Physiological Noise Research Articles

Functional engagement of white matter in resting-state brain networks

The topological characteristics of functional networks, derived from measurements of resting-state connectivity in gray matter (GM), are associated with individual cognitive abilities or specific dysfunctions. However, blood oxygen level-dependent (BOLD) signals in white matter (WM) are usually ignored or even regressed out as nuisance factors in the data analyses that underlie network models. Recent studies have demonstrated reliable detection of WM BOLD signals and imply these reflect associated neural activities. Here we evaluate quantitatively the contributions of individual WM voxels to the identification of functional networks, which we term their engagement (or conceptually, their importance). We quantify the engagement by measuring the reductions of connectivity, produced by ignoring the signal fluctuations within each WM voxel, with respect to both the entire network (global) or a single GM node (local). We observed highly reproducible spatial distributions of global engagement maps, as well as a trend toward increased relevance of deep WM voxels at delayed times. Local engagement maps exhibit homogeneous spatial distributions with respect to internal nodes that constitute a well-recognized sub-functional network, but inhomogeneous distributions with respect to other nodes. WM voxels show distinct distributions of engagement depending on their anatomical locations. These findings demonstrate the important role of WM in network modeling, thus supporting the need for changes of conventional views that WM signal variations represent only physiological noise.

Wavelet denoising as a post-processing enhancement method for non-invasive foetal electrocardiography

Background and ObjectiveThe detection of a clean and undistorted foetal electrocardiogram (fECG) from non-invasive abdominal recordings is an open research issue. Several physiological and instrumental noise sources hamper this process, even after that powerful fECG extraction algorithms have been used. Wavelet denoising is widely used for the improvement of the SNR in biomedical signal processing. This work aims to systematically assess conventional and unconventional wavelet denoising approaches for the post-processing of fECG signals by providing evidence of their effectiveness in improving fECG SNR while preserving the morphology of the signal of interest. MethodsThe stationary wavelet transform (SWT) and the stationary wavelet packet transform (SWPT) were considered, due to their different granularity in the sub-band decomposition of the signal. Three thresholds from the literature, either conventional (Minimax and Universal) and unconventional, were selected. To this aim, the unconventional one was adapted for the first time to SWPT by trying different approaches. The decomposition depth was studied in relation to the characteristics of the fECG signal. Synthetic and real datasets, publicly available for benchmarking and research, were used for quantitative analysis in terms of noise reduction, foetal QRS detection performance and preservation of fECG morphology. ResultsThe adoption of wavelet denoising approaches generally improved the SNR. Interestingly, the SWT methods outperformed the SWPT ones in morphology preservation (p<0.04) and SNR (p<0.0003), despite their coarser granularity in the sub-band analysis. Remarkably, the Han et al. threshold, adopted for the first time for fECG processing, provided the best quality improvement (p<0.003). ConclusionsThe findings of our systematic analysis suggest that particular care must be taken when selecting and using wavelet denoising for non-invasive fECG signal post-processing. In particular, despite the general noise reduction capability, signal morphology can be significantly altered on the basis of the parameterization of the wavelet methods. Remarkably, the adoption of a finer sub-band decomposition provided by the wavelet packet was not able to improve the quality of the processing.

Diffuse Optical Tomography Using Bayesian Filtering in the Human Brain

The present work describes noninvasive diffuse optical tomography (DOT), a technology for measuring hemodynamic changes in the brain. These changes provide relevant information that helps us to understand the basis of neurophysiology in the human brain. Advantages, such as portability, direct measurements of hemoglobin state, temporal resolution, and the lack of need to restrict movements, as is necessary in magnetic resonance imaging (MRI) devices, means that DOT technology can be used both in research and clinically. Here, we describe the use of Bayesian methods to filter raw DOT data as an alternative to the linear filters widely used in signal processing. Common problems, such as filter selection or a false interpretation of the results, which is sometimes caused by the interference of background physiological noise with neural activity, can be avoided with this new method.

Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising

Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.

