Physiological Noise Research Articles

Assessment of disrupted brain functional connectome in tuberous sclerosis complex using resting-state fMRI.

Tuberous sclerosis complex (TSC) is a rare genetic disorder with multisystem involvement. TSC is characterized by benign hamartomas in multiple organs, including the brain, and its clinical phenotypes may be associated with abnormal functional connections. We aimed to use resting-state functional connectivity to provide findings of disrupted functional brain networks in TSC patients using graph theoretical analysis (GTA) and network-based statistic (NBS) analysis.Forty TSC patients (age = 24.11+/-11.44 years old) and 18 age-matched (25.13+/- 10.01 years old) healthy controls were recruited; they underwent resting-state functional magnetic resonance imaging using a 3T magnetic resonance imaging scanner. After image preprocessing and removing physiological noises, GTA was used to calculate the topological parameters of the brain network. NBS analysis was then used to determine the differences in cerebrum functional connectivity between the 2 groups.In GTA, several topological parameters, including the clustering coefficient, local efficiency, transitivity, and modularity, were better in controls than in TSC patients (P < .05). In NBS analysis, the edges of the brain networks between the groups were compared. One subnetwork showed more edges in controls than in TSC patients (P < .05), including the connections from the frontal lobe to the temporal and parietal lobe.The study results provide the findings on disrupted functional connectivity and organization in TSC patients compared with controls. The findings may help better understand the underlying physiological mechanisms of brain connection in TSC.

Real-time and Recursive Estimators for Functional MRI Quality Assessment.

Real-time quality assessment (rtQA) of functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) signal changes is critical for neuroimaging research and clinical applications. The losses of BOLD sensitivity because of different types of technical and physiological noise remain major sources of fMRI artifacts. Due to difficulty of subjective visual perception of image distortions during data acquisitions, a comprehensive automatic rtQA is needed. To facilitate rapid rtQA of fMRI data, we applied real-time and recursive quality assessment methods to whole-brain fMRI volumes, as well as time-series of target brain areas and resting-state networks. We estimated recursive temporal signal-to-noise ratio (rtSNR) and contrast-to-noise ratio (rtCNR), and real-time head motion parameters by a framewise rigid-body transformation (translations and rotations) using the conventional current to template volume registration. In addition, we derived real-time framewise (FD) and micro (MD) displacements based on head motion parameters and evaluated the temporal derivative of root mean squared variance over voxels (DVARS). For monitoring time-series of target regions and networks, we estimated the number of spikes and amount of filtered noise by means of a modified Kalman filter. Finally, we applied the incremental general linear modeling (GLM) to evaluate real-time contributions of nuisance regressors (linear trend and head motion). Proposed rtQA was demonstrated in real-time fMRI neurofeedback runs without and with excessive head motion and real-time simulations of neurofeedback and resting-state fMRI data. The rtQA was implemented as an extension of the open-source OpenNFT software written in Python, MATLAB and C++ for neurofeedback, task-based, and resting-state paradigms. We also developed a general Python library to unify real-time fMRI data processing and neurofeedback applications. Flexible estimation and visualization of rtQA facilitates efficient rtQA of fMRI data and helps the robustness of fMRI acquisitions by means of substantiating decisions about the necessity of the interruption and re-start of the experiment and increasing the confidence in neural estimates.

Effects of Systemic Physiology on Mapping Resting-State Networks Using Functional Near-Infrared Spectroscopy.

