Physiological Noise Research Articles

Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks

Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon measuring activity on the surface of cerebral cortex rather than in subcortical regions such as midbrain and brainstem. Subcortical fMRI must overcome two challenges: spatial resolution and physiological noise. Here we describe an optimized set of techniques developed to perform high-resolution fMRI in human SC, a structure on the dorsal surface of the midbrain; the methods can also be used to image other brainstem and subcortical structures. High-resolution (1.2 mm voxels) fMRI of the SC requires a non-conventional approach. The desired spatial sampling is obtained using a multi-shot (interleaved) spiral acquisition (1). Since, T(2)* of SC tissue is longer than in cortex, a correspondingly longer echo time (T(E) ~ 40 msec) is used to maximize functional contrast. To cover the full extent of the SC, 8-10 slices are obtained. For each session a structural anatomy with the same slice prescription as the fMRI is also obtained, which is used to align the functional data to a high-resolution reference volume. In a separate session, for each subject, we create a high-resolution (0.7 mm sampling) reference volume using a T(1)-weighted sequence that gives good tissue contrast. In the reference volume, the midbrain region is segmented using the ITK-SNAP software application (2). This segmentation is used to create a 3D surface representation of the midbrain that is both smooth and accurate (3). The surface vertices and normals are used to create a map of depth from the midbrain surface within the tissue (4). Functional data is transformed into the coordinate system of the segmented reference volume. Depth associations of the voxels enable the averaging of fMRI time series data within specified depth ranges to improve signal quality. Data is rendered on the 3D surface for visualization. In our lab we use this technique for measuring topographic maps of visual stimulation and covert and overt visual attention within the SC (1). As an example, we demonstrate the topographic representation of polar angle to visual stimulation in SC.

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon measuring activity on the surface of cerebral cortex rather than in subcortical regions such as midbrain and brainstem. Subcortical fMRI must overcome two challenges: spatial resolution and physiological noise. Here we describe an optimized set of techniques developed to perform high-resolution fMRI in human SC, a structure on the dorsal surface of the midbrain; the methods can also be used to image other brainstem and subcortical structures. High-resolution (1.2 mm voxels) fMRI of the SC requires a non-conventional approach. The desired spatial sampling is obtained using a multi-shot (interleaved) spiral acquisition1. Since, T2* of SC tissue is longer than in cortex, a correspondingly longer echo time (TE ~ 40 msec) is used to maximize functional contrast. To cover the full extent of the SC, 8-10 slices are obtained. For each session a structural anatomy with the same slice prescription as the fMRI is also obtained, which is used to align the functional data to a high-resolution reference volume. In a separate session, for each subject, we create a high-resolution (0.7 mm sampling) reference volume using a T1-weighted sequence that gives good tissue contrast. In the reference volume, the midbrain region is segmented using the ITK-SNAP software application2. This segmentation is used to create a 3D surface representation of the midbrain that is both smooth and accurate3. The surface vertices and normals are used to create a map of depth from the midbrain surface within the tissue4. Functional data is transformed into the coordinate system of the segmented reference volume. Depth associations of the voxels enable the averaging of fMRI time series data within specified depth ranges to improve signal quality. Data is rendered on the 3D surface for visualization. In our lab we use this technique for measuring topographic maps of visual stimulation and covert and overt visual attention within the SC1. As an example, we demonstrate the topographic representation of polar angle to visual stimulation in SC.

Stimulus Site and Modality Dependence of Functional Activity within the Human Spinal Cord

Chronic pain is thought to arise because of maladaptive changes occurring within the peripheral nervous system and CNS. The transition from acute to chronic pain is known to involve the spinal cord (Woolf and Salter, 2000). Therefore, to investigate altered human spinal cord function and translate results obtained from other species, a noninvasive neuroimaging technique is desirable. We have investigated the functional response in the cervical spinal cord of 18 healthy human subjects (aged 22-40 years) to noxious thermal and non-noxious tactile stimulation of the left and right forearms. Physiological noise, which is a significant source of signal variability in the spinal cord, was accounted for in the general linear model. Group analysis, performed using a mixed-effects model, revealed distinct regions of activity that were dependent on both the side and the type of stimulation. In particular, thermal stimulation on the medial aspect of the wrist produced activity within the C6/C5 segment ipsilateral to the side of stimulation. Similar to data recorded in animals (Fitzgerald, 1982), painful thermal stimuli produced increased ipsilateral and decreased contralateral blood flow, which may reflect, respectively, excitatory and inhibitory processes. Nonpainful punctate stimulation of the thenar eminence provoked more diffuse activity but was still ipsilateral to the side of stimulation. These results present the first noninvasive evidence for a lateralized response to noxious and non-noxious stimuli in the human spinal cord. The development of these techniques opens the path to understanding, at a subject-specific level, central sensitization processes that contribute to chronic pain states.

