, LB, JB, and PA were primarily involved in data analysis. The initial manuscript draft was written by AR, PA, VL, and KJ. BS, JB, and LB contributed to reviewing and proofreading the final manuscript. All authors read and approved the final version. Funding Declaration This study was supported by a Project Grant from the Canadian Institute of Health Research (CIHR) and Surrey Memorial Hospitals Foundation. Amir Rad holds a CIHR Award from the Canadian Training Platform for Trials Leveraging Existing Networks (CAP TAP TALENT) and a Master’s scholarship Award from Michael Smith Health Research BC. Author Contribution Author Contributions StatementAR, BS, and MN contributed to the study design. Data collection was conducted by AR, VL, KJ, PA, and MN. AR, BS, LB, JB, and PA were primarily involved in data analysis. The initial manuscript draft was written by AR, PA, VL, and KJ. BS, JB, and LB contributed to reviewing and proofreading the final manuscript. All authors read and approved the final version. Data Availability The datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request. References Bokobza, L. Near Infrared Spectroscopy. J. Infrared Spectrosc. 6 (1), 3–17 (1998). Murkin, J. M. & Arango, M. Near-infrared spectroscopy as an index of brain and tissue oxygenation. Br. J. Anaesth. 103 , i3–13 (2009). Simonson, S. G. & Piantadosi, C. A. NEAR-INFRARED SPECTROSCOPY. Crit. Care Clin. 12 (4), 1019–1029 (1996). Scheeren, T. W. L., Schober, P. & Schwarte, L. A. Monitoring tissue oxygenation by near infrared spectroscopy (NIRS): background and current applications. J. Clin. Monit. Comput. 26 (4), 279–287 (2012). Amendola, C. et al. Robustness of tissue oxygenation estimates by continuous wave space-resolved near infrared spectroscopy. J. Biomed. Opt. 28 (7), 075002 (2023). Weigl, W. et al. Application of optical methods in the monitoring of traumatic brain injury: A review. J. Cereb. Blood Flow. Metab. 36 (11), 1825–1843 (2016). Davie, S. & Grocott, P. H. Impact of Extracranial Contamination on Regional Cerebral Oxygen Saturation A Comparison of Three Cerebral Oximetty Technologies. ResearchGate [Internet]. [cited 2025 Jun 25]; Available from: https://www.researchgate.net/publication/221841019_Impact_of_Extracranial_Contamination_on_Regional_Cerebral_Oxygen_Saturation_A_Comparison_of_Three_Cerebral_Oximetty_Technologies Mancini, D. M. et al. Validation of near-infrared spectroscopy in humans. J. Appl. Physiol. 77 (6), 2740–2747 (1994). Mccully, K. & Hamaoka, K. T. Near-infrared spectroscopy: What can it tell us about oxygen saturation in skeletal muscle? ResearchGate [Internet]. [cited 2025 Jun 25]; Available from: https://www.researchgate.net/publication/12402619_Near-infrared_spectroscopy_What_can_it_tell_us_about_oxygen_saturation_in_skeletal_muscle van Bel, F., Lemmers, P. & Naulaers, G. Monitoring neonatal regional cerebral oxygen saturation in clinical practice: value and pitfalls. Neonatology 94 (4), 237–244 (2008). Verhagen, E. A. et al. Cerebral oxygenation in preterm infants with germinal matrix-intraventricular hemorrhages. Stroke 41 (12), 2901–2907 (2010). Murkin, J. M. et al. Monitoring brain oxygen saturation during coronary bypass surgery: a randomized, prospective study. Anesth. Analg . 104 (1), 51–58 (2007). Kishi, K., Kawaguchi, M., Yoshitani, K., Nagahata, T. & Furuya, H. Influence of Patient Variables and Sensor Location on Regional Cerebral Oxygen Saturation Measured by INVOS 4100 Near-Infrared Spectrophotometers. J. Neurosurg. Anesthesiol . 15 (4), 302–306 (2003). Näslund, E. et al. Measuring arterial oxygen saturation from an intraosseous photoplethysmographic signal derived from the sternum. J. Clin. Monit. Comput. 34 (1), 55–62 (2020). Lipcsey, M., Woinarski, N. C. & Bellomo, R. Near infrared spectroscopy (NIRS) of the thenar eminence in anesthesia and intensive care. Ann. Intensive Care . 2 (1), 11 (2012). Lubkowska, A., Radecka, A., Pluta, W. & Wieleba, K. Reference Values of Regional Oxygen Saturation (rSO2) Determined by Near-Infrared Spectroscopy (NIRS) for 18 Selected Regions of Interest (ROIs) in Young and Elderly Healthy Volunteers. Appl. Sci. 14 (3), 1307 (2024). Sendra-Pérez, C., Priego-Quesada, J. I., Salvador-Palmer, R., Murias, J. M. & Encarnacion-Martinez, A. Sex-related differences in profiles of muscle oxygen saturation of different muscles in trained cyclists during graded cycling exercise. J. Appl. Physiol. Bethesda Md. 1985 . 135 (5), 1092–1101 (2023). Gu, G. et al. Regional cerebral oxygen saturation in the healthy population of western Sichuan: a multicenter cross-sectional study. J. Clin. Monit. Comput. 39 (2), 283–289 (2025). Leone, M., Asfar, P., Radermacher, P., Vincent, J. L. & Martin, C. Optimizing mean arterial pressure in septic shock: a critical reappraisal of the literature. Crit. Care Lond. Engl. 19 (1), 101 (2015). Georger, J. F. et al. Restoring arterial pressure with norepinephrine improves muscle tissue oxygenation assessed by near-infrared spectroscopy in severely hypotensive septic patients. Intensive Care Med. 36 (11), 1882–1889 (2010). Sun, X. et al. Skin pigmentation interferes with the clinical measurement of regional cerebral oxygen saturation. Br. J. Anaesth. 114 (2), 276–280 (2015). Kalogeris, T., Baines, C. P., Krenz, M. & Korthuis, R. J. Ischemia/Reperfusion. In: Comprehensive Physiology [Internet]. John Wiley & Sons, Ltd; [cited 2025 Jun 25]. pp. 113–70. Available from: https://onlinelibrary.wiley.com/doi/abs/ (2016). 10.1002/cphy.c160006 Paredes-Ruiz, M. J. & Jodar-Reverte, M. Effects of Gender on Oxygen Saturation of Thigh Muscles during Maximal Treadmill Exercise Testing [Internet]. [cited 2025 Jun 25]. Available from: https://journals.indexcopernicus.com/search/article?articleId=2786727 Yang, Y., Soyemi, O. O., Landry, M. R. & Soller, B. R. Influence of a fat layer on the near infrared spectra of human muscle: quantitative analysis based on two-layered Monte Carlo simulations and phantom experiments. Opt. Express . 13 (5), 1570–1579 (2005). Data Set Availability. The datasets generated. during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 11 Aug, 2025 Reviews received at journal 10 Aug, 2025 Reviews received at journal 09 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers invited by journal 30 Jul, 2025 Editor assigned by journal 29 Jul, 2025 Editor invited by journal 21 Jul, 2025 Submission checks completed at journal 17 Jul, 2025 First submitted to journal 17 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7070224","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":494526328,"identity":"104c483c-efdd-46c8-af19-a0bd2ad070b7","order_by":0,"name":"Amir Parham Pirhadi Rad","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"Parham Pirhadi","lastName":"Rad","suffix":""},{"id":494526330,"identity":"90d1d8d0-d940-4f59-a5d9-48ff97559c3a","order_by":1,"name":"Parsa Alizadeh","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Parsa","middleName":"","lastName":"Alizadeh","suffix":""},{"id":494526333,"identity":"f4432f9e-8117-4f8a-9e3e-d344793e38b8","order_by":2,"name":"Mehdi Nourizadeh","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Nourizadeh","suffix":""},{"id":494526335,"identity":"50c06822-2067-4308-9ee4-8f6b380aca19","order_by":3,"name":"Kiana Jahanshahi","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Kiana","middleName":"","lastName":"Jahanshahi","suffix":""},{"id":494526337,"identity":"e1cd7ae6-b972-4f5f-8715-91e91feee32d","order_by":4,"name":"Jocelyn