Resting-state functional connectivity (rsFC) has gained popularity mainly due to its simplicity and potential for providing insights into various brain disorders. In this vein, functional near-infrared spectroscopy (fNIRS) is an attractive choice due to its portability, flexibility, and low cost, allowing for bedside imaging of brain function. While promising, fNIRS suffers from non-neural signal contaminations (i.e., systemic physiological noise), which can increase correlation across fNIRS channels, leading to spurious rsFC networks. In the present work, we hypothesized that additional measurements with short channels, heart rate, mean arterial pressure, and end-tidal CO2 could provide a better understanding of the effects of systemic physiology on fNIRS-based resting-state networks. To test our hypothesis, we acquired 12 min of resting-state data from 10 healthy participants. Unlike previous studies, we investigated the efficacy of different pre-processing approaches in extracting resting-state networks. Our results are in agreement with previous studies and reinforce the fact that systemic physiology can overestimate rsFC. We expanded on previous work by showing that removal of systemic physiology decreases intra- and inter-subject variability, increasing the ability to detect neural changes in rsFC across groups and over longitudinal studies. Our results show that by removing systemic physiology, fNIRS can reproduce resting-state networks often reported with functional magnetic resonance imaging (fMRI). Finally, the present work details the effects of systemic physiology and outlines how to remove (or at least ameliorate) their contributions to fNIRS signals acquired at rest.

The temporal dedifferentiation of global brain signal fluctuations during human brain ageing

The variation of brain functions as healthy ageing has been discussed widely using resting-state brain imaging. Previous conclusions may be misinterpreted without considering the effects of global signal (GS) on local brain activities. Up to now, the variation of GS with ageing has not been estimated. To fill this gap, we defined the GS as the mean signal of all voxels in the gray matter and systematically investigated correlations between age and indices of GS fluctuations. What’s more, these tests were replicated with data after hemodynamic response function (HRF) de-convolution and data without noise regression as well as head motion data to verify effects of non-neural information on age. The results indicated that GS fluctuations varied as ageing in three ways. First, GS fluctuations were reduced with age. Second, the GS power transferred from lower frequencies to higher frequencies with age. Third, the GS power was more evenly distributed across frequencies in ageing brain. These trends were partly influenced by HRF and physiological noise, indicating that the age effects of GS fluctuations are associated with a variety of physiological activities. These results may indicate the temporal dedifferentiation hypothesis of brain ageing from the global perspective.

Thermal Analysis of Wearable OPM-MEG Array System for Auditory Evoked Experiments

High temperature on the surface of the OPM-MEG array can introduce physiological noise in MEG measurement and over temperature will result in scald to the skin of the epicranium. In this paper, an experimentally verified temperature field model of the OPM-MEG array is developed. The main heat sources and heat transmission of the system are analyzed, and temperature field distribution is obtained in detail by using the finite element method (FEM). A more efficient thermal management scheme was proposed which used a water-cooling design to control the sensor array temperature and verify better signal to noise ratio in auditory evoked experiments. The temperature at the bottom of sensor array can be reduced by <inline-formula> <tex-math notation="LaTeX">$17.8^\circ \text{C}$ </tex-math></inline-formula> roughly with water temperature of <inline-formula> <tex-math notation="LaTeX">$15^\circ \text{C}$ </tex-math></inline-formula> which falls within the scope of an acceptable sensible temperature. In auditory evoked experiment, the Euclidean distance between cortex where the N1m component is located and MEG signal positioning result is calculated. The location error is reduced by <inline-formula> <tex-math notation="LaTeX">$9.5~\%$ </tex-math></inline-formula>(left) and <inline-formula> <tex-math notation="LaTeX">$66.9~\%$ </tex-math></inline-formula>(right) due to the temperature control by using water cooling system. The method in this paper provides a theoretical basis for the future multi-channel MEG system design in thermal management.

