Construction of a novel Au@Os mediated TMB-H2O2 platform with dual-signal output for rapid and accurate detection of ziram in food.

The 3,3',5,5'-tetramethylbenzidine-H2O2 (TMB-H2O2) platform has gained widespread use for rapid detection of various analytes in foods. However, the existing TMB-H2O2 platforms suffer from limited accuracy, as their signal output is confined to the visible region, which is prone to interference from various food colorants in real samples. To address this challenge, a novel Au@Os-mediated TMB-H2O2 platform is developed for both rapid and accurate detection of analytes in foods. The prepared Au@Os NPs exhibit remarkable peroxidase-like activity, making the platform display dual absorption peaks in visible and near-infrared (NIR) regions, respectively. This Au@Os-mediated TMB-H2O2 platform exhibited three linear ranges across different concentrations of ziram from 1-100, 150-600, and 800-2000 nM with limit of detection (LOD) 7.9 nM and limit of quantification (LOQ) 24.15 nM respectively. Further, the Au@Os-mediated TMB-H2O2 platform was also used for rapid and accurate detection of ziram in real food samples like apple, tomato, and black tea.

Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics.

A rapid method was developed for determining the total flavonoid and protein content in Tartary buckwheat by employing near-infrared spectroscopy (NIRS) and various machine learning algorithms, including partial least squares regression (PLSR), support vector regression (SVR), and backpropagation neural network (BPNN). The RAW-SPA-CV-SVR model exhibited superior predictive accuracy for both Tartary and common buckwheat, with a high coefficient of determination (R2p = 0.9811) and a root mean squared error of prediction (RMSEP = 0.1071) for flavonoids, outperforming both PLSR and BPNN models. Additionally, the MMN-SPA-PSO-SVR model demonstrated exceptional performance in predicting protein content (R2p = 0.9247, RMSEP = 0.3906), enhancing the effectiveness of the MMN preprocessing technique for preserving the original data distribution. These findings indicate that the proposed methodology could efficiently assess buckwheat adulteration analysis. It can also provide new insights for the development of a promising method for quantifying food adulteration and controlling food quality.

One-class modelling applied to ATR-FTIR spectroscopy for determining document forgeries related to paper aging.

One of the great challenges of document analysis is determining document forgeries. The present work proposes a non-destructive approach to discriminate natural and artificially aged papers using infrared spectroscopy and soft independent modeling by class analogy (SIMCA) algorithms. This is of particular interest in cases of document falsifications made by artificial aging, for this study, SIMCA, and Data-Driven SIMCA (DD-SIMCA) classification models were built using naturally aged paper samples, taken from three time periods: 1st period from 1998 to 2003; 2nd period from 2004 to 2009; and 3rd period from 2010 to 2015. Artificially aged samples (exposed to high temperature or UV radiation) were used as test sets. Promising results in detecting document falsifications related to aging were obtained. Samples artificially aged at high temperature were correctly discriminated from the authentic samples (naturally aged) with 100% accuracy. In contrast, the samples under the photodegradation process showed a lower classification performance, with results above 90%.

Quantitative analysis of soil potassium by near-infrared (NIR) spectroscopy combined with a three-step progressive hybrid variable selection strategy.

Soil potassium is a crucial nutrient element necessary for crop growth, and its efficient measurement has become essential for developing rational fertilization plans and optimizing crop growth benefits. At present, data mining technology based on near-infrared (NIR) spectroscopy analysis has proven to be a powerful tool for real-time monitoring of soil potassium content. However, as technology and instruments improve, the curse of the dimensionality problem also increases accordingly. Therefore, it is urgent to develop efficient variable selection methods suitable for NIR spectroscopy analysis techniques. In this study, we proposed a three-step progressive hybrid variable selection strategy, which fully leveraged the respective strengths of several high-performance variable selection methods. By sequentially equipping synergy interval partial least squares (SiPLS), the random forest variable importance measurement (RF(VIM)), and the improved mean impact value algorithm (IMIV) into a fusion framework, a soil important potassium variable selection method was proposed, termed as SiPLS-RF(VIM)-IMIV. Finally, the optimized variables were fitted into a partial least squares (PLS) model. Experimental results demonstrated that the PLS model embedded with the hybrid strategy effectively improved the prediction performance while reducing the model complexity. The RMSET and RT on the test set were 0.01181% and 0.88246, respectively, better than the RMSET and RT of the full spectrum PLS, SiPLS, and SiPLS-RF(VIM) methods. This study demonstrated that the hybrid strategy established based on the combination of NIR spectroscopy data and the SiPLS-RF(VIM)-IMIV method could quantitatively analyze soil potassium content levels and potentially solve other issues of data-driven soil dynamic monitoring.