Modulation of Human Muscle Spindle Discharge by Arterial Pulsations - Functional Effects and Consequences

Arterial pulsations are known to modulate muscle spindle firing; however, the physiological significance of such synchronised modulation has not been investigated. Unitary recordings were made from 75 human muscle spindle afferents innervating the pretibial muscles. The modulation of muscle spindle discharge by arterial pulsations was evaluated by R-wave triggered averaging and power spectral analysis. We describe various effects arterial pulsations may have on muscle spindle afferent discharge. Afferents could be “driven” by arterial pulsations, e.g., showing no other spontaneous activity than spikes generated with cardiac rhythmicity. Among afferents showing ongoing discharge that was not primarily related to cardiac rhythmicity we illustrate several mechanisms by which individual spikes may become phase-locked. However, in the majority of afferents the discharge rate was modulated by the pulse wave without spikes being phase locked. Then we assessed whether these influences changed in two physiological conditions in which a sustained increase in muscle sympathetic nerve activity was observed without activation of fusimotor neurones: a maximal inspiratory breath-hold, which causes a fall in systolic pressure, and acute muscle pain, which causes an increase in systolic pressure. The majority of primary muscle spindle afferents displayed pulse-wave modulation, but neither apnoea nor pain had any significant effect on the strength of this modulation, suggesting that the physiological noise injected by the arterial pulsations is robust and relatively insensitive to fluctuations in blood pressure. Within the afferent population there was a similar number of muscle spindles that were inhibited and that were excited by the arterial pulse wave, indicating that after signal integration at the population level, arterial pulsations of opposite polarity would cancel each other out. We speculate that with close-to-threshold stimuli the arterial pulsations may serve as an endogenous noise source that may synchronise the sporadic discharge within the afferent population and thus facilitate the detection of weak stimuli.

The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy.

A major methodological challenge of functional near-infrared spectroscopy (fNIRS) is its high sensitivity to haemodynamic fluctuations in the scalp. Superficial fluctuations contribute on the one hand to the physiological noise of fNIRS, impairing the signal-to-noise ratio, and may on the other hand be erroneously attributed to cerebral changes, leading to false positives in fNIRS experiments. Here we explore the localisation, time course and physiological origin of task-evoked superficial signals in fNIRS and present a method to separate them from cortical signals. We used complementary fNIRS, fMRI, MR-angiography and peripheral physiological measurements (blood pressure, heart rate, skin conductance and skin blood flow) to study activation in the frontal lobe during a continuous performance task. The General Linear Model (GLM) was applied to analyse the fNIRS data, which included an additional predictor to account for systemic changes in the skin.We found that skin blood volume strongly depends on the cognitive state and that sources of task-evoked systemic signals in fNIRS are co-localized with veins draining the scalp. Task-evoked superficial artefacts were mainly observed in concentration changes of oxygenated haemoglobin and could be effectively separated from cerebral signals by GLM analysis. Based on temporal correlation of fNIRS and fMRI signals with peripheral physiological measurements we conclude that the physiological origin of the systemic artefact is a task-evoked sympathetic arterial vasoconstriction followed by a decrease in venous volume.Since changes in sympathetic outflow accompany almost any cognitive and emotional process, we expect scalp vessel artefacts to be present in a wide range of fNIRS settings used in neurocognitive research. Therefore a careful separation of fNIRS signals originating from activated brain and from scalp is a necessary precondition for unbiased fNIRS brain activation maps.

