Using carpet plots to analyze blood transit times in the brain during hypercapnic challenge magnetic resonance imaging.

Blood arrival time and blood transit time are useful metrics in characterizing hemodynamic behaviors in the brain. Functional magnetic resonance imaging in combination with a hypercapnic challenge has been proposed as a non-invasive imaging tool to determine blood arrival time and replace dynamic susceptibility contrast (DSC) magnetic resonance imaging, a current gold-standard imaging tool with the downsides of invasiveness and limited repeatability. Using a hypercapnic challenge, blood arrival times can be computed by cross-correlating the administered CO2 signal with the fMRI signal, which increases during elevated CO2 due to vasodilation. However, whole-brain transit times derived from this method can be significantly longer than the known cerebral transit time for healthy subjects (nearing 20 s vs. the expected 5-6 s). To address this unrealistic measurement, we here propose a novel carpet plot-based method to compute improved blood transit times derived from hypercapnic blood oxygen level dependent fMRI, demonstrating that the method reduces estimated blood transit times to an average of 5.32 s. We also investigate the use of hypercapnic fMRI with cross-correlation to compute the venous blood arrival times in healthy subjects and compare the computed delay maps with DSC-MRI time to peak maps using the structural similarity index measure (SSIM). The strongest delay differences between the two methods, indicated by low structural similarity index measure, were found in areas of deep white matter and the periventricular region. SSIM measures throughout the remainder of the brain reflected a similar arrival sequence derived from the two methods despite the exaggerated spread of voxel delays computed using CO2 fMRI.

Using carpet plots to analyze transit times of low frequency oscillations in resting state fMRI.

A "carpet plot" is a 2-dimensional plot (time vs. voxel) of scaled fMRI voxel intensity values. Low frequency oscillations (LFOs) can be successfully identified from BOLD fMRI and used to study characteristics of neuronal and physiological activity. Here, we evaluate the use of carpet plots paired with a developed slope-detection algorithm as a means to study LFOs in resting state fMRI (rs-fMRI) data with the help of dynamic susceptibility contrast (DSC) MRI data. Carpet plots were constructed by ordering voxels according to signal delay time for each voxel. The slope-detection algorithm was used to identify and calculate propagation times, or "transit times", of tilted vertical edges across which a sudden signal change was observed. We aim to show that this metric has applications in understanding LFOs in fMRI data, possibly reflecting changes in blood flow speed during the scan, and for evaluating alternative blood-tracking contrast agents such as inhaled CO2. We demonstrate that the propagations of LFOs can be visualized and automatically identified in a carpet plot as tilted lines of sudden intensity change. Resting state carpet plots produce edges with transit times similar to those of DSC carpet plots. Additionally, resting state carpet plots indicate that edge transit times vary at different time points during the scan.

Post-hoc physiological waveform extraction from motion estimation in simultaneous multislice (SMS) functional MRI using separate stack processing.

PURPOSE: Motion estimation is an essential step in functional MRI (fMRI) preprocessing. Usually, fMRI processing software packages (eg, FSL and AFNI) automatically estimate motion parameters in order to counteract the effects of motion. However, the time courses of the motion estimation for fMRI data also contain information about physiological processes. Here, we show that respiration and cardiac signals can be extracted from motion estimation at significantly higher bandwidth than is possible with current methods. METHOD: To detect motion at high effective temporal resolution (HighRes), the motion parameters of stacks of simultaneously acquired slices were estimated separately, then combined. This method was validated by extracting physiological motion signals from resting state fMRI (rsfMRI) data (Enhanced Nathan Kline Institute-Rockland Sample) and comparing them to respiration belt and pulse oximeter signals. RESULTS: HighRes motion time-courses with an effective sampling rate of 15.5 and 11.4 Hz were extracted from repetition time (TR) = 0.645 and 1.4 s data, respectively. Respiration waveforms were extracted with significantly higher accuracy than the original motion parameters. Even cardiac waveforms could be extracted, despite the fact that the sampling time or TR values were too long to sample cardiac frequencies. CONCLUSION: HighRes motion traces provide insight into the subjects' motion at higher frequencies than can be estimated using standard techniques. In its simplest form, this technique can recover accurate respiration signals and may reveal additional complexity in brain motion.

Low Frequency Systemic Hemodynamic "Noise" in Resting State BOLD fMRI: Characteristics, Causes, Implications, Mitigation Strategies, and Applications.