P035 An examination of resting state functional connectivity in patients with active Crohn’s disease

Abstract Background Crohn’s disease (CD) and its comorbidities may be associated with a specific intrinsic neural phenotype that can be measured using resting state functional connectivity (RSFC). RSFC alterations have been reported in CD in remission but have not been investigated in the active phase of the disease. Here, the primary aim was to study the effects of active CD on resting state networks (RSNs), hence identifying RSFC alterations specific to active CD status. A second aim was to investigate associations between disease duration and abdominal pain with RSFC in active CD. Methods 25 active CD patients and 25 age-, BMI- and gender-matched healthy controls (HC) were recruited. Active disease was defined as CRP &amp;gt; 5 mg/dl, or faecal calprotectin (FCP) &amp;gt;250 μg/g or through ileocolonoscopy or MRE. RsfMRI datasets were acquired on a 3T Philips Achieva scanner, with data corrected for physiological noise and motion. ICA analysis was carried out using MELODIC (FSL software). A multi-session temporal concatenation was used to generate 30 independent component (IC) maps of RSNs. A dual regression analysis with variance normalization, using group IC maps from HC group as the network template, was performed. Differences in RSN between HCs and CD patients and associations with covariates of interest were assessed. Results RSNs comprising the visual, cerebellar, frontal parietal and frontal RSN, default mode network (DMN), salience, temporal and dorsal attention RSN were identified. Enhancement of activity and increase connectivity in the medial visual RSN (orbital frontal cortex), in the frontal parietal network (occipital fusiform gyrus, thalamus, caudate, posterior cingulate cortex, postcentral gyrus) were observed in CD relative to HC. Further, decreased activity was seen in the salience network (cerebellum, postcentral gyrus), cerebellar network (occipital fusiform gyrus, cerebellum) and the DMN (parahippocampal gyrus, cerebellum) in CD relative to HC. Greater abdominal pain scores were associated with lower connectivity in the precuneus in the medial visual RSN, in the parietal operculum in the salience RSN, as well as reduced connectivity in the cerebellum in the frontal RSN. Finally, greater disease duration was associated with greater connectivity in the visual RSN in the middle temporal gyrus and planum temporale. Conclusion Alterations in RSFC between active CD and HC in RSNs implicated in cognition, attention, emotion and pain may reflect neuroplasticity in response to chronic systemic inflammation, abdominal pain, disease duration and severity. Identification of a neural phenotype specific to active CD status, may help guide intervention constituting a neurobehavioural precision medicine approach to treatment.

Functional connectome of brainstem nuclei involved in autonomic, limbic, pain and sensory processing in living humans from 7 Tesla resting state fMRI

Despite remarkable advances in mapping the functional connectivity of the cortex, the functional connectivity of subcortical regions is understudied in living humans. This is the case for brainstem nuclei that control vital processes, such as autonomic, limbic, nociceptive and sensory functions. This is because of the lack of precise brainstem nuclei localization, of adequate sensitivity and resolution in the deepest brain regions, as well as of optimized processing for the brainstem. To close the gap between the cortex and the brainstem, on 20 healthy subjects, we computed a correlation-based functional connectome of 15 brainstem nuclei involved in autonomic, limbic, nociceptive, and sensory function (superior and inferior colliculi, ventral tegmental area-parabrachial pigmented nucleus complex, microcellular tegmental nucleus-prabigeminal nucleus complex, lateral and medial parabrachial nuclei, vestibular and superior olivary complex, superior and inferior medullary reticular formation, viscerosensory motor nucleus, raphe magnus, pallidus, and obscurus, and parvicellular reticular nucleus – alpha part) with the rest of the brain. Specifically, we exploited 1.1mm isotropic resolution 7 Tesla resting-state fMRI, ad-hoc coregistration and physiological noise correction strategies, and a recently developed probabilistic template of brainstem nuclei. Further, we used 2.5mm isotropic resolution resting-state fMRI data acquired on a 3 Tesla scanner to assess the translatability of our results to conventional datasets. We report highly consistent correlation coefficients across subjects, confirming available literature on autonomic, limbic, nociceptive and sensory pathways, as well as high interconnectivity within the central autonomic network and the vestibular network. Interestingly, our results showed evidence of vestibulo-autonomic interactions in line with previous work. Comparison of 7 Tesla and 3 Tesla findings showed high translatability of results to conventional settings for brainstem-cortical connectivity and good yet weaker translatability for brainstem-brainstem connectivity. The brainstem functional connectome might bring new insight in the understanding of autonomic, limbic, nociceptive and sensory function in health and disease.