Bégin","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Jocelyn","middleName":"","lastName":"Bégin","suffix":""},{"id":494526339,"identity":"77ceb637-120a-4203-9ecf-d73beffbb31d","order_by":5,"name":"Leila baktash","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Leila","middleName":"","lastName":"baktash","suffix":""},{"id":494526344,"identity":"02c9b3c7-b98e-4d6d-856e-58d78fab9b31","order_by":6,"name":"Vincent Levandier","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Vincent","middleName":"","lastName":"Levandier","suffix":""},{"id":494526348,"identity":"e9479050-223b-4701-b801-0d5d2ace761e","order_by":7,"name":"Babak Shadgan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACAwYeBsYGBoYEMGKoAJMkaTlDshbGNiK0mLOfPfhxBkNdHn97+sXPhfMO5/E3MD/8gE+LZU9esuQGhsPFEmfeFEvP3AZkHGAzlsDrsAM5BpIPGA4kNtzISZDm3XY4cQPQqfi1nH9j/PMBQ13i/Bs5yb9554C1MP/Aq+VGjhnQYcyJG26kH5PmbQBrYcNri+WMd2mWMwwOJ24884bNmudYerHEYTYzC3xazPlzD9/sqahLnHc8/fFtnhprYNA1P76BTwvUeSCCxwDCYSasHgbYHxCvdhSMglEwCkYUAADx81Ac09Dk+wAAAABJRU5ErkJggg==","orcid":"","institution":"University of British Columbia","correspondingAuthor":true,"prefix":"","firstName":"Babak","middleName":"","lastName":"Shadgan","suffix":""}],"badges":[],"createdAt":"2025-07-08 03:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7070224/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7070224/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-23102-y","type":"published","date":"2025-10-27T15:58:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88231649,"identity":"c788d96f-8938-4cd5-bf36-85711c83a142","added_by":"auto","created_at":"2025-08-04 09:36:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129388,"visible":true,"origin":"","legend":"\u003cp\u003eThe 17 locations on the body where rSO\u003csub\u003e2\u003c/sub\u003e was measured.\u003c/p\u003e","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7070224/v1/750e9b64cc08afede520fad8.jpg"},{"id":88231622,"identity":"8dd722d4-034a-468c-bbf5-b0d2a304ea6c","added_by":"auto","created_at":"2025-08-04 09:36:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91455,"visible":true,"origin":"","legend":"\u003cp\u003eSensor placement on the anatomical landmarks of the face\u003c/p\u003e","description":"","filename":"image2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7070224/v1/9afd9f9745284ed6b675ab80.jpg"},{"id":88232916,"identity":"fdd6e519-b7c6-4a08-bfcc-2ce8ea065d23","added_by":"auto","created_at":"2025-08-04 09:44:12","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":166160,"visible":true,"origin":"","legend":"\u003cp\u003eData collection sequence – measurements start 5 minutes after participants have been in the supine position.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7070224/v1/20553508dd9bb0f926e2a851.jpeg"},{"id":88231624,"identity":"876a7941-98de-4c66-b30f-dddcca5919ae","added_by":"auto","created_at":"2025-08-04 09:36:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":39949,"visible":true,"origin":"","legend":"\u003cp\u003eReproducibility test results for the NIRS measurements on different time-points\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7070224/v1/5b70771f164380dbb9eaee5c.png"},{"id":88232920,"identity":"8107a328-66ed-4efc-b2b3-b827f24bf0cf","added_by":"auto","created_at":"2025-08-04 09:44:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":118651,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot of tissue oxygenation across anatomical landmarks in healthy individuals.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7070224/v1/1c03a989083a2c544aa9957a.png"},{"id":88232915,"identity":"4d161db0-b0d1-4754-ae74-fe87ab08b09c","added_by":"auto","created_at":"2025-08-04 09:44:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":75106,"visible":true,"origin":"","legend":"\u003cp\u003e95% confidence intervals for the estimates of rSO₂ across anatomical landmarks. Dashed line represents the mean rSO₂ value collected at the bilateral quadricep (reference landmark).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7070224/v1/49e0b9ebfaf6d5ef33c721c1.png"},{"id":88231650,"identity":"f8cf6ff7-c24a-4531-b793-16ee77ae5a18","added_by":"auto","created_at":"2025-08-04 09:36:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":134713,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of rSO2 between Male and Female Participants across different Landmarks\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7070224/v1/fd3aceb1f36680fbd9d0c1fe.png"},{"id":88232925,"identity":"a33347c9-7387-413b-b8be-f15fa1836c6d","added_by":"auto","created_at":"2025-08-04 09:44:14","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":260289,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of rSO\u003csub\u003e2\u003c/sub\u003e levels in participants of different skin pigmentation categories across different landmarks.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7070224/v1/5f80bc5ae2b4a60cb9a1cdd9.png"},{"id":88231638,"identity":"b14f624e-1c20-48b1-8ef4-723143c01c0b","added_by":"auto","created_at":"2025-08-04 09:36:14","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":244580,"visible":true,"origin":"","legend":"\u003cp\u003e1. rSO\u003csub\u003e2\u003c/sub\u003e distribution. 2. Age distribution 3. Distribution of Mean arterial pressure 4. Distribution of tissue thickness\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7070224/v1/224dd165afc3d5c7f3067580.jpeg"},{"id":95041044,"identity":"17e7f1de-d4d3-47a6-99a8-274b28e0dfb6","added_by":"auto","created_at":"2025-11-03 16:10:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1895454,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7070224/v1/b0f61469-52e5-4de2-b901-99f8b81199d3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative Analysis of Tissue Oxygenation Variability across Anatomical Landmarks in Healthy Individuals via Near-Infrared Spectroscopy","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eNear-infrared spectroscopy (NIRS) is an optical technique that enables continuous and noninvasive monitoring of changes in tissue oxygenation and hemodynamics by utilizing light in the near-infrared spectrum (650\u0026ndash;1000 nm). NIRS can penetrate several millimetres into biological tissue depending on the sensor size and placement, tissue structure, and skin pigmentation.\u003c/p\u003e\n\u003cp\u003eIn recent years, there has been a growing interest within the medical device field regarding non-invasive diagnostic and monitoring methodologies. Notably, NIRS has gained prominence due to its ability to monitor tissue and organ hemodynamics and function in healthy states and disease conditions.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWhile NIRS is a relatively new technology in medicine, it is commonly used to monitor cerebral oxygenation during and after surgical procedures.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e NIRS quantifies changes in the concentration of oxygenated hemoglobin (O₂Hb) and deoxygenated hemoglobin (HHb) within the tissue. These chromophores exhibit distinct absorption spectra, enabling NIRS sensors to determine their concentrations in tissue by analyzing near-infrared light absorbance at specific wavelengths.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e NIRS devices are categorized into three main types: continuous wave (CW), frequency domain (FD), and time domain (TD), with CW-NIRS being the most widely used due to its compact design and cost-effectiveness.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Unlike FD and TD systems, CW-NIRS can only measure relative changes in O₂Hb and HHb concentrations. However, these values can be used to compute regional tissue oxygen saturation (rSO₂), which provides an estimate of local tissue oxygenation.