Physicochemical characteristics of airborne microplastics of a typical coastal city in the Yangtze River Delta Region, China.

Airborne microplastics (MPs) are important pollutants that have been present in the environment for many years and are characterized by their universality, persistence, and potential toxicity. This study investigated the effects of terrestrial and marine transport of MPs in the atmosphere of a coastal city and compared the difference between daytime and nighttime. Laser direct infrared imaging (LDIR) and polarized light microscopy were used to characterize the physical and chemical properties of MPs, including number concentration, chemical types, shape, and size. Backward trajectories were used to distinguish the air masses from marine and terrestrial transport. Twenty chemical types were detected by LDIR, with rubber (16.7%) and phenol-formaldehyde resin (PFR; 14.8%) being major components. Three main morphological types of MPs were identified, and fragments (78.1%) are the dominant type. MPs in the atmosphere were concentrated in the small particle size segment (20-50 µm). The concentration of MPs in the air mass from marine transport was 14.7 items/m3 - lower than that from terrestrial transport (32.0 items/m3). The number concentration of airborne MPs was negatively correlated with relative humidity. MPs from terrestrial transport were mainly rubber (20.2%), while those from marine transport were mainly PFR (18%). MPs in the marine transport air mass were more aged and had a lower number concentration than those in the terrestrial transport air mass. The number concentration of airborne MPs is higher during the day than at night. These findings could contribute to the development of targeted control measures and methods to reduce MP pollution.

In Vivo Imaging of Rodent Retina in Retinal Disease.

High-quality imaging of the retina is crucial to the diagnosis and monitoring of disease, as well as for evaluating the success of therapeutics in human patients and in preclinical animal models. Here, we describe the basic principles and methods for in vivo retinal imaging in rodents, including fundus imaging, fluorescein angiography, optical coherence tomography, fundus autofluorescence, and infrared imaging. After providing a concise overview of each method and detailing the retinal diseases and conditions that can be visualized through them, we will proceed to discuss the advantages and disadvantages of each approach. These protocols will facilitate the acquisition of optimal images for subsequent quantification and analysis. Additionally, a brief explanation will be given regarding the potential results and the clinical significance of the detected abnormalities.

In situ study on articular cartilage degeneration in simulated microgravity by HOF-ATR-FTIR spectroscopy.

Fourier transform infrared spectroscopy (FTIRS) can provide rich information on the composition and content of samples, enabling the detection of subtle changes in tissue composition and structure. This study represents the first application of FTIRS to investigate cartilage under microgravity. Simulated microgravity cartilage model was firstly established by tail-suspension (TS) for 7, 14 and 21 days, which would be compared to control samples. A self-developed hollow optical fiber attenuated total reflection (HOF-ATR) probe coupled with a FTIR spectrometer was used for the spectral acquisition of cartilage samples in situ, and one-way analysis of variance (ANOVA) was employed to analyze the changes in the contents of cartilage matrix at different stages. The results indicate that cartilage degenerates in microgravity, the collagen content gradually decreases with the TS time, and the structure of collagen fibers changes. The trends of proteoglycan content and collagen integrity show an initial decrease followed by an increase, ultimately significantly decreasing. The findings provide the basis for the cartilage degeneration in microgravity with TS time, which must be of real significance for space science and health detection.

Sugarcane disease recognition through visible and near-infrared spectroscopy using deep learning assisted continuous wavelet transform-based spectrogram.

