NIRS Data Research Articles

Non-invasive optical and laboratory hematologic biomarkers correlate in patients with sickle cell disease.

The goal of this study is to identify non-invasive optical hemodynamic biomarkers that can index laboratory hematology measurements in sickle cell disease (SCD). We acquired frequency-domain NIRS (FD-NIRS) and diffuse correlation spectroscopy (DCS) data from the forearms and foreheads of 17 participants in a randomized, double-blind, placebo-controlled trial evaluating effects of isoquercetin (IQ) on thromboinflammation in SCD. We observed multiple, significant correlations between optical and hematology biomarkers including cerebral tissue oxygen saturation (StO2) and hematocrit (HCT); oxyhemoglobin ([O2Hb]) recovery rate and intercellular adhesion molecule 1 (ICAM-1); and blood flow index (BFI) reperfusion rate and coagulation index (CI). The potential of these non-invasive optical biomarkers for assessing vascular pathophysiology for the management of SCD warrants further exploration.

A Within-Subject Multimodal NIRS-EEG Classifier for Infant Data.

Functional Near Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are commonly employed neuroimaging methods in developmental neuroscience. Since they offer complementary strengths and their simultaneous recording is relatively easy, combining them is highly desirable. However, to date, very few infant studies have been conducted with NIRS-EEG, partly because analyzing and interpreting multimodal data is challenging. In this work, we propose a framework to carry out a multivariate pattern analysis that uses an NIRS-EEG feature matrix, obtained by selecting EEG trials presented within larger NIRS blocks, and combining the corresponding features. Importantly, this classifier is intended to be sensitive enough to apply to individual-level, and not group-level data. We tested the classifier on NIRS-EEG data acquired from five newborn infants who were listening to human speech and monkey vocalizations. We evaluated how accurately the model classified stimuli when applied to EEG data alone, NIRS data alone, or combined NIRS-EEG data. For three out of five infants, the classifier achieved high and statistically significant accuracy when using features from the NIRS data alone, but even higher accuracy when using combined EEG and NIRS data, particularly from both hemoglobin components. For the other two infants, accuracies were lower overall, but for one of them the highest accuracy was still achieved when using combined EEG and NIRS data with both hemoglobin components. We discuss how classification based on joint NIRS-EEG data could be modified to fit the needs of different experimental paradigms and needs.

Detection and quantification of cocoa powder adulteration using Vis-NIR spectroscopy with chemometrics approach

The rising demand for cocoa powder has resulted in an upsurge in market prices, leading to the emergence of adulteration practices aimed at achieving economic benefits. This study aimed to detect and quantify cocoa powder adulteration using visible and near-infrared spectroscopy (Vis-NIRS). The adulterants used in this study were powdered carob, cocoa shell, foxtail millet, soybean, and whole wheat. The NIRS data could not be resolved using Savitzky-Golay smoothing. Nevertheless, the application of a random forest and support vector machine successfully classified the samples with 100% accuracy. Quantification of adulteration using partial least squares (PLS), Lasso, Ridge, elastic Net, and RF regressions provided R2 higher than 0.96 and root mean square error <2.6. Coupling PLS with the Boruta algorithm produced the most reliable regression model (R2 = 1, RMSE = 0.0000). Finally, an online application was prepared to facilitate the determination of adulterants in the cocoa powder.

Cerebral Autoregulation Assessment through Near-Infrared Spectroscopy and Arterial Monitoring: Advancements and Clinical Implications

Cerebral autoregulation, maintaining stable cerebral blood flow across varying arterial pressures, is vital in-patient care during surgery. Traditional views suggest a mean arterial pressure range of 50-150 mm Hg for effective autoregulation. However, patient-specific variations in autoregulatory patterns, particularly in cases of impaired autoregulation, call for personalized hemodynamic and blood pressure management during surgical procedures. In the evaluation of cerebral autoregulation, NIRS serves as a beneficial monitoring tool. The cerebral oximetry index, correlating cerebral oxygen saturation with perfusion pressure, aids in determining autoregulation limits. The literature shows varying impacts of vasoactive drugs on patients with different autoregulatory responses, emphasizing the need for individualized care. In summary, NIRS is crucial for monitoring cerebral autoregulation, and adjusting arterial blood pressure targets based on NIRS data could improve prevention of cerebral hyper/hypoperfusion. This approach, moving away from a generalized strategy, advocates for a more customized, physiology-based patient management.