Task-related BOLD responses and resting-state functional connectivity during physiological clamping of end-tidal CO2

Carbon dioxide (CO(2)), a potent vasodilator, is known to have a significant impact on the blood-oxygen level dependent (BOLD) signal. With the growing interest in studying synchronized BOLD fluctuations during the resting state, the extent to which the apparent synchrony is due to variations in the end-tidal pressure of CO(2) (PETCO(2)) is an important consideration. CO(2)-related fluctuations in BOLD signal may also represent a potential confound when studying task-related responses, especially if breathing depth and rate are affected by the task. While previous studies of the above issues have explored retrospective correction of BOLD fluctuations related to arterial PCO(2), here we demonstrate an alternative approach based on physiological clamping of the arterial CO(2) level to a near-constant value. We present data comparing resting-state functional connectivity within the default-mode-network (DMN), as well as task-related BOLD responses, acquired in two conditions in each subject: 1) while subject's PETCO(2) was allowed to vary spontaneously; and 2) while controlling subject's PETCO(2) within a narrow range. Strong task-related responses and areas of maximal signal correlation in the DMN were not significantly altered by suppressing fluctuations in PETCO(2). Controlling PETCO(2) did, however, improve the performance of retrospective physiological noise correction techniques, allowing detection of additional regions of task-related response and resting-state connectivity in highly vascularized regions such as occipital cortex. While these results serve to further rule out systemic physiological fluctuations as a significant source of apparent resting-state network connectivity, they also demonstrate that fluctuations in arterial CO(2) are one of the factors limiting sensitivity in task-based and resting-state fMRI, particularly in regions of high vascular density. This must be considered when comparing subject groups who might exhibit differences in respiratory physiology or breathing patterns.

Enhancement of temporal resolution and BOLD sensitivity in real-time fMRI using multi-slab echo-volumar imaging

In this study, a new approach to high-speed fMRI using multi-slab echo-volumar imaging (EVI) is developed that minimizes geometrical image distortion and spatial blurring, and enables nonaliased sampling of physiological signal fluctuation to increase BOLD sensitivity compared to conventional echo-planar imaging (EPI). Real-time fMRI using whole brain 4-slab EVI with 286 ms temporal resolution (4mm isotropic voxel size) and partial brain 2-slab EVI with 136 ms temporal resolution (4×4×6 mm(3) voxel size) was performed on a clinical 3 Tesla MRI scanner equipped with 12-channel head coil. Four-slab EVI of visual and motor tasks significantly increased mean (visual: 96%, motor: 66%) and maximum t-score (visual: 263%, motor: 124%) and mean (visual: 59%, motor: 131%) and maximum (visual: 29%, motor: 67%) BOLD signal amplitude compared with EPI. Time domain moving average filtering (2s width) to suppress physiological noise from cardiac and respiratory fluctuations further improved mean (visual: 196%, motor: 140%) and maximum (visual: 384%, motor: 200%) t-scores and increased extents of activation (visual: 73%, motor: 70%) compared to EPI. Similar sensitivity enhancement, which is attributed to high sampling rate at only moderately reduced temporal signal-to-noise ratio (mean: -52%) and longer sampling of the BOLD effect in the echo-time domain compared to EPI, was measured in auditory cortex. Two-slab EVI further improved temporal resolution for measuring task-related activation and enabled mapping of five major resting state networks (RSNs) in individual subjects in 5 min scans. The bilateral sensorimotor, the default mode and the occipital RSNs were detectable in time frames as short as 75 s. In conclusion, the high sampling rate of real-time multi-slab EVI significantly improves sensitivity for studying the temporal dynamics of hemodynamic responses and for characterizing functional networks at high field strength in short measurement times.

Optimizing Preprocessing and Analysis Pipelines for Single-Subject fMRI: 2. Interactions with ICA, PCA, Task Contrast and Inter-Subject Heterogeneity

