The effect of physiological noise in phase functional magnetic resonance imaging: from blood oxygen level-dependent effects to direct detection of neuronal currents

Recently, the possibility to use both magnitude and phase image sets for the statistical evaluation of fMRI has been proposed, with the prospective of increasing both statistical power and the spatial specificity. In the present work, several issues that affect the spatial and temporal stability in fMRI phase time series in the presence of physiologic noise processes are reviewed, discussed and illustrated by experiments performed at 3 T. The observed phase value is a fingerprint of the underlying voxel averaged magnetic field variations. Those related to physiological processes can be considered static or dynamic in relation to the temporal scale of a 2D acquisition and will play out on different spatial scales as well: globally across the entire images slice, and locally depending on the constituents and their relative fractions inside the MRI voxel. The 'static' respiration-induced effects lead to magneto-mechanic scan-to-scan variations in the global magnetic field but may also contribute to local BOLD fluctuations due to respiration-related variations in arterial carbon dioxide. Likewise, the 'dynamic' cardiac-related effects will lead to global susceptibility effects caused by pulsatile motion of the brain as well as local blood pressure-related changes in BOLD and changes in blood flow velocity. Finally, subject motion may lead to variations in both local and global tissue susceptibility that will be especially pronounced close to air cavities. Since dissimilar manifestations of physiological processes can be expected in phase and in magnitude images, a direct relationship between phase and magnitude scan-to-scan fluctuations cannot be assumed a priori. Therefore three different models were defined for the phase stability, each dependent on the relation between phase and magnitude variations and the best will depend on the underlying noise processes. By experiments on healthy volunteers at rest, we showed that phase stability depends on the type of post-processing and can be improved by reducing the low-frequency respiration-induced mechano-magnetic effects. Although the manifestations of physiological noise were in general more pronounced in phase than in magnitude images, due to phase wraps and global Bo effects, we suggest that a phase stability similar to that found in magnitude could theoretically be achieved by adequate correction methods. Moreover, as suggested by our experimental data regarding BOLD-related phase effects, phase stability could even supersede magnitude stability in voxels covering dense microvascular networks with BOLD-related fluctuations as the dominant noise contributor. In the interest of the quality of both BOLD-based and nc-MRI methods, future studies are required to find alternative methods that can improve phase stability, designed to match the temporal and spatial scale of the underlying neuronal activity.     

Respiratory noise correction using phase information

Respiratory noise is a confounding factor in functional magnetic resonance imaging (MRI) data analysis. A novel method called Respiratory noise Correction using Phase information is proposed to retrospectively correct for the respiratory noise in functional MRI (fMRI) time series. It is demonstrated that the respiratory movement and the phase of functional MRI images are highly correlated in time. The signal fluctuation due to respiratory movements can be effectively estimated from the phase variation and removed from the functional MRI time series using a Wiener filtering technique. In our experiments, this new method is compared with RETROICOR, which requires recording respiration signal simultaneously in an fMRI experiment. The two techniques show comparable performance with respect to the respiratory noise correction for fMRI time series. However, this technique is more advantageous because there is no need for monitoring the subjects’ respiration or changing functional MRI protocols. This technique is also potentially useful for correcting respiratory noise from abnormal breathing or when the respiration is not periodic.     

Separation of physiological very low frequency fluctuation from aliasing by switched sampling interval fMRI scans

Anesthetized children have dominant blood-oxygen-level-dependent (BOLD) signal sources presenting high-power fluctuations at very low frequencies (VLF <0.05 Hz). Aliasing of frequencies higher than critically sampled has been regarded as one probable origin of the VLF fluctuations. Aliased signal frequencies change when the sampling rate of the data is altered. In this study, the aliasing of VLF BOLD signal fluctuation was analysed by switching the repetition time (TR) of magnetic resonance (MR) images. Eleven anesthetized children were imaged at 1.5 T using TRs of 500 and 1200 ms. The BOLD signal sources were separated with independent component analysis (ICA). Occipital cortex signal sources had nonaliased VLF fluctuation ( approximately 0.03 Hz) in 9 of 11 subjects. Arterial signal sources failed to present stable power peaks at frequencies lower than 0.42 Hz presumably due to aliasing. Cerebrospinal fluid (CSF)-related signal sources showed nonaliased VLF in four subjects. In conclusion, the VLF BOLD signal fluctuation in the occipital cortex is a true physiological fluctuation, not a result of signal aliasing.     