Pretreatment method standard normal variate (SNV) and baseline shift correction (BSC) on the NIRS-based soil spectrum for rapid prediction of soil nitrogen content

Enhancing agricultural production quality is a crucial role of nitrogen fertilization. Generally, the utilization of nitrogen fertilizer can promote plant growth, impart a more intense green hue to leaves and stems, and significantly augment biomass production. However, excessive application of nitrogen fertilizers can result in substantial cultivation cost increase and serious environmental degradation. Hence, it’s important to regulate the nitrogen content within the soil. The primary goal of this research is to employ the NIRS technique to rapidly and eco-friendly predict soil nitrogen content. Soil samples were obtained from farmlands situated in the Aceh Besar district, with spectral data gathered within a wavelength range of 1000 – 2500 nm at 0.2 nm intervals.. NIRS and Partial Least Square (PLS) spectrum data were used to make a prediction model of the nitrogen harvesting element. The results showed that the PLS method with SNV (R=0,72) and BSC (R=0,73) pretreatment showed predictive model capabilities that were almost the same as the raw data (R=0,75), although there was a slight improvement, this suggested that nitrogen elements in Aceh Besar region have not or can use the model with the suggestion to increase the amount of data and improve the treatment of the soil harvester method so that the nitrous content elements present in the ground do not easily evaporate so as to provide better model prediction results.

Identification of the human cerebral cortical hemodynamic response to passive whole-body movements using near-infrared spectroscopy.

This study aimed to investigate the cerebral cortical response to naturalistic vestibular stimulation induced by real physical motion and to validate the vestibular cerebral cortex previously identified using alternative vestibular stimulation. Functional NIRS data were collected from 12 right-handed subjects when they were sitting in a motion platform that generated three types of whole-body passive translational motion (circular, lateral, and fore-and-aft). The study found that different cortical regions were activated by the three types of motion. The cortical response was more widespread under circular motion in two dimensions compared to lateral and fore-and-aft motions in one dimensions. Overall, the identified regions were consistent with the cortical areas found to be activated in previous brain imaging studies. The results provide new evidence of brain selectivity to different types of motion and validate previous findings on the vestibular cerebral cortex.

Plaque progression is dependent on the local hemodynamics (time-averaged wall shear stress and TSVI) in combination with the plaque phenotype

Abstract Objective To investigate the predictive power of TSVI for plaque growth in human coronary arteries in plaques with different phenotypes. Methods 38 non-stented, non-culprit coronary arteries (MPACT study) were imaged with near-infrared spectroscopy intravascular ultrasound (NIRS-IVUS) and optical coherence tomography (OCT) at the baseline and after 12 months [2]. Coronary reconstructions were based on fusion of the multimodality imaging and used as input for computational fluid dynamics. Vessel-specific flow was measured using Doppler wire and used as input to calculate TAWSS and TSVI [2]. The coronary arteries were subdivided into 1.5 mmx45º sectors and the TAWSS and TSVI were classified as low, mid and high based on the range per vessel. Per sector the plaque growth was quantified as plaque burden (Plaque area/total vessel area*100%) change over time (ΔPAV). Based on the NIRS and OCT data, the sectors were characterized as lipid rich plaque (wall thickness&amp;gt;0.5 mm &amp; NIRS+, lipid rich(OCT)) or plaque without lipid (wall thickness &amp;gt;0.5 mm &amp; NIRS-, fibrous (OCT)) or plaque free wall (wall thickness &amp;lt;0.5 mm &amp;NIRS-, PFW (OCT)). The effect of TAWSS, TSVI and the combination with plaque phenotype on plaque progression was evaluated using Linear Mixed Model in SPSS. Results TSVI at baseline was significantly associated with PAV increase, with high PAV for high TSVI (p&amp;lt;0.001). A clear trend emerged for TAWSS, with high PAV for low TAWSS at baseline (Figure 1B). The highest plaque progression at 1-year follow-up was observed for lipid rich plaques (both based on NIRS and OCT) exposed to either low TAWSS or high TSVI (figure 2). Also in the PFW sectors clear plaque progression was observed dependent on either TAWSS or TSVI. Conclusion Lipid rich regions exposed to either low TAWSS or high TSVI are at the highest risk for plaque progression. These data suggest that although TAWSS and TSVI have a different mechanism of action on the endothelium, but both actions are involved in plaque progression.Figure 1Figure 2

Developing customized NIRS-EEG for infant sleep research: methodological considerations.