A variety of preprocessing techniques are available to correct subject-dependant artifacts in fMRI, caused by head motion and physiological noise. Although it has been established that the chosen preprocessing steps (or “pipeline”) may significantly affect fMRI results, it is not well understood how preprocessing choices interact with other parts of the fMRI experimental design. In this study, we examine how two experimental factors interact with preprocessing: between-subject heterogeneity, and strength of task contrast. Two levels of cognitive contrast were examined in an fMRI adaptation of the Trail-Making Test, with data from young, healthy adults. The importance of standard preprocessing with motion correction, physiological noise correction, motion parameter regression and temporal detrending were examined for the two task contrasts. We also tested subspace estimation using Principal Component Analysis (PCA), and Independent Component Analysis (ICA). Results were obtained for Penalized Discriminant Analysis, and model performance quantified with reproducibility (R) and prediction metrics (P). Simulation methods were also used to test for potential biases from individual-subject optimization. Our results demonstrate that (1) individual pipeline optimization is not significantly more biased than fixed preprocessing. In addition, (2) when applying a fixed pipeline across all subjects, the task contrast significantly affects pipeline performance; in particular, the effects of PCA and ICA models vary with contrast, and are not by themselves optimal preprocessing steps. Also, (3) selecting the optimal pipeline for each subject improves within-subject (P,R) and between-subject overlap, with the weaker cognitive contrast being more sensitive to pipeline optimization. These results demonstrate that sensitivity of fMRI results is influenced not only by preprocessing choices, but also by interactions with other experimental design factors. This paper outlines a quantitative procedure to denoise data that would otherwise be discarded due to artifact; this is particularly relevant for weak signal contrasts in single-subject, small-sample and clinical datasets.

Physiological denoising of BOLD fMRI data using Regressor Interpolation at Progressive Time Delays (RIPTiDe) processing of concurrent fMRI and near-infrared spectroscopy (NIRS)

Confounding noise in BOLD fMRI data arises primarily from fluctuations in blood flow and oxygenation due to cardiac and respiratory effects, spontaneous low frequency oscillations (LFO) in arterial pressure, and non-task related neural activity. Cardiac noise is particularly problematic, as the low sampling frequency of BOLD fMRI ensures that these effects are aliased in recorded data. Various methods have been proposed to estimate the noise signal through measurement and transformation of the cardiac and respiratory waveforms (e.g. RETROICOR and respiration volume per time (RVT)) and model-free estimation of noise variance through examination of spatial and temporal patterns. We have previously demonstrated that by applying a voxel-specific time delay to concurrently acquired near infrared spectroscopy (NIRS) data, we can generate regressors that reflect systemic blood flow and oxygenation fluctuations effects. Here, we apply this method to the task of removing physiological noise from BOLD data. We compare the efficacy of noise removal using various sets of noise regressors generated from NIRS data, and also compare the noise removal to RETROICOR+RVT. We compare the results of resting state analyses using the original and noise filtered data, and we evaluate the bias for the different noise filtration methods by computing null distributions from the resting data and comparing them with the expected theoretical distributions. Using the best set of processing choices, six NIRS-generated regressors with voxel-specific time delays explain a median of 10.5% of the variance throughout the brain, with the highest reductions being seen in gray matter. By comparison, the nine RETROICOR+RVT regressors together explain a median of 6.8% of the variance in the BOLD data. Detection of resting state networks was enhanced with NIRS denoising, and there were no appreciable differences in the bias of the different techniques. Physiological noise regressors generated using Regressor Interpolation at Progressive Time Delays (RIPTiDe) offer an effective method for efficiently removing hemodynamic noise from BOLD data.

Measurement and characterization of the human spinal cord SEEP response using event-related spinal fMRI

Although event-related fMRI is able to reliably detect brief changes in brain activity and is now widely used throughout systems and cognitive neuroscience, there have been no previous reports of event-related spinal cord fMRI. This is likely attributable to the various technical challenges associated with spinal fMRI (e.g., imaging a suitable length of the cord, reducing image artifacts from the vertebrae and intervertebral discs, and dealing with physiological noise from spinal cord motion). However, with many of these issues now resolved, the largest remaining impediment for event-related spinal fMRI is a deprived understanding of the spinal cord fMRI signal time course. Therefore, in this study, we used a proton density-weighted HASTE sequence, with functional contrast based on signal enhancement by extravascular water protons (SEEP), and a motion-compensating GLM analysis to (i) characterize the SEEP response function in the human cervical spinal cord and (ii) demonstrate the feasibility of event-related spinal fMRI. This was achieved by applying very brief (1 s) epochs of 22°C thermal stimulation to the palm of the hand and measuring the impulse response function. Our results suggest that the spinal cord SEEP response (time to peak ≈8 s; FWHM ≈4 s; and probably lacking pre- and poststimulus undershoots) is slower than previous estimates of SEEP or BOLD responses in the brain, but faster than previously reported spinal cord BOLD responses. Finally, by detecting and mapping consistent signal-intensity changes within and across subjects, and validating these regions with a block-designed experiment, this study represents the first successful demonstration of event-related spinal fMRI.