Controlling for the effect of arterial-CO2 fluctuations in resting-state fMRI: Comparing end-tidal CO2 clamping and retroactive CO2 correction

The BOLD signal, as the basis of functional MRI, arises from both neuronal and vascular factors, with their respective contributions to resting state-fMRI still unknown. Among the factors contributing to “physiological noise”, dynamic arterial CO2 fluctuations constitutes the strongest and the most widespread modulator of the grey-matter rs-fMRI signal. Some important questions are: (1) if we were able to clamp arterial CO2 such that fluctuations are removed, what would happen to rs-fMRI measures? (2) falling short of that, is it possible to retroactively correct for CO2 effects with equivalent outcome? In this study 13 healthy subjects underwent two rs-fMRI acquisitions. During the “clamped” run, end-tidal CO2 (PETCO2) is clamped to the average PETCO2 level of each participant, while during the “free-breathing” run, the PETCO2 level is passively monitored but not controlled. PETCO2 correction was applied to the free-breathing data by convolving PETCO2 with its BOLD response function, and then regressing out the result. We computed the BOLD resting-state fluctuation amplitude (RSFA), as well as seed-independent mean functional connectivity (FC) as the weighted global brain connectivity (wGBC). Furthermore, connectivity between conditions were compared using coupled intrinsic-connectivity distribution (ICD) method. We ensured that PETCO2 clamping did not significantly alter heart-beat and respiratory variation. We found that neither PETCO2 clamping nor correction produced significant change in RSFA and wGBC. In terms of the ICD, PETCO2 clamping and correction both reduced FC strength in the majority of grey matter regions, although the effect of PETCO2 correction is considerably smaller than the effect of PETCO2 clamping. Interestingly, we found the effect of the commonly employed white-mater/cerebrospinal-fluid regression to be similar to that of PETCO2 clamping than global-signal regression. Nonetheless, both methods reduce functional connectivity significantly more than does PETCO2 clamping.Furthermore, while PETCO2 clamping reduced inter-subject variability in FC, PETCO2 correction increased the variability. Overall PETCO2 correction is not the equivalent of PETCO2 clamping, although it shifts FC values towards the same direction as clamping does.

Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer

The multi-robot system (MRS) and relevant control strategy are a potential and effective approach to assist people with weak motion capability for various forms of assisted living. However, the rising transfer, a frequent and strenuous behavior, and its human-robot interaction (HRI) process with MRS, especially mental state, has never been researched, although it directly determines the user experience and security. In this paper, Functional Near-InfraRed Spectroscopy (fNIRS), a brain imaging technique to perform a continuous measure of the mental state, is introduced to monitor the user’s mental fatigue when implementing a behavior transfer in two difficulty levels assisted by multiple welfare-robots. Twenty-five subjects performed self-rising transfer and multiple welfare robots-assisted rising transfer. After removing physiological noises, six features of oxygenated and deoxygenated hemoglobin (HbO and HbR, respectively) features, which included the mean, slope, variance, peak, skewness, and kurtosis, were calculated. To maximize the distinction of fNIRS between self-rising transfer and assisted-rising transfer (multiple welfare robots assisted rising transfer), the optimal statistical feature combination for linear discriminant analysis (LDA) classification was proposed. In addition, the classification accuracy is regarded as a standard to quantify the difference of mental states between two contrasting behaviors. By fitting the index, we established the mental fatigue model that grows exponentially as the workload increases. Finally, the mental fatigue model is applied to guide the nursing mode of caregivers and the control strategy of the MRS. Our findings disclose that the combinations containing mean and peak values significantly yielded higher classification accuracies for both HbO and HbR than the entire other combinations did, across all the subjects. They effectively quantify mental fatigue to provide an evaluation with a theoretical foundation for enhancing the user experience and optimizing the control strategy of MRS.

Improved velocity-selective labeling pulses for myocardial ASL.