Hemodynamic Response Detection Using Integrated EEG-fNIRS-VPA for BCI

For BCI systems, it is important to have an accurate and less complex architecture to control a device with enhanced accuracy. In this paper, a novel methodology for more accurate detection of the hemodynamic response has been developed using a multimodal brain-computer interface (BCI). An integrated classifier has been developed for achieving better classification accuracy using two modalities. An integrated EEG-fNIRS-based vector-phase analysis (VPA) has been conducted. An open-source dataset collected at the Technische Universität Berlin, including simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals of 26 healthy participants during n-back tests, has been used for this research. Instrumental and physiological noise removal has been done using preprocessing techniques followed by individually detecting activity in both modalities. With resting state threshold circle, VPA has been used to detect a hemodynamic response in fNIRS signals, whereas phase plots for EEG signals have been constructed using Hilbert Transform to detect the activity in each trial. Multiple threshold circles are drawn in the vector plane, where each circle is drawn after task completion in each trial of EEG signal. Finally, both processes are integrated into one vector-phase plot to get combined detection of hemodynamic response for activity. Results of this study illustrate that the combined EEG-fNIRS VPA yields considerably higher average classification accuracy, that is 91.35%, as compared to other classifiers such as support vector machine (SVM), convolutional neural networks (CNN), deep neural networks (DNN) and VPA (with dual-threshold circles) with classification accuracies 82%, 89%, 87% and 86% respectively. Outcomes of this research demonstrate that improved classification performance can be feasibly achieved using multimodal VPA for EEG-fNIRS hybrid data.

Self-supervised representation learning from 12-lead ECG data

Clinical 12-lead electrocardiography (ECG) is one of the most widely encountered kinds of biosignals. Despite the increased availability of public ECG datasets, label scarcity remains a central challenge in the field. Self-supervised learning represents a promising way to alleviate this issue. This would allow to train more powerful models given the same amount of labeled data and to incorporate or improve predictions about rare diseases, for which training datasets are inherently limited. In this work, we put forward the first comprehensive assessment of self-supervised representation learning from clinical 12-lead ECG data. To this end, we adapt state-of-the-art self-supervised methods based on instance discrimination and latent forecasting to the ECG domain. In a first step, we learn contrastive representations and evaluate their quality based on linear evaluation performance on a recently established, comprehensive, clinical ECG classification task. In a second step, we analyze the impact of self-supervised pretraining on finetuned ECG classifiers as compared to purely supervised performance. For the best-performing method, an adaptation of contrastive predictive coding, we find a linear evaluation performance only 0.5% below supervised performance. For the finetuned models, we find improvements in downstream performance of roughly 1% compared to supervised performance, label efficiency, as well as robustness against physiological noise. This work clearly establishes the feasibility of extracting discriminative representations from ECG data via self-supervised learning and the numerous advantages when finetuning such representations on downstream tasks as compared to purely supervised training. As first comprehensive assessment of its kind in the ECG domain carried out exclusively on publicly available datasets, we hope to establish a first step towards reproducible progress in the rapidly evolving field of representation learning for biosignals.

Dynamic states of resting‐state connectivity in patients with mild cognitive impairment