Informed RESTORE: A method for robust estimation of diffusion tensor from low redundancy datasets in the presence of physiological noise artifacts

Physiological noise artifacts, especially those originating from cardiac pulsation and subject motion, are common in clinical Diffusion tensor-MRI acquisitions. Previous works show that signal perturbations produced by artifacts can be severe and neglecting to account for their contribution can result in erroneous diffusion tensor values. The Robust Estimation of Tensors by Outlier Rejection (RESTORE) method has been shown to be an effective strategy for improving tensor estimation on a voxel-by-voxel basis in the presence of artifactual data points in diffusion-weighted images. In this article, we address potential instabilities that may arise when using RESTORE and propose practical constraints to improve its usability. Moreover, we introduce a method, called informed RESTORE designed to remove physiological noise artifacts in datasets acquired with low redundancy (less than 30-40 diffusion-weighted image volumes)--a condition in which the original RESTORE algorithm may converge to an incorrect solution. This new method is based on the notion that physiological noise is more likely to result in signal dropouts than signal increases. Results from both Monte Carlo simulation and clinical diffusion data indicate that informed RESTORE performs very well in removing physiological noise artifacts for low redundancy diffusion-weighted image datasets.

Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER

In this article we introduce the DRIFTER algorithm, which is a new model based Bayesian method for retrospective elimination of physiological noise from functional magnetic resonance imaging (fMRI) data. In the method, we first estimate the frequency trajectories of the physiological signals with the interacting multiple models (IMM) filter algorithm. The frequency trajectories can be estimated from external reference signals, or if the temporal resolution is high enough, from the fMRI data. The estimated frequency trajectories are then used in a state space model in combination of a Kalman filter (KF) and Rauch-Tung-Striebel (RTS) smoother, which separates the signal into an activation related cleaned signal, physiological noise, and white measurement noise components. Using experimental data, we show that the method outperforms the RETROICOR algorithm if the shape and amplitude of the physiological signals change over time.

The role of physiological noise in resting-state functional connectivity

Functional connectivity between different brain regions can be estimated from MRI data by computing the temporal correlation of low frequency (<0.1Hz) fluctuations in the MRI signal. These correlated fluctuations occur even when the subject is "at rest" (not asked to perform any particular task) and result from spontaneous neuronal activity synchronized within multiple distinct networks of brain regions. This estimate of connectivity, however, can be influenced by physiological noise, such as cardiac and respiratory fluctuations. This brief review looks at the effect of physiological noise on estimates of resting-state functional connectivity, discusses ways to remove physiological noise, and provides a personal recollection of the early developments in these approaches. This review also discusses the importance of physiological noise correction and provides a summary of evidence demonstrating that functional connectivity does have a neuronal underpinning and cannot purely be the result of physiological noise.

Investigating the Need for Modelling Temporal Dependencies in a Brain-Computer Interface with Real-Time Feedback Based on near Infrared Spectra

Near infrared (NIR) spectroscopy is an emerging non-invasive brain-computer interface (BCI) modality that measures changes in haemoglobin concentrations in neurocortical tissue. Previous NIR spectroscopy studies have not employed real-time feedback with online classification, a combination which would allow users to alter their mental strategy on the fly. In particular, it is unclear whether or not the temporal dependencies of haemodynamic changes ought to be considered in online classification. To answer this quest ion, this study contrasted online classification of prefrontal haemodynamics using NIR spectra processed using two approaches: an artificial neural network (ANN) that considered instantaneous samples of oxy- and deoxy-haemoglobin concentrations (i.e. ignored temporal dependencies) and a hidden Markov model-based (HMM) classifier which modelled a temporal sequence of concentrations (i.e. embodied temporal dependencies). Both classifiers were implemented for online operation with immediate visual feedback via a monitor showing a vertical bar the height of which was contingent on the classifier's output. Ten subjects participated in two study sessions each, one with each type of classifier. Participants were cued to raise and lower the bar in alternating 20s intervals using mental fast singing and focused breathing, respectively. Only the ANN classifier facilitated online classification rates greater than chance ( P = 0.0289). The influence of physiological noise on online classification of prefrontal haemodynamics was deemed to be minimal via offline analysis of concurrently measured respiration and blood pulse. Nine of the ten participants reported using the feedback to alter their activation strategy. Mental fatigue, task repetitiveness and the lack of ambient lighting were identified as factors compromising performance in half the participants. The inferior performance of the HMM classifiers suggests that modelling of the temporal dynamics of haemoglobin concentration changes may not be necessary in an online NIR-BCI. Further study of online NIR-BCIs with instantaneous feedback is warranted.