To develop and evaluate an improved velocity-selective (VS) labeling pulse for myocardial arterial spin labeling (ASL) perfusion imaging that addresses two limitations of current pulses: (1) spurious labeling of moving myocardium and (2) low labeling efficiency. The proposed myocardial VSASL labeling pulse is designed using a Fourier Transform based Velocity-Selective labeling pulse train. The pulse utilizes bipolar velocity-encoding gradients, a 9-tap velocity-encoding envelope, and double-refocusing pulses with Malcolm Levitt phase cycling. Amplitudes of the velocity-encoding envelope were optimized to minimize the labeling of myocardial velocities during stable diastole (±2-3 cm/s) and maximize the labeling of coronary velocities (10-130 cm/s during rest/stress or 10-70 cm/s during rest). Myocardial ASL experiments were performed in seven healthy subjects using the previously developed VS-ASL protocol by Jao et al with the two proposed VS pulses and original VS pulse. Myocardial ASL experiments were also performed using FAIR ASL. Myocardial perfusion and physiological noise (PN) were evaluated and compared. Bloch simulations of the first and second proposed pulses show <2% labeling over ±3 cm/s and ±2 cm/s, respectively. Bloch simulations also show the mean labeling efficiency of arterial blood is 1.23 over the relevant coronary arterial ranges. In-vivo VSASL experiments show the proposed pulses provided comparable measurements to FAIR ASL and reduced TSNR in 5 of 7 subjects compared to the original VS pulse. We demonstrate an improved VS labeling pulse specifically for myocardial ASL perfusion imaging to reduce spurious labeling of moving myocardium and PN.

Disambiguating the role of blood flow and global signal with partial information decomposition

Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas.

Impact of physiological noise in characterizing the functional MRI default-mode network in Alzheimer’s disease

The functional connectivity of the default-mode network (DMN) monitored by functional magnetic resonance imaging (fMRI) in Alzheimer's disease (AD) patients has been found weaker than that in healthy participants. Since breathing and heart beating can cause fluctuations in the fMRI signal, these physiological activities may affect the fMRI data differently between AD patients and healthy participants. We collected resting-state fMRI data from AD patients and age-matched healthy participants. With concurrent cardiac and respiratory recordings, we estimated both physiological responses phase-locked and non-phase-locked to heart beating and breathing. We found that the cardiac and respiratory physiological responses in AD patients were 3.00 ± 0.51 s and 3.96 ± 0.52 s later (both p < 0.0001) than those in healthy participants, respectively. After correcting the physiological noise in the resting-state fMRI data by population-specific physiological response functions, the DMN estimated by seed-correlation was more localized to the seed region. The DMN difference between AD patients and healthy controls became insignificant after suppressing physiological noise. Our results indicate the importance of controlling physiological noise in the resting-state fMRI analysis to obtain clinically related characterizations in AD.

Scale-resolved analysis of brain functional connectivity networks with spectral entropy

Functional connectivity is derived from inter-regional correlations in spontaneous fluctuations of brain activity, and can be represented in terms of complete graphs with continuous (real-valued) edges. The structure of functional connectivity networks is strongly affected by signal processing procedures to remove the effects of motion, physiological noise and other sources of experimental error. However, in the absence of an established ground truth, it is difficult to determine the optimal procedure, and no consensus has been reached on the most effective approach to remove nuisance signals without unduly affecting the network intrinsic structural features. Here, we use a novel information-theoretic approach, based on von Neumann entropy, which provides a measure of information encoded in the networks at different scales. We also define a measure of distance between networks, based on information divergence, and optimal null models appropriate for the description of functional connectivity networks, to test for the presence of nontrivial structural patterns that are not the result of simple local constraints. This formalism enables a scale-resolved analysis of the distance between a functional connectivity network and its maximally random counterpart, thus providing a means to assess the effects of noise and image processing on network structure. We apply this novel approach to address a few open questions in the analysis of brain functional connectivity networks. Specifically, we demonstrate a strongly beneficial effect of network sparsification by removal of the weakest links, and the existence of an optimal threshold that maximizes the ability to extract information on large-scale network structures. Additionally, we investigate the effects of different degrees of motion at different scales, and compare the most popular processing pipelines designed to mitigate its deleterious effect on functional connectivity networks. We show that network sparsification, in combination with motion correction algorithms, dramatically improves detection of large scale network structure.

Observations on the correlation of subjective and objective attenuation measurements on earplugs.

Real-ear attenuation at threshold (REAT) has been recognized as the gold-standard method to evaluate the attenuation of hearing protection devices (HPDs). The question is often posed as to whether or not REAT measurements could be replaced with objective, insertion loss (IL) measurements conducted on acoustical test fixtures for assessing the performance and consistency of HPDs. To understand how IL data correspond to REAT data for earplugs, both methods were used to assess three earplugs. The resulting data were compared, and the HPD attenuation ranked at each frequency. In most cases, after correction for bone conduction limits, occlusion effect, and physiological noise, IL data show good agreement with REAT values. Discrepancies at some frequencies were found and varied by HPD. The IL data did not identify the high standard deviation of the REAT results on one earplug. The IL data did not correspond well to the REAT values, resulting in differences as large as 12 dB at some frequencies. In conclusion, IL and REAT procedures are used for different purposes when assessing attenuation characteristics of HPDs. While close agreement was found at most frequencies, IL data may not reflect the REAT performance of HPDs equivalently in all aspects.