Utilizing visible and near-infrared (Vis-NIR) spectroscopy in conjunction with chemometrics methods has been widespread for identifying plant diseases. However, a key obstacle involves the extraction of relevant spectral characteristics. This study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380-1400 nm) to improve the accuracy of sugarcane diseases recognition. Using 130 sugarcane leaf samples, the obtained one-dimensional CWT coefficients from Vis-NIR spectra were transformed into two-dimensional spectrograms. Employing CNN, spectrogram features were extracted and incorporated into decision tree, K-nearest neighbour, partial least squares discriminant analysis, and random forest (RF) calibration models. The RF model, integrating spectrogram-derived features, demonstrated the best performance with an average precision of 0.9111, sensitivity of 0.9733, specificity of 0.9791, and accuracy of 0.9487. This study may offer a non-destructive, rapid, and accurate means to detect sugarcane diseases, enabling farmers to receive timely and actionable insights on the crops' health, thus minimizing crop loss and optimizing yields.

A near-infrared ratiometric fluorescent probe for the sensing and imaging of sulfur dioxide derivatives in living systems.

As a reactive sulfur species, sulfur dioxide (SO2) and its derivatives play crucial role in various physiological processes, which can maintain redox homeostasis at normal levels and lead to the occurrence of many diseases at abnormal levels. So, the development of a suitable fluorescent probe is a crucial step in advancing our understanding of the role of SO2 derivatives in living organisms. Herein, we developed a near-infrared fluorescent probe (SP) based on the ICT mechanism to monitor SO2 derivatives in living organisms in a ratiometric manner. The probe SP exhibited excellent selectivity, good sensitivity, fast response rate (within 50 s), and low detection limit (1.79 µM). In addition, the cell experiment results suggested that the SP has been successfully employed for the real-time monitoring of endogenous and exogenous SO2 derivatives with negligible cytotoxicity. Moreover, SP was effective in detecting SO2 derivatives in mice.

Vibrational spectroscopy of interstellar PAHs: 1-cyanonaphthalene and 2-cyanonaphthalene.

In this work, we present a comprehensive experimental and theoretical study of the vibrational spectra of PAH molecules recently detected in the interstellar medium: 1-cyanonaphthalene and 2-cyanonaphthalene. The room temperature IR spectra of 1- and 2-cyanonaphthalene in the region 100-3100 cm-1 and their vibrational Raman spectra in the region 35-3100 cm-1 are reported here for the first time. A detailed spectral analysis is carried out using quantum chemical calculations employing the DFT methodology. Anharmonic corrections using the VPT2 method yield excellent agreement with the experimental spectra. A re-investigation of the vibrational spectrum of the parent molecule: naphthalene validates the experimental and theoretical methods used. A consistent set of assignments is reported for the fundamental bands of 1- and 2-cyanonapththalene. The experimental and theoretical data presented here would be useful inputs for modelling the role of cyanonaphthalene in astrophysical processes.

A three-stage wavelength selection algorithm for near-infrared spectroscopy calibration.

The near-infrared spectral data is highly high dimensional and contains redundant information, it is necessary to identify the most representative characteristic wavelengths before modeling to improve model accuracy and reliability. At present, there are many methods for selecting the characteristic wavelengths of NIR spectroscopy, but the collinearity among wavelengths is still a main issue that leads to poor model effects. Therefore, this study proposes a three-stage wavelength selection algorithm (Stage III) to reduce redundancy in NIR spectral data and collinearity between wavelength variables, resulting in a simpler and more accurate predictive model. The research uses a public NIR data set of corn samples as its subject. Initially, the wavelengths with the higher correlation coefficients are chosen after calculating the relationship coefficients between every wavelength vector and the concentration vector. On this basis, the correlation coefficients between the vectors of each wavelength point are calculated, and those wavelength points with smaller correlation coefficients with other wavelength points are selected. Ultimately, the stepwise regression analysis selects the wavelengths that provide substantial value to the model as the variables for modeling, leading to the development of a multiple linear regression model. The results show that the model using the three-stage wavelength selection algorithm outperforms those using the full spectrum, Stages I and Stage II, and the coefficient of determination of the test set of the Stage III-MLR model achieved an accuracy of 0.9360. Instead of the successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS), Stage III is better in the model prediction accuracy. Therefore, the three-stage wavelength selection algorithm is an effective wavelength selection algorithm that can effectively model NIR spectroscopy, reduce the collinearity between the wavelength variables, simplify the complexity of the model, and improve the prediction precision of the model.

