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Motion artifacts are a significant source of noise in many functional near-infrared spectroscopy (fNIRS) experiments. Despite this, there is no well-established method for their removal. Instead, functional trials of fNIRS data containing a motion artifact are often rejected completely. However, in most experimental circumstances the number of trials is limited, and multiple motion artifacts are common, particularly in challenging populations. Many methods have been proposed recently to correct for motion artifacts, including principle component analysis, spline interpolation, Kalman filtering, wavelet filtering and correlation-based signal improvement. The performance of different techniques has been often compared in simulations, but only rarely has it been assessed on real functional data. Here, we compare the performance of these motion correction techniques on real functional data acquired during a cognitive task, which required the participant to speak aloud, leading to a low-frequency, low-amplitude motion artifact that is correlated with the hemodynamic response. To compare the efficacy of these methods, objective metrics related to the physiology of the hemodynamic response have been derived. Our results show that it is always better to correct for motion artifacts than reject trials, and that wavelet filtering is the most effective approach to correcting this type of artifact, reducing the area under the curve where the artifact is present in 93% of the cases. Our results therefore support previous studies that have shown wavelet filtering to be the most promising and powerful technique for the correction of motion artifacts in fNIRS data. The analyses performed here can serve as a guide for others to objectively test the impact of different motion correction algorithms and therefore select the most appropriate for the analysis of their own fNIRS experiment. •A comparison of motion artifact correction techniques on real data is performed.•Motion artifact correction is a crucial step in the fNIRS signal processing stream.•Wavelet filtering is a powerful tool for motion artifact correction.

We assess the causal impact of social distancing on the spread of SARS-CoV-2 in the U.S. using the quasi-natural experimental setting created by the spontaneous relaxation of social distancing behavior brought on by the protests that erupted across the nation following George Floyd’s tragic death on May 25, 2020. Using a difference-in-difference specification and a balanced sample covering the [− 30, 30] day event window centered on the onset of protests, we document an increase of 1.34 cases per day, per 100,000 population, in the SARS-CoV-2 incidence rate in protest counties, relative to their propensity score matching non-protest counterparts. This represents a 26.8% increase in the incidence rate relative to the week preceding the protests. We find that the treatment effect only manifests itself after the onset of the protests and our placebo tests rule out the possibility that our findings are attributable to chance. Our research informs policy makers and provides insights regarding the usefulness of social distancing as an intervention to minimize the spread of SARS-CoV-2.

Near-Infrared Spectroscopy (NIRS) allows the recovery of the evoked hemodynamic response to brain activation. In adult human populations, the NIRS signal is strongly contaminated by systemic interference occurring in the superficial layers of the head. An approach to overcome this difficulty is to use additional NIRS measurements with short optode separations to measure the systemic hemodynamic fluctuations occurring in the superficial layers. These measurements can then be used as regressors in the post-experiment analysis to remove the systemic contamination and isolate the brain signal. In our previous work, we showed that the systemic interference measured in NIRS is heterogeneous across the surface of the scalp. As a consequence, the short separation measurement used in the regression procedure must be located close to the standard NIRS channel from which the evoked hemodynamic response of the brain is to be recovered. Here, we demonstrate that using two short separation measurements, one at the source optode and one at the detector optode, further increases the performance of the short separation regression method compared to using a single short separation measurement. While a single short separation channel produces an average reduction in noise of 33% for HbO, using a short separation channel at both source and detector reduces noise by 59% compared to the standard method using a general linear model (GLM) without short separation. For HbR, noise reduction of 3% is achieved using a single short separation and this number goes to 47% when two short separations are used. Our work emphasizes the importance of integrating short separation measurements both at the source and at the detector optode of the standard channels from which the hemodynamic response is to be recovered. While the implementation of short separation sources presents some difficulties experimentally, the improvement in noise reduction is significant enough to justify the practical challenges. ► Two short separation channels is better than a single short separation. ► Reductions of 59% in noise and 72% in inter-trial variability achieved for HbO ► Reduction of 47% in noise level and 76% in inter-trial variability for HbR ► Improvements are significant enough to justify the practical challenges.

Diffuse optical imaging (DOI) allows the recovery of the hemodynamic response associated with evoked brain activity. The signal is contaminated with systemic physiological interference which occurs in the superficial layers of the head as well as in the brain tissue. The back-reflection geometry of the measurement makes the DOI signal strongly contaminated by systemic interference occurring in the superficial layers. A recent development has been the use of signals from small source-detector separation (1cm) optodes as regressors. Since those additional measurements are mainly sensitive to superficial layers in adult humans, they help in removing the systemic interference present in longer separation measurements (3cm). Encouraged by those findings, we developed a dynamic estimation procedure to remove global interference using small optode separations and to estimate simultaneously the hemodynamic response. The algorithm was tested by recovering a simulated synthetic hemodynamic response added over baseline DOI data acquired from 6 human subjects at rest. The performance of the algorithm was quantified by the Pearson R2 coefficient and the mean square error (MSE) between the recovered and the simulated hemodynamic responses. Our dynamic estimator was also compared with a static estimator and the traditional adaptive filtering method. We observed a significant improvement (two-tailed paired t-test, p<0.05) in both HbO and HbR recovery using our Kalman filter dynamic estimator compared to the traditional adaptive filter, the static estimator and the standard GLM technique. ► Small optode separations measurements help remove systemic interference in NIRS data. ► Simultaneous filtering and estimation allows better recovery of the HRF. ► Dynamic filtering take into account the non-stationary behavior of the interference. ► Works well even if the baseline short-long correlation is as low as 0.1.

Near-Infrared Spectroscopy (NIRS) allows the recovery of cortical oxy- and deoxyhemoglobin changes associated with evoked brain activity. NIRS is a back-reflection measurement making it very sensitive to the superficial layers of the head, i.e. the skin and the skull, where systemic interference occurs. As a result, the NIRS signal is strongly contaminated with systemic interference of superficial origin. A recent approach to overcome this problem has been the use of additional short source-detector separation optodes as regressors. Since these additional measurements are mainly sensitive to superficial layers in adult humans, they can be used to remove the systemic interference present in longer separation measurements, improving the recovery of the cortical hemodynamic response function (HRF). One question that remains to answer is whether or not a short separation measurement is required in close proximity to each long separation NIRS channel. Here, we show that the systemic interference occurring in the superficial layers of the human head is inhomogeneous across the surface of the scalp. As a result, the improvement obtained by using a short separation optode decreases as the relative distance between the short and the long measurement is increased. NIRS data was acquired on 6 human subjects both at rest and during a motor task consisting of finger tapping. The effect of distance between the short and the long channel was first quantified by recovering a synthetic hemodynamic response added over the resting-state data. The effect was also observed in the functional data collected during the finger tapping task. Together, these results suggest that the short separation measurement must be located as close as 1.5cm from the standard NIRS channel in order to provide an improvement which is of practical use. In this case, the improvement in Contrast-to-Noise Ratio (CNR) compared to a standard General Linear Model (GLM) procedure without using any small separation optode reached 50% for HbO and 100% for HbR. Using small separations located farther than 2cm away resulted in mild or negligible improvements only. ► Short separation channel location impacts the performance of the method. ► Systemic physiology is inhomogeneous across the scalp. ► CNR improvement of 50% (HbO) and 100% (HbR) if regressor is located within 1.5cm. ► Only mild improvement if short channel is located farther than 1.5cm.

Brain imaging studies have reported age-related differences in brain activation for attentional control functions, such as inhibition and task-switching. However, age-related differences in brain activation patterns in more than one attentional control task have rarely been studied in the same group of participants. In this study, younger and older adults completed a modified Stroop task with interference and switching conditions, using functional near infra-red spectroscopy. While interference did not reveal any significant activation of the prefrontal cortex in younger adults, switching produced an increased activation bilaterally in both the anterior dorsolateral prefrontal cortex (DLPFC) and the anterior ventrolateral prefrontal cortex (VLPFC). In older adults, an isolated right and left anterior DLPFC activation was observed even in the non-executive conditions of the Stroop task (color denomination) and the interference condition revealed activation mostly in the posterior left DLPFC and bilateral VLPFC with a small right anterior DLPFC component. Specific to older adults, switching induced an increased activation spread out bilaterally over the prefrontal cortex in the bilateral anterior DLPFC, the posterior left DLPFC and bilateral VLPFC. These results suggest that for both older and younger adults, inhibition and switching are associated with distinct patterns of prefrontal activation and that age-related differences exist in these patterns such that prefrontal activation seems to be more spread out at different sites in older adults. ► Frontal activation for inhibition and task-switching was compared in a single task. ► Younger and older adults were compared. ► Distinct patterns of frontal activation during inhibition and switching were found. ► There is functional dissociation between the two executive functions. ► Age-related differences emerged with spread out activation in older adults.

Near-infrared spectroscopy (NIRS) is susceptible to signal artifacts caused by relative motion between NIRS optical fibers and the scalp. These artifacts can be very damaging to the utility of functional NIRS, particularly in challenging subject groups where motion can be unavoidable. A number of approaches to the removal of motion artifacts from NIRS data have been suggested. In this paper we systematically compare the utility of a variety of published NIRS motion correction techniques using a simulated functional activation signal added to 20 real NIRS datasets which contain motion artifacts. Principle component analysis, spline interpolation, wavelet analysis, and Kalman filtering approaches are compared to one another and to standard approaches using the accuracy of the recovered, simulated hemodynamic response function (HRF). Each of the four motion correction techniques we tested yields a significant reduction in the mean-squared error (MSE) and significant increase in the contrast-to-noise ratio (CNR) of the recovered HRF when compared to no correction and compared to a process of rejecting motion-contaminated trials. Spline interpolation produces the largest average reduction in MSE (55%) while wavelet analysis produces the highest average increase in CNR (39%). On the basis of this analysis, we recommend the routine application of motion correction techniques (particularly spline interpolation or wavelet analysis) to minimize the impact of motion artifacts on functional NIRS data.

ATLAS track reconstruction software is continuously evolving to match the demands from the increasing instantaneous luminosity of the LHC, as well as the increased center-of-mass energy. These conditions result in a higher abundance of events with dense track environments, such as the core of jets or boosted tau leptons undergoing three-prong decays. These environments are characterised by charged particle separations on the order of the ATLAS inner detector sensor dimensions and are created by the decay of boosted objects. Significant upgrades were made to the track reconstruction software to cope with the expected conditions during LHC Run 2. In particular, new algorithms targeting dense environments were developed. These changes lead to a substantial reduction of reconstruction time while at the same time improving physics performance. The employed methods are presented and physics performance studies are shown, including a measurement of the fraction of lost tracks in jets with high transverse momentum.

Near-Infrared Spectroscopy (NIRS) measures the functional hemodynamic response occurring at the surface of the cortex. Large pial veins are located above the surface of the cerebral cortex. Following activation, these veins exhibit oxygenation changes but their volume likely stays constant. The back-reflection geometry of the NIRS measurement renders the signal very sensitive to these superficial pial veins. As such, the measured NIRS signal contains contributions from both the cortical region as well as the pial vasculature. In this work, the cortical contribution to the NIRS signal was investigated using (1) Monte Carlo simulations over a realistic geometry constructed from anatomical and vascular MRI and (2) multimodal NIRS-BOLD recordings during motor stimulation. A good agreement was found between the simulations and the modeling analysis of in vivo measurements. Our results suggest that the cortical contribution to the deoxyhemoglobin signal change (ΔHbR) is equal to 16–22% of the cortical contribution to the total hemoglobin signal change (ΔHbT). Similarly, the cortical contribution of the oxyhemoglobin signal change (ΔHbO) is equal to 73–79% of the cortical contribution to the ΔHbT signal. These results suggest that ΔHbT is far less sensitive to pial vein contamination and therefore, it is likely that the ΔHbT signal provides better spatial specificity and should be used instead of ΔHbO or ΔHbR to map cerebral activity with NIRS. While different stimuli will result in different pial vein contributions, our finger tapping results do reveal the importance of considering the pial contribution. ► Pial vasculature contaminates the NIRS signal. ► Concurrent NIRS-fMRI recordings enables estimation of the cortical signal contribution. ► 20% of the HbR signal and 75% of the HbO signal has cortical origins (finger tapping). ► HbT should be used rather than HbO or HbR to map cerebral activity with NIRS.

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model.