Blind Source Separation for Myelin Water Fraction Mapping Using Multi-Echo Gradient Echo Imaging.

In conventional gradient-echo myelin water imaging (GRE-MWI), myelin water fraction (MWF) is estimated by fitting the multi-echo gradient recalled echo (mGRE) signal to a pre-assumed numerical model (e.g., multi-component exponential curves or three component exponential curves). However, in mGRE, imaging artifacts (e.g., voxel spread function and physiological noise) and noise render the signal to deviate from the numerical model, leading to misfit of the model parameters. Here, as an alternative to the model-based GRE-MWI, a blind source separation (BSS) technique for the separation of multi-exponential mGRE signal is proposed. Among the various BSS techniques, a modified robust principal component analysis (rPCA) is presented to separate signal sources by enforcing the data-driven properties such as "low rankness" and "sparsity." Considering the signal evolution of T2∗ relaxation (i.e., non-negative exponential decay), low rankness of exponential decay was enforced by nonnegative matrix factorization (NMF) and hankelization. This method provides the separation of slow-decaying, fast-decaying exponential components and artifact components from mGRE images. After the separation, MWF map is reconstructed as the ratio of the fast-decaying component to the total decaying components. The proposed method was demonstrated in numerical simulations and in vivo scans. The method provided a robust estimation of MWF in the presence of statistical noise and imaging artifacts.

The Association Between Physiological Noise Levels and Speech Understanding in Noise.

Traditionally, elevated hearing thresholds have been considered to be the main contributors to difficulty understanding speech in noise; yet, patients will often report difficulties with speech understanding in noise despite having audiometrically normal hearing. The purpose of this cross-sectional study was to critically evaluate the relationship of various metrics of auditory function (behavioral thresholds and otoacoustic emissions) on speech understanding in noise in a large sample of audiometrically normal-hearing individuals. Behavioral hearing thresholds, distortion product otoacoustic emission (DPOAE) levels, stimulus-frequency otoacoustic emission levels, and physiological noise (quantified using OAE noise floors) were measured from 921 individuals between 10 and 68 years of age with normal pure-tone averages. The quick speech-in-noise (QuickSIN) test outcome, quantified as the signal-to-noise ratio (SNR) loss, was used as the metric of speech understanding in noise. Principle component analysis (PCA) and linear regression modeling were used to evaluate the relationship between the measures of auditory function and speech in noise performance. Over 25% of participants exhibited mild or worse degree of SNR loss. PCA revealed DPOAE levels at 12.5 to 16 kHz to be significantly correlated with the variation in QuickSIN scores, although correlations were weak (R = 0.017). Out of all the metrics evaluated, higher levels of self-generated physiological noise accounted for the most variance in QuickSIN performance (R = 0.077). Higher levels of physiological noise were associated with worse QuickSIN performance in listeners with normal hearing sensitivity. We propose that elevated physiological noise levels in poorer speech in noise performers could diminish the effective SNR, thereby negatively impacting performance as seen by poorer QuickSIN scores.

Controlling for lesions, kinematics and physiological noise: impact on fMRI results of spastic post-stroke patients

Functional magnetic resonance imaging (fMRI) is a widely used technique for assessing brain function in both healthy and pathological populations. Some factors, such as motion, physiological noise and lesion presence, can contribute to signal change and confound the fMRI data, but fMRI data processing techniques have been developed to correct for these confounding effects. Fifteen spastic subacute stroke patients underwent fMRI while performing a highly controlled task (i.e. passive extension of their affected and unaffected wrists). We investigated the impact on activation maps of lesion masking during preprocessing and first- and second-level analyses, and of adding wrist extension amplitudes and physiological data as regressors using the Statistical Parametric Mapping toolbox (SPM12).We observed a significant decrease in sensorimotor region activation after the addition of lesion masks and movement/physiological regressors during the processing of stroke patients’ fMRI data. Our results demonstrate that:•The unified segmentation routine results in good normalization accuracy when dealing with stroke lesions regardless of their size;•Adding a group lesion mask during the second-level analysis seems to be a suitable option when none of the patients have lesions in target regions. Otherwise, no masking is acceptable;•Movement amplitude is a significant contributor to the sensorimotor activation observed during passive wrist extension in spastic stroke patients;•Movement features and physiological noise are relevant factors when interpreting for sensorimotor activation in studies of the motor system in patients with brain lesions. They can be added as nuisance covariates during large patient groups’ analyses.