Studies using simultaneous functional near-infrared spectroscopy (fNIRS)-electroencephalography (EEG) during natural sleep in infancy are rare. Developments for combined fNIRS-EEG for sleep research that ensure optimal comfort as well as good coupling and data quality are needed. We describe the steps toward developing a comfortable, wearable NIRS-EEG headgear adapted specifically for sleeping infants ages 5 to 9 months and present the experimental procedures and data quality to conduct infant sleep research using combined fNIRS-EEG. 5- to 9-month-old infants participated. In phase 1, (10 = slept) participated using the non-wearable version of the NIRS-EEG headgear with 13-channel-wearable EEG and 39-channel fiber-based NIRS. In phase 2, infants (21 = slept) participated with the wireless version of the headgear with 20-channel-wearable EEG and 47-channel wearable NIRS. We used QT-NIRS to assess the NIRS data quality based on the good time window percentage, included channels, nap duration, and valid EEG percentage. The infant nap rate during phase 1 was (45% valid EEG data) and increased to 90% during phase 2 (100% valid EEG data). Infants slept significantly longer with the wearable system than the non-wearable system. However, there were more included good channels based on QT-NIRS in study phase 1 (61%) than phase 2 (50%), though this difference was not statistically significant. We demonstrated the usability of an integrated NIRS-EEG headgear during natural infant sleep with both non-wearable and wearable NIRS systems. The wearable NIRS-EEG headgear represents a good compromise between data quality, opportunities of applications (home visits and toddlers), and experiment success (infants' comfort, longer sleep duration, and opportunities for caregiver-child interaction).

Comparison of near and mid-infrared reflectance spectroscopy for the estimation of soil organic carbon fractions in Madagascar agricultural soils

Assessing the different pools of soil organic carbon (SOC) improves our understanding of how and at what rate the different forms of carbon (C) are being formed or lost in soils. Physical fractionation of soil organic matter (SOM) has often been used to separate and quantify SOC pools, but this approach is very tedious and can rarely be performed on large sample sets. Infrared spectroscopy has proven useful for time- and cost-effective quantification of total SOC, which prompted us to study its ability to characterise the distribution of SOC in physical fractions. This study aimed to compare the potential of near- and mid-infrared reflectance spectroscopy (NIRS and MIRS, respectively) to predict the distribution of SOC in particle-size and particle-density fractions, using spectra of unfractionated soils. A set of 134 sieved (< 2 mm) soil samples originating from seven sites in contrasting pedoclimatic regions of Madagascar was studied. For each sample, five SOM fractions were separated: the particulate organic matter fraction (POM) and the particle-size fractions >200 μm, 50–200-μm, 20–50-μm and < 20 μm. The mass (g fraction 100 g−1 soil), SOC concentration (gC kg−1 fraction) and SOC amount (gC fraction kg−1 soil) were determined for each fraction in the laboratory. The NIR and MIR spectra were acquired on finely ground (< 0.2 mm) aliquots of unfractionated soil. Then, spectra were used for the prediction of each variable (i.e., the mass, SOC concentration and amount of each fraction, and SOC content of unfractionated soil), which was achieved with locally weighted partial least squares regression (LW-PLSR) on NIRS and MIRS data separately. For both spectral ranges, the same samples were used for calibration (n = 109, selected for spectral representativeness) and validation (n = 25).Models based on NIRS and MIRS yielded excellent predictions for SOC content in unfractionated soil (R2 = 0.98 and ratio of performance to interquartile range RPIQ ≥13) and accurate predictions (R2 ≥ 0.75 and RPIQ ≥2) for the mass, SOC concentration and SOC amount in most fractions. The predictions of SOC concentrations and SOC amounts were better in the fractions <200 μm (R2 ≥ 0.85 and RPIQ >3), especially in the fraction <20 μm (R2 > 0.9 and RPIQ >4.5). In most cases, NIRS slightly outperformed MIRS. This result contradicts most previous studies performed on soils from temperate regions but confirms those performed on soils from tropical regions.Infrared spectroscopy allowed accurate prediction of SOC distribution in particle-size fractions, which paves the way to high-throughput characterization of SOM.