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e This is the primary NIRS metric used in clinical applications. By analyzing the spatial gradient of light attenuation across multiple source-detector distances, CW-NIRS systems in spatially resolved (SR-NIRS) configuration provide more accurate estimation of rSO₂.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Light is emitted at near-infrared wavelengths and detected at several distances from the source, enabling calculation of the slope of optical density (OD) with respect to distance (\u0026part;OD/\u0026part;\u0026rho;). This slope is largely influenced by the tissue absorption coefficient (\u0026micro;ₐ) and minimally affected by scattering, thus enhancing measurement accuracy. By acquiring data at two or more wavelengths, typically around 760 nm and 850 nm, where deoxygenated and oxygenated hemoglobin absorb differently, the concentrations of O\u003csub\u003e2\u003c/sub\u003eHb and HHb are derived through the solution of simultaneous equations based on the modified Beer\u0026ndash;Lambert Law and diffusion theory. Finally, rSO₂ is computed as the ratio of O\u003csub\u003e2\u003c/sub\u003eHb to the total hemoglobin concentration (THb\u0026thinsp;=\u0026thinsp;O\u003csub\u003e2\u003c/sub\u003eHb\u0026thinsp;+\u0026thinsp;HHb) expressed as a percentage (Eq.\u0026nbsp;1). This approach allows for more accurate, depth-sensitive, and scattering-independent estimation of tissue oxygenation compared to single-distance NIRS methods.\u003c/p\u003e\n\u003cp\u003erSO\u003csub\u003e2\u003c/sub\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{O2Hb}{(O2Hb+HHB)}*100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEquation 1:\u0026nbsp;\u003c/strong\u003erSO2 Calculation\u003c/p\u003e\n\u003cp\u003eMeasuring baseline rSO₂ and monitoring its dynamic changes are critical components of clinical decision-making in a variety of settings, including surgery, critical care, tissue monitoring and rehabilitation. A baseline measurement provides a patient-specific reference that reflects individual differences in tissue oxygenation and perfusion. Monitoring deviations from this baseline, rather than relying solely on absolute thresholds, can offer early insight into physiological compromise. For instance, during monitoring a surgical flap hemodynamics, a decline in tissue rSO₂ may indicate impaired oxygen delivery to the reconstructed flap even when systemic parameters such as arterial oxygen saturation or blood pressure remain within normal limits. In other contexts, such as recovery from vascular compromise, changes in regional tissue rSO₂ can help assess the adequacy of perfusion and guide therapeutic decisions. This approach allows clinicians to respond proactively to evolving physiological changes, supporting more tailored and timely interventions. Therefore, insights on normal ranges of tissue rSO\u003csub\u003e2\u003c/sub\u003e in different organs is essential to apply NIRS in clinical settings.\u003c/p\u003e\n\u003cp\u003eNIRS-derived rSO₂ measurements are influenced by several physiological and technical factors, including the assumed arterial-to-venous blood ratio (commonly approximated as 25:75), sensor placement, local tissue composition and thickness, and the configuration and type of NIRS device used.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e In cerebral applications, particularly within the frontal cortex, normal rSO₂ values typically range from 55\u0026ndash;75%, reflecting the venous-weighted nature of the NIRS signal under resting conditions.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e In contrast, skeletal muscle rSO₂ values are generally higher, ranging from 60\u0026ndash;85%, although they can vary substantially in response to changes in perfusion, vascular tone, and physical activity.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e In neonatal and critical care populations, renal and splanchnic rSO₂ values are also reported within a similar range of 60\u0026ndash;85%.\u003csup\u003e10,11\u003c/sup\u003e Clinically, a cerebral rSO₂ value below 50% or a reduction greater than 20% from baseline is often regarded as significant, potentially indicating cerebral hypoxia or ischemia.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e The clinical utility of NIRS in monitoring regional tissue and organ oxygenation relies on a thorough understanding of normative rSO₂ values across different tissues and physiological contexts.\u003c/p\u003e\n\u003cp\u003eThe primary aim of this study was to characterize the normative ranges of tissue oxygenation across multiple anatomical regions of healthy individuals using NIRS. Additionally, we aimed to identify which anatomical site yields the most consistent and reliable rSO2 measurements under baseline physiological conditions. This information helps clinicians and researchers to recognize deviations indicative of regional tissue hypoxia or ischemia, thereby supporting timely and targeted clinical interventions.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cohort of healthy adults was recruited. The inclusion criteria were being between 19 and 70 years old, having good general health, and having no known neurological disorders or musculoskeletal impairments. Participants were excluded from the study if they had a severe cardiovascular, respiratory, or neurological condition or if they had a severe skin condition at any of the sensor placement sites. Furthermore, participants were excluded if they used medications that affect vascular tone or blood flow; if they had known allergies to materials used in the NIRS sensors or had previous adverse reactions to skin-based measurements. Participants were excluded if they were pregnant or had severe cognitive impairments or communication barriers that could have hindered their ability to provide informed consent. All participants were informed about the experimental procedure, and informed consent was obtained from all participants prior to the experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstrumentation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used an FDA-approved clinical NIRS device (Masimo Root\u0026reg; with O3\u0026reg; Regional Oximetry, Masimo Corporation, Irvine, USA). An adult (\u0026gt;\u0026thinsp;40kg) O3 rSO\u003csub\u003e2\u003c/sub\u003e NIRS sensor (source-detector distance of 35mm) was used to measure rSO\u003csub\u003e2\u003c/sub\u003e at each selected anatomical landmark. Based on accessibility and anatomical variations, the sensor was fixed in place by hand or with Velcro straps, continuously collecting data for 30 seconds, depending on the landmark. The sensor setting was adjusted for selected anatomical landmarks based on available categories in the device setting, including forehead, forearm, chest, flank, upper leg, and calf. Measurements were collected with a sampling rate of 0.5 Hz. A standard pulse oximeter (LNCS DCI\u0026reg; Adult Digit Sensor, Masimo Corporation, Irvine, USA) was placed on the left index finger to monitor heart rate and arterial oxygen saturation (SpO\u003csub\u003e2\u003c/sub\u003e). Subcutaneous tissue thickness was measured using a skinfold calliper (Skyndex LLC, Albuquerque, USA) at each site. The blood pressure was also measured using a digital blood pressure monitor (Omron Healthcare, model: BP769CAN). Skin pigmentation was measured using a Nix Mini 3 Colour Sensor (Nix Sensor Ltd, Canada) at each measurement landmark.