Advances in functional magnetic resonance imaging (fMRI) acquisition have improved signal to noise to the point where the physiology of the subject is the dominant noise source in resting state fMRI data (rsfMRI). Among these systemic, non-neuronal physiological signals, respiration and to some degree cardiac fluctuations can be removed through modeling, or in the case of newer, faster acquisitions such as simultaneous multislice acquisition, simple spectral filtering. However, significant low frequency physiological oscillation (∼0.01-0.15 Hz) remains in the signal. This is problematic, as it is the precise frequency band occupied by the neuronally modulated hemodynamic responses used to study brain connectivity, precluding its removal by spectral filtering. The source of this signal, and its method of production and propagation in the body, have not been conclusively determined. Here, we summarize the defining characteristics of the systemic low frequency noise signal, and review some current theories about the signal source and the evidence supporting them. The strength and distribution of the systemic LFO signal make characterizing and removing it essential for accurate quantification, especially for resting state connectivity, when no stimulation can be compared with the signal. Widespread correlated non-neuronal signals obscure and distort the more localized patterns of neuronal correlations between interacting brain regions; they may even cause apparent connectivity between regions with no neuronal interaction. Here, we discuss a simple method we have developed to parse the global, moving, blood-borne signal from the stationary, neuronal connectivity signals, substantially reducing the negative correlations that result from global signal regression. Finally, we will discuss some of the uses to which the moving systemic low frequency oscillation can be put if we consider it a "signal" carrying information, rather than simply "noise" complicating the interpretation of resting state connectivity. Properly utilizing this signal may offer insights into subtle hemodynamic alterations that can be used as early indicators of circulatory dysfunction in a number of neuropsychiatric conditions, such as prodromal stroke, moyamoya, and Alzheimer's disease.

Reduced Functional Connectivity in Adults with Persistent Post-Concussion Symptoms: A Functional Near-Infrared Spectroscopy Study.

Concussion, or mild traumatic brain injury (mTBI), accounts for ∼80% of all TBIs across North America. The majority of mTBI patients recover within days to weeks; however, 14-36% of the time, acute mTBI symptoms persist for months or even years and develop into persistent post-concussion symptoms (PPCS). There is a need to find biomarkers in patients with PPCS, to improve prognostic ability and to provide insight into the pathophysiology underlying chronic symptoms. Recent research has pointed toward impaired network integrity and cortical communication as a biomarker. In this study we investigated functional near-infrared spectroscopy (fNIRS) as a technique to assess cortical communication deficits in adults with PPCS. Specifically, we aimed to identify cortical communication patterns in prefrontal and motor areas during rest and task, in adult patients with persistent symptoms. We found that (1) the PPCS group showed reduced connectivity compared with healthy controls, (2) increased symptom severity correlated with reduced coherence, and (3) connectivity differences were best distinguishable during task and in particular during the working memory task (n-back task) in the right and left dorsolateral prefrontal cortex (DLPFC). These data show that reduced brain communication may be associated with the pathophysiology of mTBI and that fNIRS, with a relatively simple acquisition paradigm, may provide a useful biomarker of this injury.

Automated Processing of fNIRS Data-A Visual Guide to the Pitfalls and Consequences.

With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference showing the effects of different processing methods, to help inform researchers in setting up and evaluating a processing pipeline. We show the significant impact of pre- and post-processing choices and stress again how important it is to combine data from both hemoglobin species in order to make accurate inferences about the activation site.

Perfusion information extracted from resting state functional magnetic resonance imaging.

It is widely known that blood oxygenation level dependent (BOLD) contrast in functional magnetic resonance imaging (fMRI) is an indirect measure for neuronal activations through neurovascular coupling. The BOLD signal is also influenced by many non-neuronal physiological fluctuations. In previous resting state (RS) fMRI studies, we have identified a moving systemic low frequency oscillation (sLFO) in BOLD signal and were able to track its passage through the brain. We hypothesized that this seemingly intrinsic signal moves with the blood, and therefore, its dynamic patterns represent cerebral blood flow. In this study, we tested this hypothesis by performing Dynamic Susceptibility Contrast (DSC) MRI scans (i.e. bolus tracking) following the RS scans on eight healthy subjects. The dynamic patterns of sLFO derived from RS data were compared with the bolus flow visually and quantitatively. We found that the flow of sLFO derived from RS fMRI does to a large extent represent the blood flow measured with DSC. The small differences, we hypothesize, are largely due to the difference between the methods in their sensitivity to different vessel types. We conclude that the flow of sLFO in RS visualized by our time delay method represents the blood flow in the capillaries and veins in the brain.

Systemic Low-Frequency Oscillations in BOLD Signal Vary with Tissue Type.