MP37-18 A PROSPECTIVE COHORT STUDY DEMONSTRATING EXERCISE EFFECTS FROM HOME BASED PELVIC FLOOR MUSCLE TRAINING TO REHABILITATE NEUROGENIC LOWER URINARY TRACT DYSFUNCTION; 3D REGIONAL EFFECTS

You have accessJournal of UrologyCME1 Apr 2023MP37-18 A PROSPECTIVE COHORT STUDY DEMONSTRATING EXERCISE EFFECTS FROM HOME BASED PELVIC FLOOR MUSCLE TRAINING TO REHABILITATE NEUROGENIC LOWER URINARY TRACT DYSFUNCTION; 3D REGIONAL EFFECTS Lynn Stothers, Andrew Macnab, Naser Hakimi, W. N. J. M. Colier, M. Maresse, and P. Pourabbassi Lynn StothersLynn Stothers More articles by this author , Andrew MacnabAndrew Macnab More articles by this author , Naser HakimiNaser Hakimi More articles by this author , W. N. J. M. ColierW. N. J. M. Colier More articles by this author , M. MaresseM. Maresse More articles by this author , and P. PourabbassiP. Pourabbassi More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003275.18AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Women with neurogenic lower urinary tract dysfunction (NLUTD) rely on pelvic floor muscle rehabilitation therapy (PFMT) to aid continence. However, in NLUTD including spinal cord injury (SCI) PFM function is known to be affected regionally/asymmetrically which is not detected through exam or manometry. Objective: Report clinical translation of a optical PFM system, patient informed design to provide a 3D measures of PFM contractions quantifiable by reoxygenation recovery following sustained maximal voluntary contractions (SMVC) employed in PFMT. METHODS: An optical transvaginal interface was engineered using a circumferential grid of 6 light emitting diodes (LED’s) (wavelengths 750 & 860nm) & 4 photodiode detectors. Engineering design met preferences for size, shape, weight & ergometric features from a prior end user survey (Figure 1) Sampling at 50Hz, changes in oxy and deoxy-hemoglobin concentration (O2Hb/HHb) during PFM contractions monitored in real time, multiple point, from which HbDiff (O2Hb-HHb) is derived. All completed pre/post Qualiveen, were evaluated via NIRS during a standardized sequence of maximum & sustained PFM contractions, engaged in 12 weeks home based PFMT then repeat optical-derived measures of reoxygenation recovery. RESULTS: N=14 women (5 controls 7 NLUTD) 25–82 yrs. Baseline Qualiveen scores range: 0 (controls)-0.7 (NLUTD). Figure 1: output showing post PFMT composite graphics with NIRS data from channels monitoring the right & left sides of both the anterior & posterior segments of the PFM during 3 SMVC’s. All subjects were monitored successfully pre/post PFMT demonstrating 3D detailed functional ability of small segments of the pelvic floor & documenting effects of training. CONCLUSIONS: Optical detection of a validated quantifiable parameter of muscle reoxygenation in the PFM is feasible using this transvaginal optical interface designed based on women with NLUTD end user feedback. The multipoint design allows small regional variations in PFM functionality to be quantified & improvements from PFMT training effects to be measured in NLUTD/SCI to enhance rehabilitation of women living with NLUTD known to impact negatively on quality of life. Source of Funding: Craig H Neilsen Foundation © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e521 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Lynn Stothers More articles by this author Andrew Macnab More articles by this author Naser Hakimi More articles by this author W. N. J. M. Colier More articles by this author M. Maresse More articles by this author P. Pourabbassi More articles by this author Expand All Advertisement PDF downloadLoading ...

Classification of multiple cancer types by combination of plasma-based near-infrared spectroscopy analysis and machine learning modeling

Background and aimNear-infrared spectroscopy (NIRS) is a non-invasive and convenient tool, which gains features related to chemical components in biological samples. Machine learning (ML) has been popularized in medical diagnosis. This study aimed at investigating a novel cancer diagnosis strategy using NIRS data based ML modeling. MethodsPlasma samples were collected from a total of 247 participants, including lung cancer, cervical cancer, nasopharyngeal cancer, and healthy control, and were randomly split into train set and test set. After performing NIRS analysis, the train dataset was utilized to train ML models, including partial least-squares (PLS), random forest (RF), gradient boosting machine (GBM), and support-vector machine (SVM). Subsequently, these models were tested for their prediction performance by the test set. ResultsAll ML models demonstrated high prediction performance in differentiating cancers from controls, and SVM had high prediction accuracy for different types of cancers. SVM was considered as the most suitable model for its minimal computational cost and high accuracies for both binary and quaternary classification. ConclusionsThis strategy coupling NIRS with ML is insightful that may aid in clinic cancer diagnosis, while further studies should test our results in a larger cohort with better representativeness.