A kernel machine-based fMRI physiological noise removal method

Functional magnetic resonance imaging (fMRI) technique with blood oxygenation level dependent (BOLD) contrast is a powerful tool for noninvasive mapping of brain function under task and resting states. The removal of cardiac- and respiration-induced physiological noise in fMRI data has been a significant challenge as fMRI studies seek to achieve higher spatial resolutions and characterize more subtle neuronal changes. The low temporal sampling rate of most multi-slice fMRI experiments often causes aliasing of physiological noise into the frequency range of BOLD activation signal. In addition, changes of heartbeat and respiration patterns also generate physiological fluctuations that have similar frequencies with BOLD activation. Most existing physiological noise-removal methods either place restrictive limitations on image acquisition or utilize filtering or regression based post-processing algorithms, which cannot distinguish the frequency-overlapping BOLD activation and the physiological noise. In this work, we address the challenge of physiological noise removal via the kernel machine technique, where a nonlinear kernel machine technique, kernel principal component analysis, is used with a specifically identified kernel function to differentiate BOLD signal from the physiological noise of the frequency. The proposed method was evaluated in human fMRI data acquired from multiple task-related and resting state fMRI experiments. A comparison study was also performed with an existing adaptive filtering method. The results indicate that the proposed method can effectively identify and reduce the physiological noise in fMRI data. The comparison study shows that the proposed method can provide comparable or better noise removal performance than the adaptive filtering approach.     

On the origin of respiratory artifacts in BOLD-EPI of the human brain

BOLD-based functional MRI (fMRI) can be used to explicitly measure hemodynamic aspects and functions of human neuro-physiology. As fMRI measures changes in regional cerebral blood flow and volume as well as blood oxygenation, rather than neuronal brain activity directly, other processes that may change the above parameters have to be examined closely to assess sensitivity and specificity of fMRI results. Physiological processes that can cause artifacts include cardiac action, breathing and vasomotion. Although there has been substantial research on physiological artifacts and appropriate compensation methods, controversy still remains on the mechanisms that cause the fMRI signal fluctuations. Respiratory-correlated fluctuations may either be induced by changes of the magnetic field homogeneity due to moving organs, intra-thoracic pressure differences, respiration-dependent vasodilation or oxygenation differences. The aim of this study was to characterize the impact of different breathing patterns by varying respiration frequency and/or tidal volume on EPI time courses of the resting human brain. The amount of respiration-related oscillations during three respiration patterns was quantified, and statistically significant differences were obtained in white matter only: p < 0.03 between 6 vs. 12 ml/kg body weight end tidal volume at a respiration frequency of 15/min, p < 0.03 between 12 vs. 6 ml/kg body weight and 15 vs. 10 respiration cycles/min. There was no significant difference between 15 vs. 10 respiration cycles/min at an end tidal volume of 6 ml/kg body weight (p = 0.917). In addition, the respiration-affected brain regions were very similar with EPI readout in the a-p and l-r direction. Based on our results and published literature we hypothesize that venous oxygenation oscillations due to changing intra-thoracic pressure represent a major factor for respiration-related signal fluctuations and increase significantly with increasing end tidal volume in white matter only.     

Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study

Signal fluctuations in functional magnetic resonance imaging (fMRI) can result from a number of sources that may have a neuronal, physiologic or instrumental origin. To determine the relative contribution of these sources, we recorded physiological (respiration and cardiac) signals simultaneously with fMRI in human volunteers at rest with their eyes closed. State-of-the-art technology was used including high magnetic field (7 T), a multichannel detector array and high-resolution (3 mm³) echo-planar imaging. We investigated the relative contribution of thermal noise and other sources of variance to the observed fMRI signal fluctuations both in the visual cortex and in the whole brain gray matter. The following sources of variance were evaluated separately: low-frequency drifts due to scanner instability, effects correlated with respiratory and cardiac cycles, effects due to variability in the respiratory flow rate and cardiac rate, and other sources, tentatively attributed to spontaneous neuronal activity. We found that low-frequency drifts are the most significant source of fMRI signal fluctuations (3.0% signal change in the visual cortex, TE=32 ms), followed by spontaneous neuronal activity (2.9%), thermal noise (2.1%), effects due to variability in physiological rates (respiration 0.9%, heartbeat 0.9%), and correlated with physiological cycles (0.6%). We suggest the selection and use of four lagged physiological noise regressors as an effective model to explain the variance related to fluctuations in the rates of respiration volume change and cardiac pulsation. Our results also indicate that, compared to the whole brain gray matter, the visual cortex has higher sensitivity to changes in both the rate of respiration and the spontaneous resting-state activity. Under the conditions of this study, spontaneous neuronal activity is one of the major contributors to the measured fMRI signal fluctuations, increasing almost twofold relative to earlier experiments under similar conditions at 3 T.     