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental Protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSensor Placement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe University of British Columbia Research Ethics Board approved the following research protocol (H24-00171), and the research was conducted in accordance with the guidelines outlined in Policy Number LR9 (Research Involving Human Participants), provided by the University of British Columbia. For each participant, rSO\u003csub\u003e2\u003c/sub\u003e was recorded at 17 anatomical landmarks, including eight bilateral sites: the forehead region, the temporal region, the infraorbital rim region, the temporomandibular joint (TMJ) region, the mandibular ramus region, the thenar eminence, the quadriceps muscle (vastus lateralis) region, the tibia region and the sternum (Fig.\u0026nbsp;1). The locations were chosen to ensure that several body regions were accounted for with unique anatomical and physiological characteristics.\u003c/p\u003e\n\u003cp\u003eTo secure the sensors on the lower limb, two Velcro straps were used to affix them at standardized anatomical landmarks: one on the thigh, positioned approximately one-third of the distance from the lateral condyle of the knee to the greater trochanter, and the other on the tibia, midway between the lateral malleolus and the head of the fibula. Bilateral measurements were acquired simultaneously using two NIRS sensors. To evaluate measurement accuracy and repeatability, data collection was repeated three times in fifteen participants, across three separate sessions on different dates and times, yielding a total of 45 individual data sets. This information has been used to confirm the reproducibility of the measurement through the NIRS sensor for each participant.\u003c/p\u003e\n\u003cp\u003eIn the head and neck regions, sensors were placed along the length of the tissue segment of interest, as shown in Fig.\u0026nbsp;2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to data collection, participants were instructed to stand barefoot on the digital scale to capture their body composition data. At the anatomical measurement sites, the area was wiped clean to remove any skin or oil residues, thereby reducing interference with the light. Participants were asked to lie down in a supine position with their eyes open for 5 minutes, maintaining stillness, without any posture adjustments or talking during the data collection period. This step is crucial to ensure that stable blood pressure and tissue oxygenation levels are maintained at a stable value. Blood pressure was measured using an Omron blood pressure monitor. Data collection continued if the participants' blood pressure remained within the acceptable range of 120/80 mmHg\u0026thinsp;\u0026plusmn;\u0026thinsp;20 mmHg (Fig.\u0026nbsp;3).\u003c/p\u003e\n\u003cp\u003eSkin pigmentation was measured using the Nix Mini 3 Colour Sensor, with the measurements reported as HEX colour codes for each landmark. The HEX colour codes were calibrated using the Monk Skin Tone Scale, ranging from 1 (lightest) to 10 (darkest), to ensure consistent and standardized measurement of skin tones.\u003csup\u003e13\u003c/sup\u003e Subcutaneous tissue thickness was measured at each data collection site using a Skyndex calliper. Each measurement was repeated three times per site to minimize measurement error, and the average of the measurements was reported. The average subcutaneous tissue thickness measurement was calculated by dividing the measurement by two, following the instructions of the calliper. The pulse oximeter was placed on the participant's left index finger to continuously record heart rate and SpO\u003csub\u003e2\u003c/sub\u003e. Although these measurements were not used to calculate rSO\u003csub\u003e2\u003c/sub\u003e, monitoring them throughout data collection was essential. This ensured measurement consistency; in the event of sudden changes in heart rate or SpO\u003csub\u003e2\u003c/sub\u003e, the data collection was repeated on the same or a later day.\u003c/p\u003e\n\u003cp\u003eTo collect NIRS data, the Masimo NIRS sensors were positioned at each predefined anatomical location and held in place for approximately 30 seconds to ensure reliable readings. To standardize sensor placement and minimize errors across participants, all measurement sites were pre-marked, and measurements were taken bilaterally, except for the sternum.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTroubleshooting\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThrough working with NIRS sensors, we identified several recurring indicators of sensor malfunction, which were resolved through a systematic troubleshooting protocol:\u003c/p\u003e\n\u003cp\u003e1. Asymmetrical readings (\u0026gt;\u0026thinsp;5-unit difference between left and right sides):\u003c/p\u003e\n\u003cp\u003e1.1. Re-adjust sensor placement, clean the sensor surface, and clean the skin area.\u003c/p\u003e\n\u003cp\u003e1.2. If the discrepancy persists, replace the sensor hardware.\u003c/p\u003e\n\u003cp\u003e2. Sensor displays \u0026ldquo;off patient\u0026rdquo; despite proper placement:\u003c/p\u003e\n\u003cp\u003e2.1. Adjust sensor placement, clean both the sensor and the skin, and reset the system.\u003c/p\u003e\n\u003cp\u003e2.2. If unresolved, replace the sensor hardware.\u003c/p\u003e\n\u003cp\u003e3. Prolonged \u0026ldquo;initializing values\u0026rdquo; without data output:\u003c/p\u003e\n\u003cp\u003e3.1. Re-adjust the sensor, clean the sensor surface and skin area, and perform a system reset.\u003c/p\u003e\n\u003cp\u003e3.2. Replace the sensor if the issue continues.\u003c/p\u003e\n\u003cp\u003eThese errors are often attributable to improper sensor positioning. If such issues arise, the sensor should be removed, the site inspected, and the device repositioned to ensure optimal contact. Equal pressure across bilateral sensors is essential, as unequal pressure can result in data discrepancies. Contaminants such as skin oils or residual debris can interfere with sensor performance. Participants were advised to clean the application site thoroughly with skin wipes. Additionally, sensors were disinfected between uses in different locations to remove any transferred oils or particles before reattachment. Excessive body or facial hair can impede sensor function by obstructing optical contact with the skin. While shaving the area is ideal, it may not always be feasible or acceptable to participants. In such cases, repositioning the sensor slightly to a less hair-dense area improved signal acquisition.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003erSO\u003csub\u003e2\u003c/sub\u003e measurements were obtained from 17 landmark locations (8 bilateral\u0026thinsp;+\u0026thinsp;sternum) on 78 participants (43 male and 35 female). Participants ranged in age from 17 to 45, with an average age of 31 (\u0026plusmn;\u0026thinsp;13.4 SD).