Physiological Noise Research Articles

The stray magnetic fields in Magnetic Resonance Current Density Imaging (MRCDI)

PurposeMR Current Density Imaging (MRCDI) involves weak current-injection into the head. The resulting magnetic field changes are measured by MRI. Stray fields pose major challenges since these can dominate the fields caused by tissue currents. We analyze the sources and influences of stray fields. MethodsFirst, we supply validation data for a recently introduced MRCDI method with an unprecedented noise floor of ∼0.1 nT in vivo. Second, we assess the accuracy limit of the method and our corresponding cable current correction in phantoms ensuring high signal-to-noise ratio (SNR). Third, we simulate the influence of stray fields on current flow reconstructions for various realistic experimental set-ups. Fourth, we experimentally determine the physiological field variations. Finally, we explore the consequences of head positioning in an exemplary head coil, since off-center positioning provides space for limiting cable-induced fields. ResultsThe cable correction method performs well except near the cables. Unless correcting for cable currents, the reconstructed current flow is easily misestimated by up to 45% for a realistic experimental set-up. Stray fields dominating the fields caused by tissue currents can occur, e.g. due to a wire segment 20 cm away from the imaged region, or due to a slight cable misalignment of 3°. The noise is increased by 40% due to physiological factors. Minor patient movements can cause field changes of ∼40 nT. Off-centered head positioning can locally reduce SNR by e.g. 30%. ConclusionsQuantification of stray fields showed that MRCDI requires careful field correction. After cable correction, physiological noise is a limiting factor.

No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI

As a consequence of recent technological advances in the field of functional magnetic resonance imaging (fMRI), results can now be made available in real-time. This allows for novel applications such as online quality assurance of the acquisition, intra-operative fMRI, brain-computer-interfaces, and neurofeedback. To that aim, signal processing algorithms for real-time fMRI must reliably correct signal contaminations due to physiological noise, head motion, and scanner drift. The aim of this study was to compare performance of the commonly used online detrending algorithms exponential moving average (EMA), incremental general linear model (iGLM) and sliding window iGLM (iGLMwindow). For comparison, we also included offline detrending algorithms (i.e., MATLAB's and SPM8's native detrending functions). Additionally, we optimized the EMA control parameter, by assessing the algorithm's performance on a simulated data set with an exhaustive set of realistic experimental design parameters. First, we optimized the free parameters of the online and offline detrending algorithms. Next, using simulated data, we systematically compared the performance of the algorithms with respect to varying levels of Gaussian and colored noise, linear and non-linear drifts, spikes, and step function artifacts. Additionally, using in vivo data from an actual rt-fMRI experiment, we validated our results in a post hoc offline comparison of the different detrending algorithms. Quantitative measures show that all algorithms perform well, even though they are differently affected by the different artifact types. The iGLM approach outperforms the other online algorithms and achieves online detrending performance that is as good as that of offline procedures. These results may guide developers and users of real-time fMRI analyses tools to best account for the problem of signal drifts in real-time fMRI.

Investigation of Resting-State BOLD Networks in the Human Brainstem and Spinal Cord

Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to investigate networks within the cortex and has also provided some insight into the networks present in the brainstem (BS) and spinal cord (SC). The purpose of this study was to investigate resting-state blood oxygenation-level dependent (BOLD) fluctuations in the BS/SC and to identify resting-state networks (RSNs) across these regions. Resting-state BOLD fMRI data were obtained from the entire BS and cervical SC in 16 healthy participants, at 3 T, with T2-weighted single-shot fast spin-echo imaging. Data were spatially normalized and processed to remove physiological noise. Cluster-cluster functional connectivity was investigated across the entire 3D region by means of temporal correlations, and structural equation modeling (SEM) was used to investigate RSNs. Extensive connectivity was observed within and across BS and SC regions, with connections spanning up to 120 mm, although shorter connections were more prevalent. SEM results revealed extensive brainstem-cord connectivity that included specific anatomical regions within the brainstem. The results indicate the presence of a complex resting-state network which is highly interconnected in the spinal cord. Known anatomical connections between cortical and BS regions support the conclusion that the observed resting-state BOLD fluctuations in the BS/SC may be related to autonomic regulation. Future studies are required to further investigate these resting-state BOLD networks.