AbstractBackgroundResting‐state functional connectivity (rsFC) has demonstrated promise as a biomarker for Alzheimer’s disease. Traditional rsFC assumes that brain connectivity is unvarying during the entire acquisition window. However, recent studies show that brain connection patterns or states are dynamic and change within a few seconds. We modified existing analyses of dynamic functional connectivity to identify differences between patients with Mild Cognitive Impairment (MCI) and controls.Method50 MCI patients (22 males, 74±8 years old) and 55 controls (35 males, age=75±8 y.o.) from ADNI3 with T1 and fast BOLD fMRI (TR=600ms) images were analyzed. Preprocessing consisted of motion correction, despiking, physiological noise correction, and registration to MNI space. Sliding windows had a stride length of 1 TR and contained 50 TRs. Correlation matrices were generated between brain regions of the default mode (DMN), salience (Sal), dorsal attention (DAN), and frontoparietal (FPN) networks. The data was then harmonized using ComBat to remove scanner‐related differences. Distinct brain connectivity patterns or states were extracted with Davies‐Bouldin optimized k‐means clustering. Dwell time (i.e., the number of TRs for which a state is sustained) and transition probability (i.e., the probability of changing state) were calculated. Significant differences were assessed using a t‐test without correcting for multiple comparisons.ResultWe identified eight states (Figure 1). No significant differences were discovered in dwell time (Figure 2). Six transition probabilities had p&lt;0.05 (Figure 3). These included the transition from 2 to 5 (p=0.03), 2 to 6 (p=0.04), 2 to 7 (p=0.04), 4 to 3 (p=0.005), 6 to 3 (p=0.01), 8 to 4 (p=0.05), and 8 to 7 (p=0.05).ConclusionOur study showed MCI patients were less likely to transition from state 4 to 3 compared to controls. State 4 is characterized by positive rsFC within the DMN and FPN as well as between the networks. Compared to state 4, state 3 had decreased rsFC within the DMN and FPN and between the networks. State 3 also shows increased positive rsFC within and between Sal and DAN. Interaction between the 4 networks has been implicated in AD. The inability to perform this transition could explain cognitive deficits observed in MCI.

Classification of Individual Finger Movements from Right Hand Using fNIRS Signals.

Functional near-infrared spectroscopy (fNIRS) is a comparatively new noninvasive, portable, and easy-to-use brain imaging modality. However, complicated dexterous tasks such as individual finger-tapping, particularly using one hand, have been not investigated using fNIRS technology. Twenty-four healthy volunteers participated in the individual finger-tapping experiment. Data were acquired from the motor cortex using sixteen sources and sixteen detectors. In this preliminary study, we applied standard fNIRS data processing pipeline, i.e., optical densities conversation, signal processing, feature extraction, and classification algorithm implementation. Physiological and non-physiological noise is removed using 4th order band-pass Butter-worth and 3rd order Savitzky–Golay filters. Eight spatial statistical features were selected: signal-mean, peak, minimum, Skewness, Kurtosis, variance, median, and peak-to-peak form data of oxygenated haemoglobin changes. Sophisticated machine learning algorithms were applied, such as support vector machine (SVM), random forests (RF), decision trees (DT), AdaBoost, quadratic discriminant analysis (QDA), Artificial neural networks (ANN), k-nearest neighbors (kNN), and extreme gradient boosting (XGBoost). The average classification accuracies achieved were , , and using k-nearest neighbors (kNN), Random forest (RF) and XGBoost, respectively. KNN, RF and XGBoost classifiers performed exceptionally well on such a high-class problem. The results need to be further investigated. In the future, a more in-depth analysis of the signal in both temporal and spatial domains will be conducted to investigate the underlying facts. The accuracies achieved are promising results and could open up a new research direction leading to enrichment of control commands generation for fNIRS-based brain-computer interface applications.

Temporal SNR optimization through RF coil combination in fMRI: The more, the better?