Reproducibility of infant fNIRS studies: a meta-analytic approach.

Concerns about the reproducibility of experimental findings have recently emerged in many disciplines, from psychology to medicine and neuroscience. As NIRS is a relatively recent brain imaging technique, the question of reproducibility has not yet been systematically addressed. The current study seeks to test the replicability of effects observed in NIRS experiments assessing young infants' rule-learning ability. We conducted meta-analyses and mixed-effects modeling-based inferential statistics to determine whether effect sizes were replicable and comparable in a sample of 23 NIRS studies investigating infants' abilities to process repetition- and diversity-based regularities in linguistic and nonlinguistic auditory and visual sequences. Additionally, we tested whether effect sizes were modulated by different factors such as the age of participants or the laboratory. We obtained NIRS data from 12 published and 11 unpublished studies. The 23 studies involved a total of 487 infants, aged between 0 and 9 months, tested in four different countries (Canada, France, Italy, and USA). Our most important finding is that study and laboratory were never significant moderators of variation in effect sizes, indicating that results replicated reliably across the different studies and labs included in the sample. We observed small-to-moderate effect sizes, similar to effect sizes found with other neuroimaging and behavioral techniques in the developmental literature. In line with existing findings, effect sizes were modulated by the participants' age and differed across the different regularities tested, with repetition-based regularities giving rise to the strongest effects; in particular, the overall magnitude of this effect in the left temporal region was 0.27 when analyzing the entire dataset. Meta-analysis is a useful tool for assessing replicability and cross-study variability. Here, we have shown that infant NIRS studies in the language domain replicate robustly across various NIRS machines, testing sites, and developmental populations.

Bayesian Fusion Model Enhanced Codfish Classification Using Near Infrared and Raman Spectrum.

In this study, a Bayesian-based decision fusion technique was developed for the first time to quickly and non-destructively identify codfish using near infrared (NIRS) and Raman spectroscopy (RS). NIRS and RS spectra from 320 codfish samples were collected, and separate partial least squares discriminant analysis (PLS-DA) models were developed to establish the relationship between the raw data and cod identity for each spectral technique. Three decision fusion methods: decision fusion, data layer or feature layer, were tested and compared. The decision fusion model based on the Bayesian algorithm (NIRS-RS-B) was developed on the optimal discrimination features of NIRS and RS data (NIRS-RS) extracted by the PLS-DA method whereas the other fusion models followed conventional, non-Bayesian approaches. The Bayesian model showed enhanced classification metrics (92% sensitivity, 98% specificity, 98% accuracy) that were significantly superior to those demonstrated by any of other two spectroscopic methods (NIRS, RS) and the two data fusion methods (data layer fused, NIRS-RS-D, or feature layer fused, NIRS-RS-F). This novel proposed approach can provide an alternative classification for codfish and potentially other food speciation cases.

Splanchnic-cerebral oxygenation ratio associated with packed red blood cell transfusion in preterm infants.

Splanchnic-cerebral oxygenation ratio (SCOR), the ratio of splanchnic tissue oxygen (StO2 s) to simultaneously measured cerebral tissue oxygen (StO2 c), has been described as a surrogate to detect impaired splanchnic oxygenation associated with hypoperfusion status such as necrotizing enterocolitis. This concept is based on the presumption that any change in SCOR indicates a corresponding change in splanchnic tissue oxygenation as the numerator, whereas cerebral tissue oxygenation as the denominator remains stable. However, it is questionable to utilise this concept to detect splanchnic oxygenation changes in the context of packed red blood cell transfusion (PRBCT). The current study examines the contribution of both cerebral and splanchnic oxygenation components to PRBCT-associated SCOR changes in preterm infants. Prospective cohort study. Neonatal intensive care. Hemodynamically stable infants: Gestation <32 weeks; birth weight <1500 g; postmenstrual age <37 weeks: tolerating ≥120 ml/kg/day feed volume. PRBCT at 15 ml/kg, over 4 h. Transfusion-associated changes were determined by performing mixed models for repeated measures analysis between the 4-h mean pre-transfusion values (SCOR 0, StO2 s 0, and StO2 c 0) and the post-transfusion hourly mean values for the next 28 h (SCOR 1-28, StO2 s 1-28, and StO2 c 1-28). Dunnett's method was used to adjust for the multiplicity of the p value. Of 30 enrolled infants 14 [46.7%] male; median [IQR] birth weight, 923 [655-1064] g; gestation, 26.4 [25.5-28.1] weeks; enrolment weight, 1549 [1113-1882] g; and postmenstrual age, 33.6 [32.4-35.0] weeks, one infant was excluded because of corrupted NIRS data. With the commencement of PRBCT, SCOR demonstrated a downward trend throughout the study period. This drift was associated with an increasing StO2 c trend, while StO2 s remained unchanged throughout the study period. PRBCT-associated SCOR decrease suggests improvement in cerebral oxygenation rather than worsening splanchnic oxygenation. Our study underlines that it is necessary to determine individual components of SCOR, namely cerebral and splanchnic StO2 to understand SCOR changes in the context of PRBCT.