On the analysis of rapidly sampled fMRI data

Recent advances in parallel imaging and simultaneous multi-slice techniques have permitted whole-brain fMRI acquisitions at sub-second sampling intervals, without significantly sacrificing the spatial coverage and resolution. Apart from probing brain function at finer temporal scales, faster sampling rates may potentially lead to enhanced functional sensitivity, owing possibly to both cleaner neural representations (due to less aliased physiological noise) and additional statistical benefits (due to more degrees of freedom for a fixed scan duration). Accompanying these intriguing aspects of fast acquisitions, however, confusion has also arisen regarding (1) how to preprocess/analyze these fast fMRI data, and (2) what exactly is the extent of benefits with fast acquisitions, i.e., how fast is fast enough for a specific research aim? The first question is motivated by the altered spectral distribution and noise characteristics at short sampling intervals, while the second question seeks to reconcile the complicated trade-offs between the functional contrast-to-noise ratio and the effective degrees of freedom. Although there have been recent efforts to empirically approach different aspects of these two questions, in this work we discuss, from a theoretical perspective accompanied by some illustrative, proof-of-concept experimental in vivo human fMRI data, a few considerations that are rarely mentioned, yet are important for both preprocessing and optimizing statistical inferences for studies that employ acquisitions with sub-second sampling intervals. Several summary recommendations include concerns regarding advisability of relying on low-pass filtering to de-noise physiological contributions, employment of statistical models with sufficient complexity to account for the substantially increased serial correlation, and cautions regarding using rapid sampling to enhance functional sensitivity given that different analysis models may associate with distinct trade-offs between contrast-to-noise ratios and the effective degrees of freedom. As an example, we demonstrate that as TR shortens, the intrinsic differences in how noise is accommodated in general linear models and Pearson correlation analyses (assuming Gaussian distributed stochastic signals and noise) can result in quite different outcomes, either gaining or losing statistical power.

Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis

Resting state functional magnetic resonance imaging is used to study how brain regions are functionally connected by measuring temporal correlation of the fMRI signals, when a subject is at rest. Sparse dictionary learning is used to estimate a dictionary of resting state networks by decomposing the whole brain signals into several temporal features (atoms), each being shared by a set of voxels associated to a network. Recently, we proposed and validated a new method entitled Sparsity-based Analysis of Reliable K-hubness (SPARK), suggesting that connector hubs of brain networks participating in inter-network communication can be identified by counting the number of atoms involved in each voxel (sparse number k). However, such hub analysis can be corrupted by the presence of noise-related atoms, where physiological fluctuations in cardiorespiratory processes may remain even after band-pass filtering and regression of confound signals from the white matter and cerebrospinal fluid. Handling this issue might require manual classification of noisy atoms, which is a time-consuming and subjective task. Motivated by the fact that the physiological fluctuations are often localized in tissues close to large vasculatures, i.e. sagittal sinus, we propose an automatic classification of physiological noise-related atoms for SPARK using spatial priors and a stepwise regression procedure. We measured the degree to which the noise-characteristic time-courses within the mask are explained by each atom, and classified noise-related atoms using a subject-specific threshold estimated using a bootstrap resampling based strategy. Using real data from healthy subjects (N = 25), manual classification of the atoms by two independent reviewers showed the presence of sagittal sinus related noise in 65% of the runs. Applying the same manual classification after the proposed automatic removal method reduced this rate to 19%. A 10-fold cross-validation on real data showed good specificity and accuracy of the proposed automated method in classifying the target noise (area under the ROC curve= 0.89), when compared to the manual classification considered as the reference. We demonstrated decrease in k-hubness values in the voxels involved in the sagittal sinus at both individual and group levels, suggesting a significant improvement of SPARK, which is particularly important when considering clinical applications.

Fast Robust Dejitter and Interslice Discontinuity Removal in MRI Phase Acquisitions: Application to Magnetic Resonance Elastography.