For functional MRI with a multi-channel receiver RF coil, images are often reconstructed channel by channel, resulting into multiple images per time frame. The final image to analyze usually is the result of the covariance Sum-of-Squares (covSoS) combination across these channels. Although this reconstruction is quasi-optimal in SNR, it is not necessarily the case in terms of temporal SNR (tSNR) of the time series, which is yet a more relevant metric for fMRI data quality. In this work, we investigated tSNR optimality through voxel-wise RF coil combination and its effects on BOLD sensitivity. An analytical solution for an optimal RF coil combination is described, which is somewhat tied to the extended Krueger-Glover model involving both thermal and physiological noise covariance matrices. Compared experimentally to covSOS on four volunteers at 7T, the method yielded great improvement of tSNR but, surprisingly, did not result into higher BOLD sensitivity. Solutions to improve the method such as for example the t-score for the mean recently proposed are also explored, but result into similar observations once the statistics are corrected properly. Overall, the work shows that data-driven RF coil combinations based on tSNR considerations alone should be avoided unless additional and unbiased assumptions can be made.

An ICA Investigation into the Effect of Physiological Noise Correction on Dimensionality and Spatial Maps of Intrinsic Connectivity Networks.

Physiological processes such as cardiac pulsations and respiration can induce signal modulations in functional magnetic resonance imaging (fMRI) time series, and confound inferences made about neural processing from analyses of the blood oxygenation level-dependent (BOLD) signals. Retrospective image space correction of physiological noise (RETROICOR) is a widely used approach to reduce physiological signals in data. Independent component analysis (ICA) is a valuable blind source separation method for analyzing brain networks, referred to as intrinsic connectivity networks (ICNs). Previously, we showed that temporal properties of the ICA-derived networks such as spectral power and functional network connectivity could be impacted by RETROICOR corrections. The goal of this study is to investigate the effect of retrospective correction of physiological artifacts on the ICA dimensionality (model order) and intensities of ICN spatial maps. To this aim, brain BOLD fMRI, heartbeat, and respiration were measured in 22 healthy subjects during resting state. ICA dimensionality was estimated using minimum description length (MDL) based on i.i.d. data samples and smoothness FWHM kernel, and entropy-rate based order selection by finite memory length model (ER-FM) and autoregressive model (ER-AR). Differences in spatial maps between the raw and denoised data were compared using the paired t-test and false discovery rate (FDR) thresholding was used to correct for multiple comparisons. Results showed that ICA dimensionality was greater in the raw data compared to the denoised data. Significant differences were found in the intensities of spatial maps for three ICNs: basal ganglia, precuneus, and frontal network. These preliminary results indicate that the retrospective physiological noise correction can induce change in the resting state spatial map intensity related to the within-network connectivity. More research is needed to understand this effect.

A visualization tool for assessment of spinal cord functional magnetic resonance imaging data quality.

Functional magnetic resonance imaging (fMRI) is an extensively used neuroimaging technique to non-invasively detect neural activity. Data quality is highly variable, and fMRI analysis typically consists of a number of complex processing steps. It is crucial to visually assess images throughout analysis to ensure that data quality at each step is satisfactory. For fMRI analysis of the brain, there is a simple tool to visualize four-dimensional data on a two-dimensional plot for qualitative analysis. Despite the practicality of this method, it cannot be directly applied to fMRI data of the spinal cord, and a comparable approach does not exist for spinal cord fMRI analysis. The additional challenges encountered in spinal cord imaging, including the small size of the cord and the influence of physiological noise sources, drive the importance of developing a similar visualization technique for spinal cord fMRI. Here, we introduce a highly versatile image analysis tool to visualize spinal cord fMRI data as a simple heatmap and to co-visualize relevant traces such as physiological or motion timeseries. We present multiple variations of the plot, data features that can be identified with the heatmap, and examples of the useful qualitative analyses that can be performed using this method. The spinal cord plot can be easily integrated into an fMRI analysis pipeline and can streamline visual inspection and qualitative analysis of functional imaging data.Clinical Relevance- Implementation of this data visualization method is a simple addition to spinal cord fMRI analysis that could be used to identify normal vs. abnormal signal variation in pathologies that impact the cord, such as spinal cord injury or multiple sclerosis.

Robustness of convolutional neural networks tophysiological electrocardiogram noise.