Grape pomace and pecan shell fortified bread: The effect of dietary fiber-phenolic compounds interaction on the in vitro accessibility of phenolic compounds and in vitro glycemic index.

Grape pomace (GP) and pecan shell (PS) are two by-products rich in phenolic compounds (PC), and dietary fiber (DF) that may be considered for the development of functional baked foods. In this study, four formulations with different GP:PS ratios (F1(8%:5%), F2(5%:5%), F3(5%:2%), F4(0%:5%), and control bread (CB)) were elaborated and characterized (physiochemical and phytochemical content). Also, their inner structure (SEM), changes in their FTIR functional group's vibrations, and the bioaccessibility of PC and sugars, including an in vitro glycemic index, were analyzed. Results showed that all GP:PS formulations had higher mineral, protein, DF (total, soluble, and insoluble), and PC content than CB. Additionally, PC and non-starch polysaccharides affected gluten and starch absorbance and pores distribution. In vitro digestion model showed a reduction in the glycemic index for all formulations, compared to CB. These findings highlight the possible health benefits of by-products and their interactions in baked goods.

Toward understanding ammonia capture in two amino-functionalized metal-organic frameworks using in-situ infrared spectroscopy and DFT calculation.

Two isostructural, three-dimensional, interpenetrated amino-functionalized Metal-Organic Frameworks (Co-2AIN-MOF and Cd-2AIN-MOF) based on 2-aminoisonicotinic acid (2AIN) were synthesized, structurally characterized and determined. Based on the PXRD analysis, the solvent exchange hardly changed their framework structure, and the samples fully activated by methanol can be achieved and examined by infrared spectroscopy. Due to the presence of the carbonyl group and free amino groups in the pore of the framework, the NH3 uptakes of Co-2AIN-MOF and Cd-2AIN-MOF are 11.70 and 13.81 mmol/g and at 1 bar, respectively. In-situ Infrared spectroscopy and DFT calculations revealed the different adsorption sites and processes between Co-2AIN-MOF and Cd-2AIN-MOF.

Identification and quantification of goat milk adulteration using mid-infrared spectroscopy and chemometrics.

The fraudulent adulteration of goat milk with cheaper and more available milk of other species such as cow milk is occurrence. The aims of the present study were to investigate the effect of goat milk adulteration with cow milk on the mid-infrared (MIR) spectrum and further evaluate the potential of MIR spectroscopy to identify and quantify the goat milk adulterated. Goat milk was adulterated with cow milk at 5 different levels including 10%, 20%, 30%, 40%, and 50%. Statistical analysis showed that the adulteration had significant effect on the majority of the spectral wavenumbers. Then, the spectrum was preprocessed with standard normal variate (SNV), multiplicative scattering correction (MSC), Savitzky-Golay smoothing (SG), SG plus SNV, and SG plus MSC, and partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) were used to establish classification and regression models, respectively. PLS-DA models obtained good results with all the sensitivity and specificity over 0.96 in the cross-validation set. Regression models using raw spectrum obtained the best result, with coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) of cross-validation set were 0.98, 2.01, and 8.49, respectively. The results preliminarily indicate that the MIR spectroscopy is an effective technique to detect the goat milk adulteration with cow milk. In future, milk samples from different origins and different breeds of goats and cows should be collected, and more sophisticated adulteration at low levels should be further studied to explore the potential and effectiveness of milk mid-infrared spectroscopy and chemometrics.

An integrated and rapid evaluation of Curcumae Radix from different botanical origins based on chemical components, antiplatelet aggregation effect and Fourier transform near-infrared spectroscopy.