MRI phase contrast imaging methods that assemble slice-wise acquisitions into volumes can contain interslice phase discontinuities (IPDs) over the course of the scan from sources, including unavoidable physiological activity. In magnetic resonance elastography (MRE), this can alter wavelength and tissue stiffness estimates, invalidating the analysis. We first model this behavior as jitter along the z-axis of the phase of 3D complex-valued wave volumes. A two-step image processing pipeline is then proposed that removes IPDs. First, constant slicewise phase shift is removed with a novel, non-convex dejittering algorithm. Then, regional physiological noise artifacts are removed with novel filtering of 3D wavelet coefficients. Calibration of two pipeline coefficients, the dejitter parameter α and the wavelet band high-pass coefficient ωc , was first performed on a finite-element method brain phantom. A comparative investigation was then performed, on a cohort of 48 brain acquisitions, of four approaches to IPDs: 1) the proposed method; 2) a "control" condition of neglect of IPDs; 3) an anisotropic wavelet-based method; and 4) a method of in-plane (2D) processing. The present method showed medians of [Formula: see text] Pa for a multifrequency wave inversion centered at 40 Hz which was within 6% of methods 3) and 4), while neglect produced [Formula: see text] estimates a mean of 17% lower. The proposed method reduced the value range of the cohort against methods 3) and 4) by 29% and 31%, respectively. Such reduction in variance enhances the ability of brain MRE to predict subtler physiological changes. Our theoretical approach further enables more powerful applications of fundamental findings in noise and denoising to MRE.

Temporal dynamics of the pharmacological MRI response to subanaesthetic ketamine in healthy volunteers: A simultaneous EEG/fMRI study

Pharmacological magnetic resonance imaging has been used to investigate the neural effects of subanaesthetic ketamine in healthy volunteers. However, the effect of ketamine has been modelled with a single time course and without consideration of physiological noise. This study aimed to investigate ketamine-induced alterations in resting neural activity using conventional pharmacological magnetic resonance imaging analysis techniques with physiological noise correction, and a novel analysis utilising simultaneously recorded electroencephalography data. Simultaneous electroencephalography/functional magnetic resonance imaging and physiological data were collected from 30 healthy male participants before and during a subanaesthetic intravenous ketamine infusion. Consistent with previous literature, we show widespread cortical blood-oxygen-level dependent signal increases and decreased blood-oxygen-level dependent signals in the subgenual anterior cingulate cortex following ketamine. However, the latter effect was attenuated by the inclusion of motion regressors and physiological correction in the model. In a novel analysis, we modelled the pharmacological magnetic resonance imaging response with the power time series of seven electroencephalography frequency bands. This showed evidence for distinct temporal time courses of neural responses to ketamine. No electroencephalography power time series correlated with decreased blood-oxygen-level dependent signal in the subgenual anterior cingulate cortex. We suggest the decrease in blood-oxygen-level dependent signals in the subgenual anterior cingulate cortex typically seen in the literature is the result of physiological noise, in particular cardiac pulsatility. Furthermore, modelling the pharmacological magnetic resonance imaging response with a single temporal model does not completely capture the full spectrum of neuronal dynamics. The use of electroencephalography regressors to model the response can increase confidence that the pharmacological magnetic resonance imaging is directly related to underlying neural activity.

Performance of source imaging techniques of spatially extended generators of uterine activity

Abstract Preterm birth (PTB) is one of the most important complications in pregnancy. Reliable diagnosis means are lacking and the underlying physiological mechanisms are unclear. Determining the location of various correlated and simultaneously active uterus sources from abdominal ElectroHysterogram (EHG) recordings and extracting the corresponding uterus signals is a challenging problem. The use of the EHG for imaging the sources of the uterine electrical activity could be a new and powerful diagnosis technique. In this paper we compare the ability of six distributed source localization methods to recover extended sources of uterus activity from abdominal EHG. As no gold standard to evaluate source localization methods, exists, we perform our evaluation by using a well-controlled realistic simulations of EHG signals, involving several locations. Simulated data were corrupted by physiological EHG noise. The performance of several state-of-the-art techniques for extended source localization is evaluated using a detection accuracy index using the Dipole Localization Error, the Area Under the Receiver Operating Characteristic (ROC) Curve AUC, and the Correlation Coefficient.

Flow stabilization in wearable microfluidic sensors enables noise suppression.

Dilatometric strain sensors (DSS) that work based on detection of volume change in microfluidic channels; i) are highly sensitive to biaxial strain, ii) can be fabricated using only soft and transparent materials, and iii) are easy to integrate with smart-phones. These features are especially attractive for contact lens based intraocular pressure (IOP) sensing applications. The inherent flow stabilization of the microfluidic systems is an additional advantage suitable for filtering out rapid fluctuations. Here, we have demonstrated that the low-pass filtering in microfluidic sensors improves the signal-to-noise-ratio for ophthalmic applications. We have fabricated devices with a time constant in the range of 1-200 seconds. We have demonstrated that the device architecture and working liquid viscosity (10-866 cSt) are the two independent factors that determine the sensor time constant. We have developed an equivalent circuit model for the DSS that accurately represents the experimental results thus can be used as a computational model for design and development of microfluidic sensors. For a sensor with the time constant of 4 s, we report that microfluidic signal filtering in IOP monitoring applications can suppress the rapid fluctuations (i.e., the noise due to ocular pulsation, blinking etc.) by 9 dB without the need for electronic components.