The electrocardiogram (ECG) is a widespread diagnostic tool in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal. However, there are open questions around the robustness of these methods to various factors, including physiological ECG noise. In this study, we generate clean and noisy versions of an ECG dataset before applying symmetric projection attractor reconstruction (SPAR) and scalogram image transformations. A convolutional neural network is used to classify these image transforms. For the clean ECG dataset, F1 scores for SPAR attractor and scalogram transforms were 0.70 and 0.79, respectively. Scores decreased by less than 0.05 for the noisy ECG datasets. Notably, when the network trained on clean data was used to classify the noisy datasets, performance decreases of up to 0.18 in F1 scores were seen. However, when the network trained on the noisy data was used to classify the clean dataset, the decrease was less than 0.05. We conclude that physiological ECG noise impacts classification using deep learning methods and careful consideration should be given to the inclusion of noisy ECG signals in the training data when developing supervised networks for ECG classification.This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

Correlated noise in brain magnetic resonance elastography.

Magnetic resonance elastography (MRE) uses phase-contrast MRI to generate mechanical property maps of the in vivo brain through imaging of tissue deformation from induced mechanical vibration. The mechanical property estimation process in MRE can be susceptible to noise from physiological and mechanical sources encoded in the phase, which is expected to be highly correlated. This correlated noise has yet to be characterized in brain MRE, and its effects on mechanical property estimates computed using inversion algorithms are undetermined. To characterize the effects of signal noise in MRE, we conducted 3 experiments quantifying (1) physiomechanical sources of signal noise, (2) physiological noise because of cardiac-induced movement, and (3) impact of correlated noise on mechanical property estimates. We use a correlation length metric to estimate the extent that correlated signal persists in MRE images and demonstrate the effect of correlated noise on property estimates through simulations. We found that both physiological noise and vibration noise were greater than image noise and were spatially correlated across all subjects. Added physiological and vibration noise to simulated data resulted in property maps with higher error than equivalent levels of Gaussian noise. Our work provides the foundation to understand contributors to brain MRE data quality and provides recommendations for future work to correct for signal noise in MRE.

Performance Assessment of a Commercial Continuous-Wave Near-Infrared Spectroscopy Tissue Oximeter for Suitability for Use in an International, Multi-Center Clinical Trial.

Despite the wide range of clinical and research applications, the reliability of the absolute oxygenation measurements of continuous wave near-infrared spectroscopy sensors is often questioned, partially due to issues of standardization. In this study, we have compared the performances of 13 units of a continuous wave near-infrared spectroscopy device (PortaMon, Artinis Medical Systems, NL) to test their suitability for being used in the HEMOCOVID-19 clinical trial in 10 medical centers around the world. Detailed phantom and in vivo tests were employed to measure the precision and reproducibility of measurements of local blood oxygen saturation and total hemoglobin concentration under different conditions: for different devices used, different operators, for probe repositioning over the same location, and over time (hours/days/months). We have detected systematic differences between devices when measuring phantoms (inter-device variability, <4%), which were larger than the intra-device variability (<1%). This intrinsic variability is in addition to the variability during in vivo measurements on the forearm muscle resulting from errors in probe positioning and intrinsic physiological noise (<9%), which was also larger than the inter-device differences (<3%) during the same test. Lastly, we have tested the reproducibility of the protocol of the HEMOCOVID-19 clinical trial; that is, forearm muscle oxygenation monitoring during vascular occlusion tests over days. Overall, our conclusion is that these devices can be used in multi-center trials but care must be taken to characterize, follow-up, and statistically account for inter-device variability.