Curcumae Radix (CR) is a widely used traditional Chinese medicine with significant pharmaceutical importance, including enhancing blood circulation and addressing blood stasis. This study aims to establish an integrated and rapid quality assessment method for CR from various botanical origins, based on chemical components, antiplatelet aggregation effects, and Fourier transform near-infrared (FT-NIR) spectroscopy combined with multivariate algorithms. Firstly, ultra-performance liquid chromatography-photodiode array (UPLC-PDA) combined with chemometric analyses was used to examine variations in the chemical profiles of CR. Secondly, the activation effect on blood circulation of CR was assessed using an in vitro antiplatelet aggregation assay. The studies revealed significant variations in chemical profiles and antiplatelet aggregation effects among CR samples from different botanical origins, with constituents such as germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin, and curcumin showing a positive correlation with antiplatelet aggregation biopotency. Thirdly, FT-NIR spectroscopy was integrated with various machine learning algorithms, including Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Subspace K-Nearest Neighbors (Subspace KNN), to classify CR samples from four distinct sources. The result showed that FT-NIR combined with KNN and SVM classification algorithms after SNV and MSC preprocessing successfully distinguished CR samples from four plant sources with an accuracy of 100%. Finally, Quantitative models for active constituents and antiplatelet aggregation bioactivity were developed by optimizing the partial least squares (PLS) model with interval combination optimization (ICO) and competitive adaptive reweighted sampling (CARS) techniques. The CARS-PLS model achieved the best predictive performance across all five components. The coefficient of determination (R2p) and root mean square error (RMSEP) in the independent test sets were 0.9708 and 0.2098, 0.8744 and 0.2065, 0.9511 and 0.0034, 0.9803 and 0.0066, 0.9567 and 0.0172 for germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin and curcumin, respectively. The ICO-PLS model demonstrated superior predictive capabilities for antiplatelet aggregation biotency, achieving an R2p of 0.9010, and an RMSEP of 0.5370. This study provides a valuable reference for the quality evaluation of CR in a more rapid and comprehensive manner.

Exploring near-infrared autofluorescence properties in parathyroid tissue: an analysis of fresh and paraffin-embedded thyroidectomy specimens.

Significance: Near-infrared autofluorescence (NIRAF) utilizes the natural autofluorescence of parathyroid glands (PGs) to improve their identification during thyroid surgeries, reducing the risk of inadvertent removal and subsequent complications such as hypoparathyroidism. This study evaluates NIRAF's effectiveness in real-world surgical settings, highlighting its potential to enhance surgical outcomes and patient safety. Aim: We evaluate the effectiveness of NIRAF in detecting PGs during thyroidectomy and central neck dissection and investigate autofluorescence characteristics in both fresh and paraffin-embedded tissues. Approach: We included 101 patients diagnosed with papillary thyroid cancer who underwent surgeries in 2022 and 2023. We assessed NIRAF's ability to locate PGs, confirmed via parathyroid hormone assays, and involved both junior and senior surgeons. We measured the accuracy, speed, and agreement levels of each method and analyzed autofluorescence persistence and variation over 10 years, alongside the expression of calcium-sensing receptor (CaSR) and vitamin D. Results: NIRAF demonstrated a sensitivity of 89.5% and a negative predictive value of 89.1%. However, its specificity and positive predictive value (PPV) were 61.2% and 62.3%, respectively, which are considered lower. The kappa statistic indicated moderate to substantial agreement (kappa = 0.478; P < 0.001 ). Senior surgeons achieved high specificity (86.2%) and PPV (85.3%), with substantial agreement (kappa = 0.847; P < 0.001 ). In contrast, junior surgeons displayed the lowest kappa statistic among the groups, indicating minimal agreement (kappa = 0.381; P < 0.001 ). Common errors in NIRAF included interference from brown fat and eschar. In addition, paraffin-embedded samples retained stable autofluorescence over 10 years, showing no significant correlation with CaSR and vitamin D levels. Conclusions: NIRAF is useful for PG identification in thyroid and neck surgeries, enhancing efficiency and reducing inadvertent PG removals. The stability of autofluorescence in paraffin samples suggests its long-term viability, with false positives providing insights for further improvements in NIRAF technology.

Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

Acute impact of inorganic nitrate supplementation after ischemia and during small muscle mass exercise in postmenopausal females: A pilot study.