Spatially variable Rician noise in magnetic resonance imaging

Magnetic resonance images tend to be influenced by various random factors usually referred to as "noise". The principal sources of noise and related artefacts can be divided into two types: arising from hardware (acquisition coil arrays, gradient coils, field inhomogeneity); and arising from the subject (physiological noise including body motion, cardiac pulsation or respiratory motion). These factors negatively affect the resolution and reproducibility of the images. Therefore, a proper noise treatment is important for improving the performance of clinical and research investigations. Noise reduction becomes especially critical for the images with a low signal-to-noise ratio, such as those typically acquired in diffusion tensor imaging at high diffusion weightings. The standard methods of signal correction usually assume a uniform distribution of the standard deviation of the noise across the image and evaluate a single correction parameter for the whole image. We pursue a more advanced approach based on the assumption of an inhomogeneous distribution of noise in space and evaluate correction factors for each voxel individually. The Rician nature of the underlying noise is considered for low and high signal-to-noise ratios. The approach developed here has been examined using numerical simulations and in vivo brain diffusion tensor imaging experiments. The efficacy and usefulness of this approach is demonstrated here and the resultant effective tool is described.

Assessment of physiological noise modelling methods for functional imaging of the spinal cord

The spinal cord is the main pathway for information between the central and the peripheral nervous systems. Non-invasive functional MRI offers the possibility of studying spinal cord function and central sensitisation processes. However, imaging neural activity in the spinal cord is more difficult than in the brain. A significant challenge when dealing with such data is the influence of physiological noise (primarily cardiac and respiratory), and currently there is no standard approach to account for these effects. We have previously studied the various sources of physiological noise for spinal cord fMRI at 1.5T and proposed a physiological noise model (PNM) (Brooks et al., 2008). An alternative de-noising strategy, selective averaging filter (SAF), was proposed by Deckers et al. (2006). In this study we reviewed and implemented published physiological noise correction methods at higher field (3T) and aimed to find the optimal models for gradient-echo-based BOLD acquisitions. Two general techniques were compared: physiological noise model (PNM) and selective averaging filter (SAF), along with regressors designed to account for specific signal compartments and physiological processes: cerebrospinal fluid (CSF), motion correction (MC) parameters, heart rate (HR), respiration volume per time (RVT), and the associated cardiac and respiratory response functions. Functional responses were recorded from the cervical spinal cord of 18 healthy subjects in response to noxious thermal and non-noxious punctate stimulation. The various combinations of models and regressors were compared in three ways: the model fit residuals, regression model F-tests and the number of activated voxels. The PNM was found to outperform SAF in all three tests. Furthermore, inclusion of the CSF regressor was crucial as it explained a significant amount of signal variance in the cord and increased the number of active cord voxels. Whilst HR, RVT and MC explained additional signal (noise) variance, they were also found (in particular HR and RVT) to have a negative impact on the parameter estimates (of interest)--as they may be correlated with task conditions e.g. noxious thermal stimuli. Convolution with previously published cardiac and respiratory impulse response functions was not found to be beneficial. The other novel aspect of current study is the investigation of the influence of pre-whitening together with PNM regressors on spinal fMRI data. Pre-whitening was found to reduce non-white noise, which was not accounted for by physiological noise correction, and decrease false positive detection rates.

Detection of physiological noise in resting state fMRI using machine learning.