Comparison of α-chloralose,medetomidine and isoflurane anesthesia for functional connectivity mapping in the rat

Functional connectivity measures based upon low-frequency blood-oxygenation-level-dependent functional magnetic resonance imaging (BOLD fMRI) signal fluctuations have become a widely used tool for investigating spontaneous brain activity in humans. Still unknown, however, is the precise relationship between neural activity, the hemodynamic response and fluctuations in the MRI signal. Recent work from several groups had shown that correlated low-frequency fluctuations in the BOLD signal can be detected in the anesthetized rat — a first step toward elucidating this relationship. Building on this preliminary work, through this study, we demonstrate that functional connectivity observed in the rat depends strongly on the type of anesthesia used. Power spectra of spontaneous fluctuations and the cross-correlation-based connectivity maps from rats anesthetized with α-chloralose, medetomidine or isoflurane are presented using a high-temporal-resolution imaging sequence that ensures minimal contamination from physiological noise. The results show less localized correlation in rats anesthetized with isoflurane as compared with rats anesthetized with α-chloralose or medetomidine. These experiments highlight the utility of using different types of anesthesia to explore the fundamental physiological relationships of the BOLD signal and suggest that the mechanisms contributing to functional connectivity involve a complicated relationship between changes in neural activity, neurovascular coupling and vascular reactivity.     

The effects of respiratory CO2 fluctuations in the resting-state BOLD signal differ between eyes open and eyes closed

Resting fluctuations in arterial CO₂ (a cerebral vasodilator) are believed to be an important source of low-frequency blood oxygenation level dependent (BOLD) signal fluctuations. In this study we focus on the two commonly used resting-states in functional magnetic resonance imaging experiments, eyes open and eyes closed, and quantify the degree to which measured spontaneous fluctuations in the partial pressure of end-tidal CO₂ (Pet_co2) relate to BOLD signal time series. A significantly longer latency of BOLD signal changes following Pet_co2 fluctuations was found in the eyes closed condition compared to with eyes open, which may reveal different intrinsic vascular response delays in CO₂ reactivity or an alteration in the net BOLD signal arising from Pet_co2 fluctuations and altered neural activity with eyes closed. By allowing a spatially varying time delay for the compensation of this temporal difference, a more spatially consistent CO₂ correlation map can be obtained. Finally, Granger-causality analysis demonstrated a “causal” relationship between Pet_co2 and BOLD. The identified dominant Pet_co2→BOLD directional coupling supports the notion that Pet_co2 fluctuations are indeed a cause of resting BOLD variance in the majority of subjects.     

SNR and functional sensitivity of BOLD and perfusion-based fMRI using arterial spin labeling with spiral SENSE at 3 T

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies using parallel imaging to reduce the readout window have reported a loss in temporal signal-to-noise ratio (SNR) that is less than would be expected given a purely thermal noise model. In this study, the impact of parallel imaging on the noise components and functional sensitivity of both BOLD and perfusion-based fMRI data was investigated. Dual-echo arterial spin labeling data were acquired on five subjects using sensitivity encoding (SENSE), at reduction factors (R) of 1, 2 and 3. Direct recording of cardiac and respiratory activity during data acquisition enabled the retrospective removal of physiological noise. The temporal SNR of the perfusion time series closely followed the thermal noise prediction of a √R loss in SNR as the readout window was shortened, with temporal SNR values (relative to the R=1 data) of 0.72 and 0.56 for the R=2 and R=3 data, respectively, after accounting for physiological noise. However, the BOLD temporal SNR decreased more slowly than predicted even after accounting for physiological noise, with relative temporal SNR values of 0.80 and 0.63 for the R=2 and R=3 data, respectively. Spectral analysis revealed that the BOLD trends were dominated by low-frequency fluctuations, which were not dominant in the perfusion data due to signal processing differences. The functional sensitivity, assessed using mean F values over activated regions of interest (ROIs), followed the temporal SNR trends for the BOLD data. However, results for the perfusion data were more dependent on the threshold used for ROI selection, most likely due to the inherently low SNR of functional perfusion data.     