Menopause is associated with reduced endothelial-dependent vasodilation and increased cardiovascular disease (CVD) risk. Dietary nitrate, a non-pharmacological approach, may increase vasodilatory capacity consequentially reducing CVD risk. We investigated macro- and microvascular function after acute nitrate supplementation in postmenopausal females (PMF). Vascular function was studied with flow-mediated vasodilation (FMD) and near-infrared post occlusive reactive hyperemia (PORH). Incremental handgrip exercise was performed to investigate blood flow and tissue oxygenation. We hypothesized acute dietary nitrate would not impact resting endothelial measures but would increase post ischemic vasodilation and incremental exercise blood flow. Late-phase PMF (n = 12) participated in a randomized crossover design with 140 mL of nitrate-rich (NR) beetroot juice or nitrate-poor black currant juice. Testing included a 5-min FMD, a 3-min ischemic exercise FMD, and incremental exercise at 10%, 15%, and 20% maximal voluntary contraction to measure blood flow and pressure responses. A p ≤ 0.05 was considered significant. One-way ANOVA indicated lower resting pressures, but no change to FMD, or PORH in either protocol. Two-way repeated measures ANOVA indicated NR supplementation significantly reduced mean arterial pressure at rest and during incremental exercise at all intensities without changes to blood flow. Acute nitrate is effective for resting and exercising blood pressure management in PMF.

Relationship of prefrontal cortex activity with anhedonia and cognitive function in major depressive disorder: an fNIRS study.

Background: Major depressive disorder (MDD) is associated with deficits in cognitive function, thought to be related to underlying decreased hedonic experiences. Further research is needed to fully elucidate the role of functional brain activity in this relationship. In this study, we investigated the neurofunctional correlate of the interplay between cognitive function and hedonic experiences in medication-free MDD using functional near-infrared spectroscopy (fNIRS). Methods: We examine differences of brain activation corresponding to the verbal fluency test (VFT) between MDD patients and healthy controls (HCs). Fifty-six MDD patients and 35 HCs underwent fMRI scanning while performing the VFT. In exploratory analyses, cognitive performance, as assessed by the Cambridge Neuropsychological Test Automated Battery (CANTAB), four dimensions of hedonic processing (desire, motivation, effort, and consummatory pleasure) measured by the Dimensional Anhedonia Rating Scale (DARS), and relative changes in oxygenated hemoglobin concentration during the VFT were compared across groups. Results: Patients with MDD demonstrated impairments in sustained attention and working memory, accompanied by lower total and subscale scores on the DARS. Compared to healthy controls, MDD patients exhibited reduced activation in the prefrontal cortex (PFC) during the VFT task (t = 2.32 to 4.77, p < 0.001 to 0.02, FDR corrected). DARS motivation, desire, and total scores as well as sustained attention, were positively correlated with activation in the dorsolateral PFC and Broca's area (p < 0.05, FDR corrected). Conclusions: These findings indicate that changes in prefrontal lobe oxygenated hemoglobin levels, a region implicated in hedonic motivation and cognitive function, may serve as potential biomarkers for interventions targeting individuals with MDD. Our results corroborate the clinical consensus that the prefrontal cortex is a primary target for non-invasive neuromodulatory treatments for depression.

Comparing different motion correction approaches for resting-state functional connectivity analysis with functional near-infrared spectroscopy data.

Significance: Motion artifacts are a notorious challenge in the functional near-infrared spectroscopy (fNIRS) field. However, little is known about how to deal with them in resting-state data. Aim: We assessed the impact of motion artifact correction approaches on assessing functional connectivity, using semi-simulated datasets with different percentages and types of motion artifact contamination. Approach: Thirty-five healthy adults underwent a 15-min resting-state acquisition. Semi-simulated datasets were generated by adding spike-like and/or baseline-shift motion artifacts to the real dataset. Fifteen pipelines, employing various correction approaches, were applied to each dataset, and the group correlation matrix was computed. Three metrics were used to test the performance of each approach. Results: When motion artifact contamination was low, various correction approaches were effective. However, with increased contamination, only a few pipelines were reliable. For datasets mostly free of baseline-shift artifacts, discarding contaminated frames after pre-processing was optimal. Conversely, when both spike and baseline-shift artifacts were present, discarding contaminated frames before pre-processing yielded the best results. Conclusions: This study emphasizes the need for customized motion correction approaches as the effectiveness varies with the specific type and amount of motion artifacts present.