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive statistics for all anatomical sites, arranged by increasing standard deviation and a visual summary of rSO₂ values obtained from different landmarks is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe reproducibility test results for the NIRS measurements indicate that there are no significant differences in rSO2 readings across different sessions (Time-point 1, Time-point 2, and Time-point 3 are relatively consistent) at the same landmark (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDescriptive statistics were used to evaluate the reliability of various anatomical landmarks for NIRS sensor placement by assessing the variability in rSO₂ measurements. Inter-participant variability was inferred from the standard deviation (SD), with lower SD values indicating higher reliability. Among the evaluated landmarks, the quadriceps (2.72 SD) and sternum (2.96 SD) demonstrated the highest reliability, suggesting they are the least variable sites for NIRS placement. Conversely, the thenar exhibited the greatest variability (6.31 SD), indicating it is the least reliable landmark in this context.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of rSO\u003csub\u003e2\u003c/sub\u003e across Anatomical Landmarks. Ordered from smallest to largest standard deviation. Utility: To determine baseline rSO\u003csub\u003e2\u003c/sub\u003e ranges at different landmarks in healthy participants.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLandmarks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStd. Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVariance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eShapiro-Wilk p\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral Quadriceps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e73.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSternum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e74.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTMJ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral Ramus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral Orbital Rim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e74.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral Tibia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e74.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e21.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral Forehead\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e21.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral Thenar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e39.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eEstimated differences between eight anatomical landmarks and the bilateral quadriceps, displayed (blue markers) with their corresponding 95% confidence intervals (red horizontal bars). All intervals lie wholly to one side of this line, indicating statistically significant deviations at every site. The bilateral thenar (estimate \u0026asymp; \u0026minus;\u0026thinsp;7, 95% CI \u0026asymp; \u0026minus;\u0026thinsp;10 to \u0026minus;\u0026thinsp;6) and bilateral forehead (estimate \u0026asymp; \u0026minus;\u0026thinsp;5% CI \u0026asymp; \u0026minus;\u0026thinsp;7.5 to \u0026minus;\u0026thinsp;4) show pronounced negative differences relative to the quadriceps, whereas the remaining cranio-facial, axial, and lower-limb landmarks exhibit modest positive differences (estimates\u0026thinsp;\u0026asymp;\u0026thinsp;+\u0026thinsp;1.5 to +\u0026thinsp;2.5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA multiple linear regression analysis was conducted to examine the associations between tissue rSO₂ and its potential predictors, including sex, age, mean arterial pressure, anatomical landmark, skin pigmentation, and tissue thickness. The regression model accounted for a substantial portion of the variability in tissue oxygenation, with an adjusted R\u0026sup2; of 55.7%. Multicollinearity among the explanatory variables was assessed using the Variance Inflation Factor (VIF), with all values falling below 5.0 (VIF\u0026thinsp;\u0026lt;\u0026thinsp;5.0), indicating no evidence of multicollinearity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePredictors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStand. Estimate (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e95% Confidence Interval (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEffect size (r)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e95% Confidence Interval (r)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eLower\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eUpper\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eLower\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eUpper\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-8.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[-0.370, -0.243]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean Arterial Pressure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[-0.067, 0.081]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender (M \u0026ndash; F)\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[0.310, 0.428]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTissue Pigmentation\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[-0.121, 0.026]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[-0.110, 0.037]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[-0.105, 0.042]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[-0.090, 0.058]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[-0.117, 0.030]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[-0.203, -0.059]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLandmarks\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral Forehead \u0026ndash; Bilateral Quadriceps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[-0.305, -0.169]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporal \u0026ndash; Bilateral Quadriceps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[0.005, 0.151]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral Orbital Rim \u0026ndash;Bilateral Quadriceps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[0.053, 0.197]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTMJ \u0026ndash; Bilateral Quadriceps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[0.066, 0.209]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral Ramus \u0026ndash; Bilateral Quadriceps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[0.064, 0.207]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral Thenar \u0026ndash; Bilateral Quadriceps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-8.535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[-0.368, -0.241]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral Tibia \u0026ndash; Bilateral Quadriceps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[0.003, 0.149]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSternum \u0026ndash; Bilateral Quadriceps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[-0.002, 0.144]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTissue Thickness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-4.30e\u0026thinsp;\u0026minus;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e[0.000, 0.146]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eDependent variable: SRO2(Mean)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;2: Regression analysis of predictors associated with rSO\u003csub\u003e2\u003c/sub\u003e among healthy participants.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUsing the quadricep as the reference landmark, selected for its high reliability, rSO₂ levels showed statistically significant associations with all other landmarks (Table\u0026nbsp;2), except for the sternum (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.057), as visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Most landmarks exhibited a R\u0026thinsp;=\u0026thinsp;7.2\u0026ndash;13.9% increase in rSO₂ compared to the quadricep. In contrast, the forehead and thenar landmarks showed decreases in rSO₂, with reductions of R\u0026thinsp;=\u0026thinsp;23.9% and 30.7%, respectively.