Sensitivity limitations of high-resolution perfusion-based human fMRI at 7 Tesla

The study of the brain's functional organization at laminar and columnar level of the cortex with blood oxygenation-level dependent (BOLD) functional MRI (fMRI) is affected by the contribution of large veins downstream from the microvascular response to brain activity. Blood volume- and especially perfusion-based techniques may reduce this problem because of their reduced sensitivity to venous effects, but may not allow the same spatial resolution because of smaller signal changes associated with brain activity. Here we investigated the practical resolution limits of perfusion-weighted fMRI in human visual stimulation experiments. For this purpose, we used a highly sensitive, single-shot perfusion labeling (SSPL) technique at 7 T and compared sensitivity to detect visual activation at low (2 mm, n = 10) and high (1 mm, n = 8) nominal isotropic spatial, and 3 s temporal, resolution with BOLD in 5½-minute-long experiments. Despite the smaller absolute signal change with activation, 2 mm resolution SSPL yielded comparable sensitivity to BOLD. This was attributed to a superior suppression of physiological noise with SSPL. However, at 1 mm nominal resolution, SSPL sensitivity fell on average at least 42% below that of BOLD, and detection of visual activation was compromised. This is explained by the fact that at high resolution, with both techniques, typically thermal noise rather than physiological noise dominates sensitivity. The observed sensitivity loss implies that to perform 1-mm resolution, perfusion weighted fMRI with a robustness similar to BOLD, scan times that are almost 3 times longer than the comparable BOLD experiment are required. This is in line with or slightly better than previous comparisons between perfusion-weighted fMRI and BOLD. The lower sensitivity has to be weighed against the spatial fidelity advantages of high-resolution perfusion-weighted fMRI.

The Longitudinal Effect of Meditation on Resting-State Functional Connectivity Using Dynamic Arterial Spin Labeling: A Feasibility Study.

We aimed to assess whether dynamic arterial spin labeling (dASL), a novel quantitative MRI technique with minimal contamination of subject motion and physiological noises, could detect the longitudinal effect of focused attention meditation (FAM) on resting-state functional connectivity (rsFC). A total of 10 novice meditators who recorded their FAM practice time were scanned at baseline and at the 2-month follow-up. Two-month meditation practice caused significantly increased rsFC between the left medial temporal (LMT) seed and precuneus area and between the right frontal eye (RFE) seed and medial prefrontal cortex. Meditation practice time was found to be positively associated with longitudinal changes of rsFC between the default mode network (DMN) and dorsal attention network (DAN), between DMN and insula, and between DAN and the frontoparietal control network (FPN) but negatively associated with changes of rsFC between DMN and FPN, and between DAN and visual regions. These findings demonstrate the capability of dASL in identifying the FAM-induced rsFC changes and suggest that the practice of FAM can strengthen the efficient control of FPN on fast switching between DMN and DAN and enhance the utilization of attentional resources with reduced focus on visual processing.

Signal to noise and b-value analysis for optimal intra-voxel incoherent motion imaging in the brain.

Intravoxel incoherent motion (IVIM) is a method that can provide quantitative information about perfusion in the human body, in vivo, and without contrast agent. Unfortunately, the IVIM perfusion parameter maps are known to be relatively noisy in the brain, in particular for the pseudo-diffusion coefficient, which might hinder its potential broader use in clinical applications. Therefore, we studied the conditions to produce optimal IVIM perfusion images in the brain. IVIM imaging was performed on a 3-Tesla clinical system in four healthy volunteers, with 16 b values 0, 10, 20, 40, 80, 110, 140, 170, 200, 300, 400, 500, 600, 700, 800, 900 s/mm2, repeated 20 times. We analyzed the noise characteristics of the trace images as a function of b-value, and the homogeneity of the IVIM parameter maps across number of averages and sub-sets of the acquired b values. We found two peaks of noise of the trace images as function of b value, one due to thermal noise at high b-value, and one due to physiological noise at low b-value. The selection of b value distribution was found to have higher impact on the homogeneity of the IVIM parameter maps than the number of averages. Based on evaluations, we suggest an optimal b value acquisition scheme for a 12 min scan as 0 (7), 20 (4), 140 (19), 300 (9), 500 (19), 700 (1), 800 (4), 900 (1) s/mm2.