Comparison of benchtop near infrared and micro near infrared spectrometer for quality control of dried ginger and its different degrees of processed products

Dried ginger (DG) is the dried rhizome of Zingiber offcinale Rosc. (Fam. Zingiberaceae), Ginger charcoal (GC) is the processed products of DG, due to the difficulty in controlling the processing degree, unqualified processed products (light carbonized ginger, LCG; heavy carbonized ginger, HCG) often appear, which can both affect clinical efficacy. To ensure the quality and safety of DG and its processed products, this paper mainly focused on the feasibility of rapidly determining the quality of DG and its processed products by benchtop-NIR spectrometer and Micro-NIR spectrometer. A total of 100 samples were scanned by benchtop-NIR spectrometer and Micro-NIR spectrometer respectively. The collected NIRS data were qualitatively and quantitatively analyzed by different models. The partial least squares discriminant models established based on the benchtop-NIR and Micro-NIR spectroscopy both have a discrimination rate of 100% for the prediction set. Three characteristic wavelength selection methods (CARS, VCPA-IRIV and VCPA-GA) were used to improve the performance of partial least squares regression model. The results showed that the benchtop-NIR model with the wavelength selected by the CARS algorithm performed best, and the Micro-NIR model performance of the characteristic wavelength selected by the VCPA-IRIV algorithm had a better prediction ability. The consistency evaluation of the prediction models showed that the two instruments had a very good coherence except for the zingerone model. Therefore, A micro-NIR spectrometer combined with an appropriate wavelength selection method can precisely distinguish DG, LCG, MCG and HCG from each other and accurately determine the five gingerol compounds in DG and its processed products.

Outcome Prediction in Neonatal Hypoxic-Ischaemic Encephalopathy Using Neurophysiology and Neuroimaging

Objective: The aim of the study was to determine the predictive power of the combined use of neurophysiological (amplitude-integrated electroencephalography [aEEG], near-infrared spectroscopy [NIRS]) methods and neuroimaging (magnetic resonance imaging [MRI]) for long-term outcome prediction in neonates with hypoxic-ischaemic encephalopathy (HIE). Study Design: Prospective cohort study of 56 patients with moderate to severe HIE and hypothermia treatment at the Medical University of Vienna between 2008 and 2020. aEEG and NIRS were recorded continuously over a period of >4 days (102 h) starting at the initiation of hypothermia treatment, MRI was performed at a median age of 8 days. Receiver operating characteristic curves and area under the curve were calculated to evaluate the prognostic ability of aEEG, NIRS, and MRI parameters for outcome assessed via Bayley Scales of Infant Development 3rd edition at 2 years of age. Results: Combined aEEG and MRI parameters showed highest predictive power regarding long-term outcome. The highest area under the curve values (0.96–0.99) were obtained for aEEG (combination of background pattern and sleep-wake cycling) between 66 and 102 h after initiation of hypothermia in combination with MRI findings. NIRS parameters did not differ significantly between infants with favourable and adverse outcome. Conclusions: Combined aEEG and MRI parameter scores were more predictive than single parameter scores. No further improvement was observed when combining aEEG/MRI with NIRS data.