颅脑梯度回波成像:呼吸伪影和导航回波矫正

梯度回波序列是磁共振成像中常用的脉冲序列,然而梯度回波对主磁场波动非常敏感,呼吸等生理运动引起的信号波动会导致图像伪影.该文报道了采用导航回波技术获取呼吸运动导致的局部磁场波动,用以矫正图像回波中随时间变化的相位波动,并将该技术应用于三维多回波梯度回波成像和T2*定量图研究.研究结果显示:矫正前,相位波动幅度随回波时间增长而增大,模图和T2*定量图在相位编码方向有明显伪影,并且男女呼吸伪影水平有显著性差异;矫正后,相位波动幅度大幅下降,图像伪影水平有显著性下降.     

Comparison between end-tidal CO₂ and respiration volume per time for detecting BOLD signal fluctuations during paced hyperventilation

Respiratory motion and capnometry monitoring were performed during blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) of the brain while a series of paced hyperventilation tasks were performed that caused significant hypocapnia. Respiration volume per time (RVT) and end-tidal carbon dioxide (ETCO(2)) were determined and compared for their ability to explain BOLD contrast changes in the data. A 35% decrease in ETCO(2) was observed along with corresponding changes in RVT. A best-fit ETCO(2) response function, with an average initial peak delay time of 12 s, was empirically determined. ETCO(2) data convolved with this response function was more strongly and prevalently correlated to BOLD signal changes than RVT data convolved with the corresponding respiration response function. The results suggest that ETCO(2) better models BOLD signal fluctuations in fMRI experiments with significant transient hypocapnia. This is due to hysteresis in the ETCO(2) response when moving from hypocapnia to normocapnia, compared to moving from normocapnia to hypocapnia.     

Spontaneous low-frequency blood oxygenation level-dependent fluctuations and functional connectivity analysis of the 'resting' brain

Functional magnetic resonance imaging techniques using the blood oxygenation level-dependent (BOLD) contrast are widely used to map human brain function by relating local hemodynamic responses to neuronal stimuli compared to control conditions. There is increasing interest in spontaneous cerebral BOLD fluctuations that are prominent in the low-frequency range (<0.1 Hz) and show intriguing spatio-temporal correlations in functional networks. The nature of these signal fluctuations remains unclear, but there is accumulating evidence for a neural basis opening exciting new avenues to study human brain function and its connectivity at rest. Moreover, an increasing number of patient studies report disease-dependent variation in the amplitude and spatial coherence of low-frequency BOLD fluctuations (LFBF) that may afford greater diagnostic sensitivity and easier clinical applicability than standard fMRI. The main disadvantage of this emerging tool relates to physiological (respiratory, cardiac and vasomotion) and motion confounds that are challenging to disentangle requiring thorough preprocessing. Technical aspects of functional connectivity fMRI analysis and the neuroscientific potential of spontaneous LFBF in the default mode and other resting-state networks have been recently reviewed. This review will give an update on the current knowledge of the nature of LFBF, their relation to physiological confounds and potential for clinical diagnostic and pharmacological studies.     

Decoding neural events from fMRI BOLD signal: A comparison of existing approaches and development of a new algorithm