\u003c/p\u003e\u003cp\u003eAccording to the standardized regression coefficients from the multiple regression model (Table\u0026nbsp;2), tissue rSO₂ was significantly associated with participants' gender and age. Male participants exhibited R\u0026thinsp;=\u0026thinsp;37.1% higher rSO₂ levels compared to female participants (β\u0026thinsp;=\u0026thinsp;0.290***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.371, 95% CI [0.310, 0.428]) (Table\u0026nbsp;2). In contrast, older participants showed a R\u0026thinsp;=\u0026thinsp;30.9% reduction in rSO₂ levels relative to younger participants (β = \u0026minus;0.229***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001,\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.309, 95% CI [\u0026minus;\u0026thinsp;0.370, \u0026minus;\u0026thinsp;0.243]) (Table\u0026nbsp;2). This association indicated a negative relationship between age and rSO₂.\u003c/p\u003e\u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, male participants appeared to exhibit higher rSO₂% levels across all anatomical landmarks. However, due to a violation of the homogeneity of variance assumption, statistical comparisons across all landmarks could not be performed to confirm this trend. As a result, this observation will not be analyzed in depth in the current discussion. Future studies should aim to formally investigate sex-related differences in rSO₂ across anatomical landmarks using methods robust to unequal variances.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNo significant association was found between rSO₂ and mean arterial pressure (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.586). A marginal, non-significant association was observed between tissue thickness and rSO₂, with participants having thicker tissue exhibiting R\u0026thinsp;=\u0026thinsp;7.4% higher rSO₂ levels (β\u0026thinsp;=\u0026thinsp;0.279***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.051, 95% CI [0.310, 0.428]) (Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eThe skin pigmentation category was not significantly associated with rSO₂, except for category 7 compared to category 2. Participants in pigmentation category 7 demonstrated significantly lower rSO₂ levels (β = \u0026minus;0.243***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.132, 95% CI [\u0026minus;\u0026thinsp;0.203, \u0026minus;\u0026thinsp;0.059]) (Table\u0026nbsp;2). All pigmentation categories were compared against category 2, as category 1 had only two participants and was therefore deemed an unreliable reference group (see study limitations).\u003c/p\u003e\u003cp\u003eSimilar trends in the effects of skin pigmentation on rSO₂ across all anatomical landmarks are visible in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. However, due to a violation of the homogeneity of variance assumption, further statistical analysis could not be performed to validate these observations. As discussed later, future research is needed to better understand how skin pigmentation influences NIR light transmission, with a potential sub-focus on landmark-specific effects.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe assumption of multivariate normality was evaluated both statistically and graphically. The Shapiro\u0026ndash;Wilk test yielded a non-significant result (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that the residuals were normally distributed. Visual inspection further supported this finding, as the residuals were symmetrically distributed around the mean and closely followed the regression line (see Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), consistent with the assumption of normality.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eNormative tissue rSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eThis study aimed to establish normative regional tissue rSO₂% values across 17 anatomical landmarks in healthy adults using NIRS and to identify anatomical regions that offer the most stable and reproducible rSO₂% measurements under resting physiological conditions. A total of 78 participants were assessed, and bilateral rSO₂% values were collected from the head, upper limb, trunk, and lower limb regions. The analysis demonstrated considerable regional variation in mean rSO₂%, with the highest average levels recorded at the temporomandibular joint (75.80% \u0026plusmn; 3.89) and mandibular ramus (75.76% \u0026plusmn; 3.99), and the lowest at the thenar eminence (65.03% \u0026plusmn; 6.31) and forehead (67.99% \u0026plusmn; 4.67). Across all landmarks, the overall range of rSO₂ values in this healthy cohort spanned from 50.5\u0026ndash;86.0%, with most regions falling between 65% and 76%.\u003c/p\u003e\u003cp\u003eReference landmark\u003c/p\u003e\u003cp\u003eThe quadriceps muscle (mean rSO₂ = 73.89% \u0026plusmn; 2.72) exhibited the lowest inter-participant variability, making it the most reliable anatomical site for rSO₂ measurement under resting conditions. The sternum also showed low variability (mean\u0026thinsp;=\u0026thinsp;74.35% \u0026plusmn; 2.96) and may serve as a suitable reference rSO₂ in applications involving physical movement. In contrast, the thenar region had the highest variability, limiting its utility as a reference site. These results offer baseline reference values and support the standardized use of NIRS in both clinical monitoring and research involving regional tissue oxygenation.\u003c/p\u003e\u003cp\u003ePrevious studies have identified the sternum as a highly accurate site for detecting changes in tissue oxygen saturation.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e This reliability has been largely attributed to its high vascularization, supported by multiple blood supplies, which ensures a stable and continuous perfusion.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Additional factors that may contribute to the low variability in NIRS readings at the sternum include the minimal subcutaneous fat in this region and the consistent soft tissue overlying the bone, both of which have been associated with improved NIRS accuracy in prior research.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e The low inter-participant variability observed in our study may similarly be explained by these anatomical and physiological characteristics. The thenar region exhibited the highest standard deviation in tissue oxygen saturation, indicating it is the least reliable landmark for use as a control site. Although the thenar has been widely used in NIRS research as a key site\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e the high variability observed in our data raises important concerns regarding its reliability for rSO₂ measurement. Due to its small surface area, the thenar region is likely more vulnerable to variations in sensor placement, contributing to the observed inconsistency in measurements. This issue is discussed further in the study limitations section.\u003c/p\u003e\u003cp\u003erSO\u003csub\u003e2\u003c/sub\u003e baseline ranges\u003c/p\u003e\u003cp\u003eRegional differences in rSO₂ levels were observed across all anatomical landmarks when compared to the quadriceps (73.89% \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;2.72)\u003c/span\u003e, except for the sternum (74.35% \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;2.96)\u003c/span\u003e. This finding emphasizes the importance of the study\u0026rsquo;s secondary aim: to establish baseline rSO₂% values in healthy individuals across different anatomical regions. These reference values are critical for identifying deviations in pathological tissues and enhancing the diagnostic and monitoring utility of NIRS in clinical settings.