We present a technique for predicting cardiac and respiratory phase on a time point by time point basis, from fMRI image data. These predictions have utility in attempts to detrend effects of the physiological cycles from fMRI image data. We demonstrate the technique both in the case where it can be trained on a subject's own data, and when it cannot. The prediction scheme uses a multiclass support vector machine algorithm. Predictions are demonstrated to have a close fit to recorded physiological phase, with median Pearson correlation scores between recorded and predicted values of 0.99 for the best case scenario (cardiac cycle trained on a subject's own data) down to 0.83 for the worst case scenario (respiratory predictions trained on group data), as compared to random chance correlation score of 0.70. When predictions were used with RETROICOR--a popular physiological noise removal tool--the effects are compared to using recorded phase values. Using Fourier transforms and seed based correlation analysis, RETROICOR is shown to produce similar effects whether recorded physiological phase values are used, or they are predicted using this technique. This was seen by similar levels of noise reduction noise in the same regions of the Fourier spectra, and changes in seed based correlation scores in similar regions of the brain. This technique has a use in situations where data from direct monitoring of the cardiac and respiratory cycles are incomplete or absent, but researchers still wish to reduce this source of noise in the image data.

Improved method for retinotopy constrained source estimation of visual‐evoked responses

Retinotopy constrained source estimation (RCSE) is a method for noninvasively measuring the time courses of activation in early visual areas using magnetoencephalography (MEG) or electroencephalography (EEG). Unlike conventional equivalent current dipole or distributed source models, the use of multiple, retinotopically mapped stimulus locations to simultaneously constrain the solutions allows for the estimation of independent waveforms for visual areas V1, V2, and V3, despite their close proximity to each other. We describe modifications that improve the reliability and efficiency of this method. First, we find that increasing the number and size of visual stimuli results in source estimates that are less susceptible to noise. Second, to create a more accurate forward solution, we have explicitly modeled the cortical point spread of individual visual stimuli. Dipoles are represented as extended patches on the cortical surface, which take into account the estimated receptive field size at each location in V1, V2, and V3 as well as the contributions from contralateral, ipsilateral, dorsal, and ventral portions of the visual areas. Third, we implemented a map fitting procedure to deform a template to match individual subject retinotopic maps derived from functional magnetic resonance imaging (fMRI). This improves the efficiency of the overall method by allowing automated dipole selection, and it makes the results less sensitive to physiological noise in fMRI retinotopy data. Finally, the iteratively reweighted least squares (IRLS) method was used to reduce the contribution from stimulus locations with high residual error for robust estimation of visual evoked responses.

The utility of near-infrared spectroscopy in the regression of low-frequency physiological noise from functional magnetic resonance imaging data

Near-infrared spectroscopy (NIRS) signals have been shown to correlate with resting-state BOLD-fMRI data across the whole brain volume, particularly at frequencies below 0.1Hz. While the physiological origins of this correlation remain unclear, its existence may have a practical application in minimizing the background physiological noise present in BOLD-fMRI recordings. We performed simultaneous, resting-state fMRI and 28-channel NIRS in seven adult subjects in order to assess the utility of NIRS signals in the regression of physiological noise from fMRI data. We calculated the variance of the residual error in a general linear model of the baseline fMRI signal, and the reduction of this variance achieved by including NIRS signals in the model. In addition, we introduced a sequence of simulated hemodynamic response functions (HRFs) into the resting-state fMRI data of each subject in order to quantify the effectiveness of NIRS signals in optimizing the recovery of that HRF. For comparison, these calculations were also performed using a pulse and respiration RETROICOR model. Our results show that the use of 10 or more NIRS channels can reduce variance in the residual error by as much as 36% on average across the whole cortex. However the same number of low-pass filtered white noise regressors is shown to produce a reduction of 19%. The RETROICOR model obtained a variance reduction of 6.4%. Our HRF simulation showed that the mean-squared error (MSE) between the recovered and true HRFs is reduced by 21% on average when 10 NIRS channels are applied and by introducing an optimized time lag between the NIRS and fMRI time series, a single NIRS channel can provide an average MSE reduction of 14%. The RETROICOR model did not provide a significant change in MSE. By each of the metrics calculated, NIRS recording is shown to be of significant benefit to the regression of low-frequency physiological noise from fMRI data.