Disambiguating the role of blood flow and global signal with Partial Information Decomposition

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

Financial gain- and loss-related BOLD signals in the human ventral tegmental area and substantia nigra pars compacta.

Neurons in the ventral tegmental area (VTA) and substantia nigra pars compacta (SNC) play central roles in reward‐related behaviours. Nonhuman animal studies suggest that these neurons also process aversive events. However, our understanding of how the human VTA and SNC responds to such events is limited and has been hindered by the technical challenge of using functional magnetic resonance imaging (fMRI) to investigate a small structure where the signal is particularly vulnerable to physiological noise. Here we show, using methods optimized specifically for the midbrain (including high‐resolution imaging, a novel registration protocol, and physiological noise modelling), a BOLD (blood‐oxygen‐level dependent) signal to both financial gain and loss in the VTA and SNC, along with a response to nil outcomes that are better or worse than expected in the VTA. Taken together, these findings suggest that the human VTA and SNC are involved in the processing of both appetitive and aversive financial outcomes in humans.

Adaptive filtering of physiological noises in fNIRS data

The study presents a recursive least-squares estimation method with an exponential forgetting factor for noise removal in functional near-infrared spectroscopy data and extraction of hemodynamic responses (HRs) from the measured data. The HR is modeled as a linear regression form in which the expected HR, the first and second derivatives of the expected HR, a short-separation measurement data, three physiological noises, and the baseline drift are included as components in the regression vector. The proposed method is applied to left-motor-cortex experiments on the right thumb and little finger movements in five healthy male participants. The algorithm is evaluated with respect to its performance improvement in terms of contrast-to-noise ratio in comparison with Kalman filter, low-pass filtering, and independent component method. The experimental results show that the proposed model achieves reductions of 77% and 99% in terms of the number of channels exhibiting higher contrast-to-noise ratios in oxy-hemoglobin and deoxy-hemoglobin, respectively. The approach is robust in obtaining consistent HR data. The proposed method is applied for both offline and online noise removal.

Effect of Physiological noise on Thoraco-Lumbar spinal cord fMRI in 3T Magnetic field

Introduction: Functional MRI methods have been used to study sensorimotor processing in the brain and the Spinal cord. However, these techniques confront unwanted contributions to the measured signal from physiological fluctuations. For the spinal cord imaging, most of the challenges are consequences of cardiac and respiratory movement artifacts that are considered as significant sources of noise. Spinal cord in Thoraco-Lumbar is close to lungs and diaphragms that influence cerebrospinal fluid-filled spaces and cause changes in thesusceptibility due to the change in the amount of air in the lung. In this study, we investigatedthe effect of each source of physiological noise and contribution of them to the outcome ofthe analysis of the BOLD signal in human spinal cord during a sensory stimulation of the foot. Materials and Methods: Fifteen young healthy male volunteers participated in the study. Sixty pressure pain stimuli delivered on L5 dermatome between the two malleoli. All of functional data were collected using a 3T Siemens Prisma scanner with 31 axial slices T2*-weighted ZOOMit (TR = 3000 ms; TE = 30 ms; FA = 90°; FoV = 160 × 160 mm; matrix size = 64 × 64). Respiratory and cardiac signals were recorded during the imaging session using the data acquired from theimplemented physiological monitoring unit. The Spinal Cord Toolbox and FSL were used forimage processing and analysis. Generated respiration and cardiac regressors were includedin the GLM for the quantification of the effect of each of them on the task-analyses results. The sum of activated voxels of the clusters in the spinal cord and in all the image was calculated. Results: Comparison of sum of activated voxels. A one-way within subject’s ANOVA was applied toevaluate the effect of physiological functions on spinal cord fMRI in three noise correction inthe GLM. There was a statistically significant effect of physiological noise correctiononnumber of activated voxels in Image, F (3, 42) = 3.00689, p = .040817, η2 =0.17, and thisvalues in spinal cord was F(3, 42) = 21.314, p =.00001, , η2 =0.6. Bonferroni post hoc testsillustrate that cardiac noise correction had an effective role on increase activated voxels (mean = 23.46±9.46) compared to other noise correction methods. Conclusion: The effect of the Heart-beat and Respiration movements has a significant role in thephysiological noise and concurrency between movements with task and correction of thismovements can remove the real effect of stimulation. Cardiac effect and changes in thecorrected results are higher than other physiological noise sources. In spite of previous work,displacement of the spinal cord and effect of this noise in the fMRI results are significant andcannot be ignored.

Acoustic respiration rate and pulse oximetry-derived respiration rate: a clinical comparison study

Respiration rate (RR) is a critical vital sign that provides early detection of respiratory compromise. The acoustic technique of measuring continuous respiration rate (RRa) interprets the large airway sound envelope to calculate respiratory rate while pulse oximetry-derived respiratory rate (RRoxi) interprets modulations of the photoplethsymograph in response to hemodynamic changes during the respiratory cycle. The aim of this study was to compare the performance of these technologies to each other and to a capnography-based reference device. Subjects were asked to decrease their RR from 14 to 4 breaths per minute (BPM) and then increase RR from 14 to 24 BPM. The effects of physiological noise, ambient noise, and head movement and shallow breathing on device performance were also evaluated. The test devices were: (1) RRa, Radical-7 (Masimo Corporation), (2) RRoxi, Nellcor™ Bedside Respiratory Patient Monitoring System (Medtronic), and (3) reference device, Capnostream20p™ (Medtronic). All devices were configured with their default settings. Twenty-nine healthy adult subjects were included in the study. During abrupt changes in breathing, overall RRoxi was accurate for longer periods of time than RRa; specifically, RRoxi was more accurate during low and normal RR, but not during high RR. RRoxi also displayed a value for significantly longer time periods than RRa when the subjects produced physiological sounds and moved their heads, but not during shallow breathing or ambient noise. RRoxi may be more accurate than RRa during development of bradypnea. Also, RRoxi may display a more reliable RR value during routine patient activities.

Influence of background suppression and retrospective realignment on free-breathing renal perfusion measurement using pseudo-continuous ASL.

To assess the influence of background suppression and retrospective realignment on physiological noise and image quality in free-breathing renal pseudo-continuous arterial spin labeling (pCASL). Ten subjects were scanned at 3T with a pCASL prepared single-slice coronal acquisition through the kidneys under free breathing. Multiple acquisitions were performed with various levels of residual background signal based on optimization of pulse timings to achieve specific background suppression levels (<2%, <5%, <10%, <20%). A retrospective non-rigid motion-correction strategy was also implemented. Decreasing level of residual background signal was associated with higher temporal SNR. The retrospective motion-correction provided an additional but not statistically significant improvement in tSNR. The highest image quality was obtained with the lowest level of residual background signal accompanied by the retrospective motion-correction, although no significant difference in quantitative renal blood-flow could be observed. Renal perfusion measurement with ASL under free breathing is feasible and robust against physiological noise when using strong background suppression strategies. Finally, retrospective motion-correction further improves image quality but cannot replace background suppression.

Multi-echo fMRI, resting-state connectivity, and high psychometric schizotypy.

Disrupted striatal functional connectivity is proposed to play a critical role in the development of psychotic symptoms. Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies typically reported disrupted striatal connectivity in patients with psychosis and in individuals at clinical and genetic high risk of the disorder relative to healthy controls. This has not been widely studied in healthy individuals with subclinical psychotic-like experiences (schizotypy). Here we applied the emerging technology of multi-echo rs-fMRI to examine corticostriatal connectivity in this group, which is thought to drastically maximize physiological noise removal and increase BOLD contrast-to-noise ratio. Multi-echo rs-fMRI data (echo times, 12, 28, 44, 60 ms) were acquired from healthy individuals with low (LS, n = 20) and high (HS, n = 19) positive schizotypy as determined with the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE). After preprocessing to ensure optimal contrast and removal of non-BOLD signal components, whole-brain functional connectivity from six striatal seeds was compared between the HS and LS groups. Effects were considered significant at cluster-level p < .05 family-wise error correction. Compared to LS, HS subjects showed lower rs-fMRI connectivity between ventromedial prefrontal regions and ventral striatal regions. Lower connectivity was also observed between the dorsal putamen and the hippocampus, occipital regions, as well as the cerebellum. These results demonstrate that subclinical positive psychotic-like experiences in healthy individuals are associated with striatal hypoconnectivity as detected using multi-echo rs-fMRI. Further application of this approach may aid in characterizing functional connectivity abnormalities across the extended psychosis phenotype.

Characterizing contrast origins and noise contribution in spin-echo EPI BOLD at 3 T

In this work, we characterize contrast origins and noise contributions of spin echo (SE) EPI BOLD signal at 3 T. SE BOLD is a fMRI method of choice for imaging brain regions affected by susceptibility artifacts at lower fields, but its sensitivity remains a limiting factor for whole-brain imaging. To resolve this, the signal and noise contributions as well as TE dependence of SE EPI are characterized in this study. By integrating a two-compartment BOLD model with a physiological-thermal noise model, a new SE-BOLD signal model was introduced. The new SE-BOLD model was fit into SE-EPI fMRI data acquired during hypercapnic manipulations at various TEs, using typical fMRI voxel dimensions (3.4 × 3.4 × 5 mm3). Our model predicts intra- and extravascular signal and noise contributions consistent with our understanding of the SE-EPI contrast mechanism. The intravascular BOLD contribution is shown to dominate at TEs lower than tissue T2, but the physiological noise contributions in SE-EPI signal is also shown to be lower than that of gradient-echo (GE). Furthermore, SE-EPI contrast-to-noise ratio (CNR) is not maximized at tissue T2 as is typically assumed. To summarize, a new SE-BOLD model was proposed to characterize SE-BOLD contrast and physiological noise contribution at 3 T. Results suggests that SE-BOLD sensitivity can be improved by using shorter TEs, making it a more attractive choice for fMRI, especially in regions with susceptibility artifacts. Such optimizations could also help extend the application of SE BOLD to WM fMRI studies.

Identifying Respiration-Related Aliasing Artifacts in the Rodent Resting-State fMRI.

Resting-state functional magnetic resonance imaging (rs-fMRI) combined with optogenetics and electrophysiological/calcium recordings in animal models is becoming a popular platform to investigate brain dynamics under specific neurological states. Physiological noise originating from the cardiac and respiration signal is the dominant interference in human rs-fMRI and extensive efforts have been made to reduce these artifacts from the human data. In animal fMRI studies, physiological noise sources including the respiratory and cardiorespiratory artifacts to the rs-fMRI signal fluctuation have typically been less investigated. In this article, we demonstrate evidence of aliasing effects into the low-frequency rs-fMRI signal fluctuation mainly due to respiration-induced B0 offsets in anesthetized rats. This aliased signal was examined by systematically altering the fMRI sampling rate, i.e., the time of repetition (TR), in free-breathing conditions and by adjusting the rate of ventilation. Anesthetized rats under ventilation showed a significantly narrower frequency bandwidth of the aliasing effect than free-breathing animals. It was found that the aliasing effect could be further reduced in ventilated animals with a muscle relaxant. This work elucidates the respiration-related aliasing effects on the rs-fMRI signal fluctuation from anesthetized rats, indicating non-negligible physiological noise needed to be taken care of in both awake and anesthetized animal rs-fMRI studies.

The Use of Physiological Signals in Brainstem/Midbrain fMRI.

Brainstem and midbrain nuclei are closely linked to cognitive performance and autonomic function. To advance the localization in this area, precise functional imaging is fundamental. In this study, we used a sophisticated fMRI technique as well as physiological recordings to investigate the involvement of brainstem/midbrain nuclei in cognitive control during a Stroop task. The temporal signal-to-noise ratio (tSNR) increased due to physiological noise correction (PNC) especially in regions adjacent to arteries and cerebrospinal fluid. Within the brainstem/cerebellum template an average tSNR of 68 ± 16 was achieved after the simultaneous application of a high-resolution fMRI, specialized co-registration, and PNC. The analysis of PNC data revealed an activation of the substantia nigra in the Stroop interference contrast whereas no significant results were obtained in the midbrain or brainstem when analyzing uncorrected data. Additionally, we found that pupil size indicated the level of cognitive effort. The Stroop interference effect on pupillary responses was correlated to the effect on reaction times (R2 = 0.464, p < 0.05). When Stroop stimuli were modulated by pupillary responses, we observed a significant activation of the LC in the Stroop interference contrast. Thus, we demonstrated the beneficial effect of PNC on data quality and statistical results when analyzing neuronal responses to a cognitive task. Parametric modulation of task events with pupillary responses improved the model of LC BOLD activations in the Stroop interference contrast.

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

Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event‐related designs we used, considering multiple event types and randomization of events between subjects. We consider the lack of validity found with one‐sample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two‐sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all data sets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (p = .01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.