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Detailed descriptive statistics on each landmark are available in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Further discussion of the limitations of these baseline values, particularly their failure to account for age and sex differences, is provided below.\u003c/p\u003e\u003cp\u003ePredictor effects on rSO2\u003c/p\u003e\u003cp\u003ePrevious studies have reported various physiological and anatomical factors that influence rSO₂, including sex differences\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, a negative correlation with age\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, a positive correlation with mean arterial pressure (MAP)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, as well as interference from subcutaneous fat and skin pigmentation.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e In our dataset, multiple predictors contributed to the observed variability in rSO₂ measurements. The influence of each individual factor is discussed in detail below.\u003c/p\u003e\u003cp\u003eSex\u003c/p\u003e\u003cp\u003eThe data from this study indicate that male participants exhibited higher average rSO₂ levels than females (Male rSO\u003csub\u003e2\u003c/sub\u003e \u0026ge; +5% compared to female) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). While some studies have reported higher rSO₂ values in men across several muscle groups at rest,\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e the majority of the literature suggests no significant sex differences across most anatomical landmarks,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e with a few studies reporting higher rSO₂ values in women at specific sites.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e In the context of cerebral oxygenation, evidence consistently shows higher rSO₂ levels in female participants.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e These sex-based differences in rSO₂ may be explained by a combination of physiological, hormonal, and metabolic factors, including variations in hemoglobin levels, tissue perfusion, and oxygen utilization.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAge\u003c/p\u003e\u003cp\u003eThe results of this study demonstrated a negative relationship between age and rSO₂ levels, aligning with prior findings using NIRS technology and with previous research showing that gas exchange efficiency and oxyhemoglobin saturation decline with physiological aging.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e These observations underscore the importance of accounting for both age and sex when establishing reference ranges for rSO₂ to ensure greater accuracy and clinical relevance across diverse populations. It is important to note that this study was conducted on 78 participants (43 male and 35 female). Participants ranged in age from 17 to 45, with an average age of 31 (\u0026plusmn;\u0026thinsp;13.4 SD).\u003c/p\u003e\u003cp\u003eMean Arterial Pressure\u003c/p\u003e\u003cp\u003eNo association was found between MAP and rSO₂ in our study, which contrasts with previous findings that reported a positive correlation between the two variables.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e However, those studies induced MAP increases pharmacologically using epinephrine and measured rSO₂ changes before and after drug administration. This suggests that acute elevations in MAP may influence rSO₂, whereas baseline variations in MAP may not have a direct impact on\u003c/p\u003e\u003cp\u003eTissue Thickness\u003c/p\u003e\u003cp\u003eA marginal association was observed between tissue thickness and rSO₂ in our study; however, this finding did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.051). In contrast, previous studies have reported reduced accuracy of rSO₂ measurements due to the interference of subcutaneous fat with near-infrared (NIR) light transmission.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e As a result, these studies recommend accounting for subcutaneous fat levels when interpreting rSO₂ values. Given the variability in subcutaneous tissue across anatomical landmarks, further research is warranted to explore how regional differences in fat thickness may influence rSO₂ measurements.\u003c/p\u003e\u003cp\u003eSkin Pigmentation\u003c/p\u003e\u003cp\u003eNo significant differences in rSO₂ levels were observed across most pigmentation levels, with the exception of a comparison between levels 2 and 7. Previous research has shown that darker skin pigmentation can attenuate NIRS signals, potentially leading to the underestimation of oxygen saturation.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e The lack of significant differences in our findings may be attributed to several limitations. Notably, some pigmentation groups were underrepresented, resulting in limited statistical power. Additionally, pigmentation data were collected from a single location, while NIRS measurements were taken from multiple anatomical landmarks, each potentially exhibiting different skin tones. Further studies with larger, more diverse samples and site-specific pigmentation assessments are needed to fully understand the impact of pigmentation on rSO₂ measurements.\u003c/p\u003e\u003cp\u003eStudy Limitations\u003c/p\u003e\u003cp\u003eOne of the primary limitations of this study was the lack of representativeness across pigmentation categories. The majority of participants fell into category 2, while several other categories were underrepresented, limiting the generalizability of findings related to pigmentation. Additionally, the method used to assess pigmentation had a greater depth of penetration than the NIRS sensor and may have captured characteristics beyond superficial skin pigmentation. For analyses examining the effects of pigmentation, rSO₂ values from all anatomical landmarks were used; however, pigmentation data were collected solely from the forehead. This is problematic, as pigmentation can vary across body regions and may not accurately reflect the characteristics of other landmarks.\u003c/p\u003e\u003cp\u003eIn the measurement of rSO₂ at the thenar region, achieving consistent sensor placement proved challenging. The curvature of the thenar, the large size of the sensor, and the lack of a strong anatomical anchor for applying uniform pressure all contributed to high variability in the measurements from this site. Furthermore, the baseline rSO₂ reference ranges reported in this study were determined solely by anatomical landmarks and did not account for the effects of sex or age, despite these factors showing significant associations with rSO₂. Moreover, significant involuntary movements were observed at this site, introducing motion artifacts that compromised the accuracy of the readings.\u003c/p\u003e\u003cp\u003eFuture Studies\u003c/p\u003e\u003cp\u003eFuture research should focus on establishing individualized reference ranges that incorporate multiple participant characteristics, including age, sex, and skin pigmentation, to improve the clinical utility and accuracy of NIRS-based monitoring. Moreover, using a colour sensor that can measure skin pigmentation with a lower depth of penetration is recommended to minimize artifacts from deeper tissue layers.