A First Attempt to Combine NIRS and Plenoptic Cameras for the Assessment of Grasslands Functional Diversity and Species Composition

Grassland represents more than half of the agricultural land. Numerous metrics (biomass, functional trait, species composition) can be used to describe grassland vegetation and its multiple functions. The measures of these metrics are generally destructive and laborious. Indirect measurements using optical tools are a possible alternative. Some tools have high spatial resolutions (digital camera), and others have high spectral resolutions (Near Infrared Spectrometry NIRS). A plenoptic camera is a multifocal camera that produces clear images at different depths in an image. The objective of this study was to test the interest of combining plenoptic images and NIRS data to characterize different descriptors of two Mediterranean legumes mixtures. On these mixtures, we measured biomass, species biomass, and functional trait diversity. NIRS and plenoptic images were acquired just before the field measurements. The plenoptic images were analyzed using Trainable Weka Segmentation ImageJ to evaluate the percentage of each species in the image. We calculated the average and standard deviation of the different colors (red, green, blue reflectance) in the image. We assessed the percentage of explanation of outputs of the images and NIRS analyses using variance partition and partial least squares. The biomass Trifolium michelianum and Vicia sativa were predicted with more than 50% variability explained. For the other descriptors, the variability explained was lower but nevertheless significant. The percentage variance explained was nevertheless quite low, and further work is required to produce a useable tool, but this work already demonstrates the interest in combining image analysis and NIRS.

Cortical Activity During Pain Relief: A Functional Near-infrared Spectroscopy Study of Offset Analgesia

Offset analgesia is defined by a dramatic drop in perceived pain intensity with a relatively small decrease in noxious input. Although functional magnetic resonance imaging studies implicate subcortical descending inhibitory circuits during offset analgesia, the role of cortical areas remains unclear. The current study performed task-based and connectivity analyses to elucidate the cortical correlates of offset analgesia using functional near infrared spectroscopy (fNIRS). This study was approved by the University of Pittsburgh IRB. After providing informed consent, twenty-four healthy volunteers (18-50 years old) underwent fNIRS scanning at rest and then during offset (OS) and control (Con) heat stimuli applied to the forearm. NIRS data were collected with a head probe targeting bilateral frontal and somatomotor cortices (NIRx NIRSport2). Pain intensity was recorded continuously on a computerized visual analogue scale (COVAS, Medoc). NIRS data were preprocessed and analyzed in NIRS Toolbox, a free analysis package based in Matlab. After controlling for non-neural hemodynamic responses in superficial tissues, widespread increases in cortical oxygenated hemoglobin concentration were observed, reflecting cortical activation during heat pain. OS – Con contrasts revealed deactivations in bilateral medial prefrontal cortex (mPFC) and bilateral somatosensory cortex (SSC) associated with offset analgesia. Right dorsolateral prefrontal cortex (dlPFC) showed activation only during OS. Functional connectivity analyses are ongoing. These data demonstrate opposing cortical activation patterns during offset analgesia and support a model in which right dlPFC underlies ongoing evaluation of pain intensity change. With predictions of decreasing pain intensity, right dlPFC activation likely inhibits ascending noxious input via subcortical pathways resulting in SSC and mPFC deactivation. This study identifies cortical circuitry underlying offset analgesia and introduces the use of fNIRS to study pain modulation in an outpatient clinical environment. Grant support from NIGMS T32GM075770 (BA), NIBIB R01EB028248-01 (TH), the Virginia Kaufman Endowment Fund in the form of a Pitt CTSI competitive research grant (BA, HS, AW, TH), and the International Anesthesia Research Society in the form of the Mentored Research Award (BA).

The performance of phenomic selection depends on the genetic architecture of the target trait

Key messageThe phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL.Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study.

fNIRS hyperscanning is widely used to detect the neurobiological underpinnings of social interaction. With this technique, researchers qualify the concurrent brain activity of two or more interactive individuals with a novel index called inter-brain synchrony (IBS) (i.e., phase and/or amplitude alignment of the neuronal or hemodynamic signals across time). A protocol for conducting fNIRS hyperscanning experiments on collaborative learning dyads in a naturalistic learning environment is presented here. Further, a pipeline of analyzing IBS of oxygenated hemoglobin (Oxy-Hb) signal is explained. Specifically, the experimental design, the process of NIRS data recording, data analysis methods, and future directions are all discussed. Overall, implementing a standardized fNIRS hyperscanning pipeline is a fundamental part of second-person neuroscience. Also, this is in line with the call for open-science to aid the reproducibility of research.