Neuroimaging methodology predominantly relies on the blood oxygenation level dependent (BOLD) signal. While the BOLD signal is a valid measure of neuronal activity, variances in fluctuations of the BOLD signal are not only due to fluctuations in neural activity. Thus, a remaining problem in neuroimaging analyses is developing methods that ensure specific inferences about neural activity that are not confounded by unrelated sources of noise in the BOLD signal. Here, we develop and test a new algorithm for performing semiblind (i.e., no knowledge of stimulus timings) deconvolution of the BOLD signal that treats the neural event as an observable, but intermediate, probabilistic representation of the system's state. We test and compare this new algorithm against three other recent deconvolution algorithms under varied levels of autocorrelated and Gaussian noise, hemodynamic response function (HRF) misspecification and observation sampling rate. Further, we compare the algorithms' performance using two models to simulate BOLD data: a convolution of neural events with a known (or misspecified) HRF versus a biophysically accurate balloon model of hemodynamics. We also examine the algorithms' performance on real task data. The results demonstrated good performance of all algorithms, though the new algorithm generally outperformed the others (3.0% improvement) under simulated resting-state experimental conditions exhibiting multiple, realistic confounding factors (as well as 10.3% improvement on a real Stroop task). The simulations also demonstrate that the greatest negative influence on deconvolution accuracy is observation sampling rate. Practical and theoretical implications of these results for improving inferences about neural activity from fMRI BOLD signal are discussed.     

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.     

Evaluation of preprocessing steps to compensate for magnetic field distortions due to body movements in BOLD fMRI

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is currently the dominant technique for non-invasive investigation of brain functions. One of the challenges with BOLD fMRI, particularly at high fields, is compensation for the effects of spatiotemporally varying magnetic field inhomogeneities (ΔB₀) caused by normal subject respiration and, in some studies, movement of the subject during the scan to perform tasks related to the functional paradigm. The presence of ΔB₀ during data acquisition distorts reconstructed images and introduces extraneous fluctuations in the fMRI time series that decrease the BOLD contrast-to-noise ratio. Optimization of the fMRI data-processing pipeline to compensate for geometric distortions is of paramount importance to ensure high quality of fMRI data. To investigate ΔB₀ caused by subject movement, echo-planar imaging scans were collected with and without concurrent motion of a phantom arm. The phantom arm was constructed and moved by the experimenter to emulate forearm motions while subjects remained still and observed a visual stimulation paradigm. These data were then subjected to eight different combinations of preprocessing steps. The best preprocessing pipeline included navigator correction, a complex phase regressor and spatial smoothing. The synergy between navigator correction and phase regression reduced geometric distortions better than either step in isolation and preconditioned the data to make them more amenable to the benefits of spatial smoothing. The combination of these steps provided a 10% increase in t-statistics compared to only navigator correction and spatial smoothing and reduced the noise and false activations in regions where no legitimate effects would occur.     

Subfrequency noise signal extraction in fiber-optic strain sensors using postprocessing

Laser frequency fluctuations typically limit the performance of high-resolution interferometric fiber strain sensors. Using time delay interferometry, we demonstrate a frequency noise immune fiber sensing system, where strain signals were extracted well below the noise floor normally imposed by the frequency fluctuations of the laser. Initial measurements show a reduction in the noise floor by a factor of 30, with strain sensitivities of a nanostrain/Hz at 100 mHz and reaching 100 ps/Hz at 1 Hz. Further characterization of the system indicates the potential for at least 4.5 orders of magnitude frequency fluctuation rejection.     

Initial attempts at directly detecting alpha wave activity in the brain using MRI

The “direct detection” of neuronal activity by MRI could offer improved spatial and temporal resolution compared to the blood oxygenation level-dependent (BOLD) effect. Here we describe initial attempts to use MRI to detect directly the neuronal currents resulting from spontaneous alpha wave activity, which have previously been shown to generate the largest extracranial magnetic fields. Experiments were successfully carried out on four subjects at 3 T. A single slice was imaged at a rate of 25 images per second under two conditions. The first (in darkness with eyes-closed) was chosen to promote alpha wave activity, while the second (eyes-open viewing a visual stimulus) was chosen to suppress it. The fluctuations of the phase and magnitude of the resulting MR image data were frequency analysed, and tested for the signature of both alpha wave activity and neuronal activity evoked by the visual stimulus.

Regions were found that consistently showed elevated power in fluctuations of the phase of the MR signal, in the frequency range of alpha waves, during the eyes-closed condition. It was conservatively assumed that if oscillations occurred at the same frequency in the magnitude signal from the same region or at the same frequency in the phase or magnitude signal from other regions overlying large vessels or cerebrospinal fluid (CSF), then the phase changes were not due to neuronal activity related to alpha waves. Using these criteria the data obtained were consistent with direct detection of alpha wave activity in three of the four volunteers. No significant MR signal fluctuations due to evoked activity were identified.     