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study provides a comprehensive characterization of normative regional tissue rSO₂ values across 17 anatomical landmarks in healthy adults using continuous-wave NIRS. Among all evaluated sites, the quadriceps muscle exhibited the lowest inter-individual variability, establishing it as the most reliable control landmark for resting-state NIRS measurements. The sternum also demonstrated low variability, supporting its use in dynamic or exercise-based applications particularly when lower limb muscles may be physiologically altered. Across all anatomical regions and participants, rSO₂ values ranged from 50.5\u0026ndash;86.0%, with the majority of values clustered between 65% and 76%, providing a practical reference range for healthy tissue oxygenation. Multivariate regression analysis further revealed that sex and age were significant predictors of rSO₂, with males exhibiting higher values than females, and older individuals showing a decline in tissue oxygenation. In contrast, mean arterial pressure, subcutaneous tissue thickness, and skin pigmentation showed minimal or inconsistent associations. Together, these findings offer a foundational reference for interpreting rSO₂ measurements in both clinical and research settings, improving the ability to detect early signs of tissue hypoxia or perfusion compromise.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics Approval\u003c/h2\u003e\u003cp\u003eThe University of British Columbia Research Ethics Board approved the following research protocol.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eAuthor Contributions Statement\u003c/h2\u003e\u003cp\u003eAR, BS, and MN contributed to the study design. Data collection was conducted by AR, VL, KJ, PA, and MN. AR, BS, LB, JB, and PA were primarily involved in data analysis. The initial manuscript draft was written by AR, PA, VL, and KJ. BS, JB, and LB contributed to reviewing and proofreading the final manuscript. All authors read and approved the final version.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003cp\u003eThis study was supported by a Project Grant from the Canadian Institute of Health Research (CIHR) and Surrey Memorial Hospitals Foundation. Amir Rad holds a CIHR Award from the Canadian Training Platform for Trials Leveraging Existing Networks (CAP TAP TALENT) and a Master\u0026rsquo;s scholarship Award from Michael Smith Health Research BC.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions StatementAR, BS, and MN contributed to the study design. Data collection was conducted by AR, VL, KJ, PA, and MN. AR, BS, LB, JB, and PA were primarily involved in data analysis. The initial manuscript draft was written by AR, PA, VL, and KJ. BS, JB, and LB contributed to reviewing and proofreading the final manuscript. All authors read and approved the final version.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBokobza, L. Near Infrared Spectroscopy. \u003cem\u003eJ. Infrared Spectrosc.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e (1), 3\u0026ndash;17 (1998).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurkin, J. M. \u0026amp; Arango, M. Near-infrared spectroscopy as an index of brain and tissue oxygenation. \u003cem\u003eBr. J. Anaesth.\u003c/em\u003e \u003cb\u003e103\u003c/b\u003e, i3\u0026ndash;13 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimonson, S. G. \u0026amp; Piantadosi, C. A. 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Express\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e (5), 1570\u0026ndash;1579 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eData Set Availability.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThe datasets generated. during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Near-Infrared Spectroscopy (NIRS)- Tissue Oxygenation- Regional Saturation (rSO₂)","lastPublishedDoi":"10.21203/rs.3.rs-7070224/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7070224/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNear-infrared spectroscopy (NIRS) enables noninvasive assessment of tissue oxygenation, but its broader clinical application is hindered by the absence of standardized reference values across anatomical regions. This study aimed to characterize baseline regional tissue oxygen saturation (rSO₂) across 17 anatomical landmarks in healthy adults and to identify the most consistent and reproducible measurement sites.\u003c/p\u003e\u003cp\u003eSeventy-eight healthy participants (mean age 31\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4 years) underwent rSO₂ assessments using a continuous-wave NIRS system. Demographic and physiological data, including age, sex, skin pigmentation, tissue thickness, and mean arterial pressure, were collected. rSO₂ values ranged from 50.5\u0026ndash;86.0%, with most values between 65% and 76%. The temporomandibular joint and mandibular ramus had the highest mean rSO₂ (~\u0026thinsp;75.8%), while the thenar eminence and forehead showed the lowest. The quadriceps exhibited the lowest inter-individual variability (2.72 SD), making it the most reliable site for baseline measurements. The sternum also showed low variability (2.96 SD), suggesting its usefulness in dynamic monitoring. Age and sex significantly influenced rSO₂ (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while other variables had limited impact.\u003c/p\u003e\u003cp\u003eThese findings establish normative rSO₂ values and identify optimal NIRS placement sites, supporting standardization in clinical and research applications to improve detection of tissue hypoxia and perfusion abnormalities.\u003c/p\u003e","manuscriptTitle":"Quantitative Analysis of Tissue Oxygenation Variability across Anatomical Landmarks in Healthy Individuals via Near-Infrared Spectroscopy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 09:36:07","doi":"10.21203/rs.3.rs-7070224/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-11T07:58:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-10T13:52:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-09T09:12:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17403819068821390112427357039931872576","date":"2025-07-30T10:31:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312641184513779854915233809395824064353","date":"2025-07-30T10:28:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-30T06:50:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T04:39:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-21T05:43:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-17T23:00:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-17T20:22:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9a2143e3-ab18-4275-9cb6-d14a8d672efd","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52524771,"name":"Health sciences/Anatomy"},{"id":52524772,"name":"Health sciences/Health care"},{"id":52524773,"name":"Health sciences/Medical research"},{"id":52524774,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2025-11-03T16:08:14+00:00","versionOfRecord":{"articleIdentity":"rs-7070224","link":"https://doi.org/10.1038/s41598-025-23102-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-27 15:58:43","publishedOnDateReadable":"October 27th, 2025"},"versionCreatedAt":"2025-08-04 09:36:07","video":"","vorDoi":"10.1038/s41598-025-23102-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-23102-y","workflowStages":[]},"version":"v1","identity":"rs-7070224","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7070224","identity":"rs-7070224","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}