Blood-oxygen-level dependent (BOLD) signals are widely used in functional magnetic resonance imaging (fMRI) as a proxy measure of brain activation. However, because these signals are blood-related, they are also influenced by other physiological processes. This is especially true in resting state fMRI, during which no experimental stimulation occurs. Previous studies have found that the amplitude of resting state BOLD is closely related to regional vascular density. In this study, we investigated how some of the temporal fluctuations of the BOLD signal also possibly relate to regional vascular density. We began by identifying the blood-bound systemic low-frequency oscillation (sLFO). We then assessed the distribution of all voxels based on their correlations with this sLFO. We found that sLFO signals are widely present in resting state BOLD signals and that the proportion of these sLFOs in each voxel correlates with different tissue types, which vary significantly in underlying vascular density. These results deepen our understanding of the BOLD signal and suggest new imaging biomarkers based on fMRI data, such as amplitude of low-frequency fluctuation (ALFF) and sLFO, a combination of both, for assessing vascular density.

Correcting for Blood Arrival Time in Global Mean Regression Enhances Functional Connectivity Analysis of Resting State fMRI-BOLD Signals.

Resting state functional connectivity analysis is a widely used method for mapping intrinsic functional organization of the brain. Global signal regression (GSR) is commonly employed for removing systemic global variance from resting state BOLD-fMRI data; however, recent studies have demonstrated that GSR may introduce spurious negative correlations within and between functional networks, calling into question the meaning of anticorrelations reported between some networks. In the present study, we propose that global signal from resting state fMRI is composed primarily of systemic low frequency oscillations (sLFOs) that propagate with cerebral blood circulation throughout the brain. We introduce a novel systemic noise removal strategy for resting state fMRI data, "dynamic global signal regression" (dGSR), which applies a voxel-specific optimal time delay to the global signal prior to regression from voxel-wise time series. We test our hypothesis on two functional systems that are suggested to be intrinsically organized into anticorrelated networks: the default mode network (DMN) and task positive network (TPN). We evaluate the efficacy of dGSR and compare its performance with the conventional "static" global regression (sGSR) method in terms of (i) explaining systemic variance in the data and (ii) enhancing specificity and sensitivity of functional connectivity measures. dGSR increases the amount of BOLD signal variance being modeled and removed relative to sGSR while reducing spurious negative correlations introduced in reference regions by sGSR, and attenuating inflated positive connectivity measures. We conclude that incorporating time delay information for sLFOs into global noise removal strategies is of crucial importance for optimal noise removal from resting state functional connectivity maps.

Comparison of peripheral near-infrared spectroscopy low-frequency oscillations to other denoising methods in resting state functional MRI with ultrahigh temporal resolution.

PURPOSE: Functional MRI (fMRI) blood-oxygen level-dependent (BOLD) signals result not only from neuronal activation, but also from nonneuronal physiological processes. These changes, especially in the low-frequency domain (0.01-0.2 Hz), can significantly confound inferences about neuronal processes. It is crucial to effectively identify these nuisance low-frequency oscillations (LFOs). METHOD: A high temporal resolution (repetition time, ∼0.5 s) fMRI resting state study was conducted with simultaneous physiological measurements to compare LFOs measured directly by near-infrared spectroscopy (NIRS) in the periphery and three methods that model LFOs from the respiration or cardiac signal: 1) the respiration volume per time (RVT), 2) the respiratory variation (RVRRF), and 3) the cardiac variation method (HRCRF). The LFO noise regressors from these methods were compared temporally and spatially as well as in their denoising efficiency. RESULTS: Methods were not highly correlated with one another, temporally or spatially. The set of two NIRS LFOs combined explained over 13% of BOLD signal variance and explained equal or more variance than HRCRF and RVRRF or RVT combined (in 14 of 16 participants). CONCLUSION: LFOs collected using NIRS in the periphery contain distinct temporal and spatial information about the LFOs in BOLD fMRI that is not contained in current low-frequency denoising methods derived from respiration and cardiac pulsation. Magn Reson Med 76:1697-1707, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Time delay processing of hypercapnic fMRI allows quantitative parameterization of cerebrovascular reactivity and blood flow delays.

Blood oxygenation level-dependent fMRI contrast depends on the volume and oxygenation of blood flowing through the circulatory system. The effects on image intensity depend temporally on the arrival of blood within a voxel, and signal can be monitored during the time course of such blood flow. It has been previously shown that the passage of global endogenous variations in blood volume and oxygenation can be tracked as blood passes through the brain by determining the strength and peak time lag of their cross-correlation with blood oxygenation level-dependent data. By manipulating blood composition using transient hypercarbia and hyperoxia, we can induce much larger oxygenation and volume changes in the blood oxygenation level-dependent signal than result from natural endogenous fluctuations. This technique was used to examine cerebrovascular parameters in healthy subjects (n = 8) and subjects with intracranial stenosis (n = 22), with a subgroup of intracranial stenosis subjects scanned before and after surgical revascularization (n = 6). The halfwidth of cross-correlation lag times in the brain was larger in IC stenosis subjects (21.21 ± 14.22 s) than in healthy control subjects (8.03 ± 3.67), p < 0.001, and was subsequently reduced in regions that co-localized with surgical revascularization. These data show that blood circulatory timing can be measured robustly and longitudinally throughout the brain using simple respiratory challenges.