A 3D Convolutional Encapsulated Long Short-Term Memory (3DConv-LSTM) Model for Denoising fMRI Data.

Function magnetic resonance imaging (fMRI) data are typically contaminated by noise introduced by head motion, physiological noise, and thermal noise. To mitigate noise artifact in fMRI data, a variety of denoising methods have been developed by removing noise factors derived from the whole time series of fMRI data and therefore are not applicable to real-time fMRI data analysis. In the present study, we develop a generally applicable, deep learning based fMRI denoising method to generate noise-free realistic individual fMRI volumes (time points). Particularly, we develop a fully data-driven 3D convolutional encapsulated Long Short-Term Memory (3DConv-LSTM) approach to generate noise-free fMRI volumes regularized by an adversarial network that makes the generated fMRI volumes more realistic by fooling a critic network. The 3DConv-LSTM model also integrates a gate-controlled self-attention model to memorize short-term dependency and historical information within a memory pool. We have evaluated our method based on both task and resting state fMRI data. Both qualitative and quantitative results have demonstrated that the proposed method outperformed state-of-the-art alternative deep learning methods.

Effect of Physiological Noise on Thoracolumbar Spinal Cord Functional Magnetic Resonance Imaging in 3T Magnetic Field.

Introduction:Functional Magnetic Resonance Imaging (fMRI) methods have been used to study sensorimotor processing in the spinal cord. However, these techniques confront unwanted noises to the measured signal from the physiological fluctuations. In the spinal cord imaging, most of the challenges are consequences of cardiac and respiratory movement artifacts that are considered as significant sources of noise, especially in the thoracolumbar region. In this study, we investigated the effect of each source of physiological noise and their contribution to the outcome of the analysis of the blood-oxygen-level-dependent signal in the human thoracolumbar spinal cord.Methods:Fifteen young healthy male volunteers participated in the study, and pain stimuli were delivered on the L5 dermatome between the two malleoli. Respiratory and cardiac signals were recorded during the imaging session, and the generated respiration and cardiac regressors were included in the general linear model for quantification of the effect of each of them on the task-analysis results. The sum of active voxels of the clusters was calculated in the spinal cord in three correction states (respiration correction only, cardiac correction only, and respiration and cardiac noise corrections) and analyzed with analysis of variance statistical test and receiver operating characteristic curve.Results:The results illustrated that cardiac noise correction had an effective role in increasing the active voxels (Mean±SD = 23.46±9.46) compared to other noise correction methods. Cardiac effects were higher than other physiological noise sourcesConclusion:In summary, our results indicate great respiration effects on the lumbar and thoracolumbar spinal cord fMRI, and its contribution to the heartbeat effect can be a significant variable in the individual fMRI data analysis. Displacement of the spinal cord and the effects of this noise in the thoracolumbar and lumbar spinal cord fMRI results are significant and cannot be ignored.

Compact, Portable, High-Density Functional Near-Infrared Spectroscopy System for Brain Imaging