The impact of physiologic noise correction applied to functional MRI of pain at 1.5 and 3.0 T

This study quantified the impact of the well-known physiologic noise correction algorithm RETROICOR applied to a pain functional magnetic resonance imaging (FMRI) experiment at two field strengths: 1.5 and 3.0 T. In the 1.5-T acquisition, there was an 8.2% decrease in time course variance (σ) and a 227% improvement in average model fit (increase in mean R²_a). In the 3.0-T acquisition, significantly greater improvements were seen: a 10.4% decrease in σ and a 240% increase in mean R²_a. End-tidal carbon dioxide data were also collected during scanning and used to account for low-frequency changes in cerebral blood flow; however, the impact of this correction was trivial compared to applying RETROICOR. Comparison between two implementations of RETROICOR demonstrated that oversampled physiologic data can be applied by either downsampling or modification of the timing in the RETROICOR algorithm, with equivalent results. Furthermore, there was no significant effect from manually aligning the physiologic data with corresponding image slices from an interleaved acquisition, indicating that RETROICOR accounts for timing differences between physiologic changes and MR signal changes. These findings suggest that RETROICOR correction, as it is commonly implemented, should be included as part of the data analysis for pain FMRI studies performed at 1.5 and 3.0 T.     

Characterization of cardiac-related noise in fMRI of the cervical spinal cord

Magnetic resonance imaging (MRI) has recently been applied to study spinal cord function in humans. However, spinal functional MRI (fMRI) encounters major technical challenges with cardiac noise being considered a major source of noise. The present study relied on echo-planar imaging of the cervical cord at short TR (TR=250 ms; TE=40 ms; flip=45 degrees), combined with plethysmographic recordings to characterize the spatiotemporal properties of cardiac-induced signal changes in spinal fMRI. Frequency-based analyses examining signal change at the cardiac frequency confirmed mean fluctuations of about 10% (relative to the mean signal) in the spinal cord and surrounding cerebrospinal fluid (CSF), with maximal responses reaching up to 66% in some voxels. A spatial independent component analysis (sICA) confirmed that cardiac noise is an important source of variance in spinal fMRI with several components showing a response coherent with the cardiac frequency spectrum. The time course of the main cardiac components approximated a sinusoidal function tightly coupled to the cardiac systole with at least one component showing a comparable temporal profile across runs and subjects. Spatially, both the frequency-domain analysis and the sICA demonstrated cardiac noise distributed irregularly along the full rostrocaudal extent of the segments scanned with peaks concentrated in the ventral part of the lateral slices in all scans and subjects, consistent with the major channels of CSF flow. These results confirm that cardiac-induced changes are a significant source of noise likely to affect the detection of spinal Blood Oxygen Level Dependent (BOLD) responses. Most importantly, the complex spatiotemporal structure of cardiac noise is unlikely to be accounted for adequately by ad hoc linear methods, especially in data acquired using long TR (i.e. aliasing the cardiac frequency). However, the reliable spatiotemporal distribution of cardiac noise across scanning runs and within subjects may provide a valid means to identify and extract cardiac noise based on sICA methods.     

The comparative effects of signal sensation level and sound-pressure level on interaural time discrimination

A series of experiments was performed to examine the extent to which precision of interaural time discrimination depends on the sound-pressure level (SPL) and/or sensation level (SL) of the signal. All experiments used a tone burst signal and a continuous white noise masker, which was either diotic or interaurally phase reversed. Results of the first experiment indicate that (1) at equal signal SLs, interaural time and intensity discrimination is more precise when measured with the added diotic noise, and (2) addition of the phase reversed noise, previously shown to cause less precise interaural time discrimination, has a similar effect on interaural intensity discrimination. In the second experiment, interaural time JNDs for a signal of constant SPL were measured as a function of noise level. Results show that a low-level diotic noise can benefit interaural time discrimination, particularly at 500 Hz. The third and fourth experiments were performed to measure interaural time discrimination as a function of increasing signal SPL but constant signal-to-noise ratio. The data show the JND decreasing with increasing signal SPL at nearly the same rate with or without the added noise, indicating that an increase in signal-to-noise ratio is not necessary for improved discrimination.