Can apparent resting state connectivity arise from systemic fluctuations?

It is widely accepted that the fluctuations in resting state blood oxygenation level dependent (BOLD) functional MRI (fMRI) reflect baseline neuronal activation through neurovascular coupling; this data is used to infer functional connectivity in the human brain during rest. Consistent activation patterns, i.e., resting state networks (RSN) are seen across groups, conditions, and even species. In this study, we show that some of these patterns can also be generated from the dynamic, systemic, non-neuronal physiological low frequency oscillations (sLFOs) in the BOLD signal alone. We have previously used multimodal imaging to demonstrate the wide presence of the same sLFOs in the brain (BOLD) and periphery with different time delays. This study shows that these sLFOs from BOLD signals alone can give rise to stable spatial patterns, which can be detected during resting state analyses. We generated synthetic resting state data for 11 subjects based only on subject-specific, dynamic sLFO information obtained from resting state data using concurrent peripheral optical imaging or a novel recursive procedure. We compared the results obtained by performing a group independent component analysis (ICA) on this synthetic data (i.e., the result from simulation) to the results obtained from analysis of the real data. ICA detected most of the eight well-known RSNs, including visual, motor, and default mode networks (DMNs), in both the real and the synthetic data sets. These findings suggest that RSNs may reflect, to some extent, vascular anatomy associated with systemic fluctuations, rather than neuronal connectivity.

Short repetition time multiband echo-planar imaging with simultaneous pulse recording allows dynamic imaging of the cardiac pulsation signal.

PURPOSE: Recently developed simultaneous multislice echo-planar imaging (EPI) sequences permit imaging of the whole brain at short repetition time (TR), allowing the cardiac fluctuations to be fully sampled in blood-oxygen-level dependent functional MRI (BOLD fMRI). A novel low computational analytical method was developed to dynamically map the passage of the pulsation signal through the brain and visualize the whole cerebral vasculature affected by the pulse signal. This algorithm is based on a simple combination of fast BOLD fMRI and the scanner's own built-in pulse oximeter. METHODS: Multiple, temporally shifted copies of the pulse oximeter data (with 0.08 s shifting step and coverage of a 1-s span) were downsampled and used as cardiac pulsation regressors in a general linear model based analyses (FSL) of the fMRI data. The resulting concatenated z-statistics maps show the voxels that are affected as the cardiac signal travels through the brain. RESULTS: Many voxels were highly correlated with the pulsation regressor or its temporally shifted version. The dynamic and static cardiac pulsation maps obtained from both the task and resting state scans, resembled cerebral vasculature. CONCLUSION: The results demonstrated: (i) cardiac pulsation significantly affects most voxels in the brain; (ii) combining fast fMRI and this analytical method can reveal additional clinical information to functional studies.

Evaluating the effects of systemic low frequency oscillations measured in the periphery on the independent component analysis results of resting state networks.

Independent component analysis (ICA) is widely used in resting state functional connectivity studies. ICA is a data-driven method, which uses no a priori anatomical or functional assumptions. However, as a result, it still relies on the user to distinguish the independent components (ICs) corresponding to neuronal activation, peripherally originating signals (without directly attributable neuronal origin, such as respiration, cardiac pulsation and Mayer wave), and acquisition artifacts. In this concurrent near infrared spectroscopy (NIRS)/functional MRI (fMRI) resting state study, we developed a method to systematically and quantitatively identify the ICs that show strong contributions from signals originating in the periphery. We applied group ICA (MELODIC from FSL) to the resting state data of 10 healthy participants. The systemic low frequency oscillation (LFO) detected simultaneously at each participant's fingertip by NIRS was used as a regressor to correlate with every subject-specific IC time course. The ICs that had high correlation with the systemic LFO were those closely associated with previously described sensorimotor, visual, and auditory networks. The ICs associated with the default mode and frontoparietal networks were less affected by the peripheral signals. The consistency and reproducibility of the results were evaluated using bootstrapping. This result demonstrates that systemic, low frequency oscillations in hemodynamic properties overlay the time courses of many spatial patterns identified in ICA analyses, which complicates the detection and interpretation of connectivity in these regions of the brain.