Advancements in functional near-infrared spectroscopy (fNIRS) neuroimaging have extended its usage from clinical settings to real-life applications. We developed a wearable and wireless fNIRS-based neuroimaging system that can be applied for brain disease diagnostics and during daily life activities. We utilized 128 compact dual-wavelength LEDs of 735 nm and 850 nm and a silicon photodiode (SiPD). The LEDs and SiPD were closely assembled in a 3D-printed flexible configuration holder fitted in a soft neoprene cap. The developed configuration has 128 channels in a $7\times 7$ cm area with source-detector separations ranging from 5 mm to 38 mm, which intends to provide a high-density device. The system acquires data from multiple brain depths to generate a 3D activation image. Short-separation channels provide the physiological noise in the superficial layer, which can be used to enhance fNIRS signals. The experimental data are stored in a USB flash disk attached to the device. We developed software that is connected to the device using Wi-Fi to display the oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) levels in real-time. The system architecture allows the selection of channels and the use of multiple devices simultaneously. The system was tested using a silicone phantom as well as human subjects. The LED intensity is dynamically calibrated within the safety range once the device is set up, so that every channel can acquire stable data. The system provided high-quality signals with a signal-to-noise ratio of more than 64 dB, high optical sensitivity, and dynamic optical range of 140 dB in the phantom test. A sampling rate of 30 Hz was achieved with all the channels used. The system exhibited valid HbO and HbR responses during the arterial occlusion test and a motor task. These results show that the developed system can provide precise, robust, and multi-depth measurements resulting in high-density images. With these novel characteristics, the system favors clinical usage and outdoor activities as well as brain-computer interface applications.

Benefits of Low-Dose CT Scan of Head for Patients With Intracranial Hemorrhage.

Objectives:For patients with intracranial hemorrhage (ICH), routine follow-up computed tomography (CT) scans are typically required to monitor the progression of intracranial pathology. Remarkable levels of radiation exposure are accumulated during repeated CT scan. However, the effects and associated risks have still remained elusive. This study presented an effective approach to quantify organ-specific radiation dose of repeated CT scans of head for patients with ICH. We also indicated whether a low-dose CT scan may reduce radiation exposure and keep the image quality highly acceptable for diagnosis.Methods:Herein, 72 patients with a history of ICH were recruited. The patients were divided into 4 groups and underwent CT scan of head with different tube current–time products (250, 200, 150, and 100 mAs). Two experienced radiologists visually rated scores of quality of images according to objective image noise, sharpness, diagnostic acceptability, and artifacts due to physiological noise on the same workstation. Organ-/tissue-specific radiation doses were analyzed using Radimetrics.Results:In conventional CT scan group, signal to noise ratio (SNR) and contrast to noise ratio (CNR) of ICH images were significantly higher than those in normal brain structures. Reducing the tube current–time product may decrease the image quality. However, the predilection sites for ICH could be clearly identified. The SNR and CNR in the predilection sites for ICH were notably higher than other areas. The brain, eye lenses, and salivary glands received the highest radiation dose. Reducing tube current–time product from 250 to 100 mA can significantly reduce the radiation dose.Discussion:We demonstrated that low-dose CT scan of head can still provide reasonable images for diagnosing ICH. The radiation dose can be reduced to ∼45% of the conventional CT scan group.

Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis

For the robust estimation of evoked brain activity from functional Near-Infrared Spectroscopy (fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion. The current best practice incorporates short-separation (SS) fNIRS measurements as regressors in a General Linear Model (GLM). However, several challenging signal characteristics such as non-instantaneous and non-constant coupling are not yet addressed by this approach and additional auxiliary signals are not optimally exploited. We have recently introduced a new methodological framework for the unsupervised multivariate analysis of fNIRS signals using Blind Source Separation (BSS) methods. Building onto the framework, in this manuscript we show how to incorporate the advantages of regularized temporally embedded Canonical Correlation Analysis (tCCA) into the supervised GLM. This approach allows flexible integration of any number of auxiliary modalities and signals. We provide guidance for the selection of optimal parameters and auxiliary signals for the proposed GLM extension. Its performance in the recovery of evoked HRFs is then evaluated using both simulated ground truth data and real experimental data and compared with the GLM with short-separation regression. Our results show that the GLM with tCCA significantly improves upon the current best practice, yielding significantly better results across all applied metrics: Correlation (HbO max. +45%), Root Mean Squared Error (HbO max. −55%), F-Score (HbO up to 3.25-fold) and p-value as well as power spectral density of the noise floor. The proposed method can be incorporated into the GLM in an easily applicable way that flexibly combines any available auxiliary signals into optimal nuisance regressors. This work has potential significance both for conventional neuroscientific fNIRS experiments as well as for emerging applications of fNIRS in everyday environments, medicine and BCI, where high Contrast to Noise Ratio is of importance for single trial analysis.

Comment to: "Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates".

Comment to: "Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates".