Controlling for the effect of arterial-CO2 fluctuations in resting-state fMRI: Comparing end-tidal CO2 clamping and retroactive CO2 correction.

The BOLD signal, as the basis of functional MRI, arises from both neuronal and vascular factors, with their respective contributions to resting state-fMRI still unknown. Among the factors contributing to "physiological noise", dynamic arterial CO2 fluctuations constitutes the strongest and the most widespread modulator of the grey-matter rs-fMRI signal. Some important questions are: (1) if we were able to clamp arterial CO2 such that fluctuations are removed, what would happen to rs-fMRI measures? (2) falling short of that, is it possible to retroactively correct for CO2 effects with equivalent outcome? In this study 13 healthy subjects underwent two rs-fMRI acquisitions. During the "clamped" run, end-tidal CO2 (PETCO2) is clamped to the average PETCO2 level of each participant, while during the "free-breathing" run, the PETCO2 level is passively monitored but not controlled. PETCO2 correction was applied to the free-breathing data by convolving PETCO2 with its BOLD response function, and then regressing out the result. We computed the BOLD resting-state fluctuation amplitude (RSFA), as well as seed-independent mean functional connectivity (FC) as the weighted global brain connectivity (wGBC). Furthermore, connectivity between conditions were compared using coupled intrinsic-connectivity distribution (ICD) method. We ensured that PETCO2 clamping did not significantly alter heart-beat and respiratory variation. We found that neither PETCO2 clamping nor correction produced significant change in RSFA and wGBC. In terms of the ICD, PETCO2 clamping and correction both reduced FC strength in the majority of grey matter regions, although the effect of PETCO2 correction is considerably smaller than the effect of PETCO2 clamping. Interestingly, we found the effect of the commonly employed white-mater/cerebrospinal-fluid regression to be similar to that of PETCO2 clamping than global-signal regression. Nonetheless, both methods reduce functional connectivity significantly more than does PETCO2 clamping. Furthermore, while PETCO2 clamping reduced inter-subject variability in FC, PETCO2 correction increased the variability. Overall PETCO2 correction is not the equivalent of PETCO2 clamping, although it shifts FC values towards the same direction as clamping does.

Characterizing the modulation of resting-state fMRI metrics by baseline physiology.

The blood-oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) signal is commonly used to assess functional connectivity across brain regions, particularly in the resting state (rs-fMRI). However, the BOLD fMRI signal is not merely a representation of neural activity, but a combination of neural activity and vascular response. These aspects of the BOLD signal are easily influenced by systemic physiology, potentially biasing BOLD-based functional connectivity measurements. In this work, we focus on the following physiological modulators of the BOLD signal: cerebral blood flow (CBF), venous blood oxygenation, and cerebrovascular reactivity (CVR). We use simulations and experiments to examine the relationship between the physiological parameters and rs-fMRI functional connectivity measurements in three resting-state networks: default mode network, somatosensory network and visual network. By using the general linear model, we demonstrate that physiological modulators significantly impact functional connectivity measurements in these regions, but in a manner that depends on the interplay between signal- and noise-driven correlations. Moreover, we find that the physiological effects vary by brain region and depend on the range of physiological conditions probed; the associations are more complex than previously reported. The results confirm that it is important to account for the effect of physiological modulators when comparing resting-state fMRI metrics. We note that such modulatory effects may be amplified by disease conditions, which will warrant future investigations.

Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults.

In conventional neuroimaging, cerebrovascular reactivity (CVR) is quantified primarily using the blood-oxygenation level-dependent (BOLD) functional MRI (fMRI) signal, specifically, as the BOLD response to intravascular carbon dioxide (CO2) modulations, in units of [%ΔBOLD/mmHg]. While this method has achieved wide appeal and clinical translation, the tolerability of CO2-related tasks amongst patients and the elderly remains a challenge in more routine and large-scale applications. In this work, we propose an improved method to quantify CVR by exploiting intrinsic fluctuations in CO2 and corresponding changes in the resting-state BOLD signal (rs-qCVR). Our rs-qCVR approach requires simultaneous monitoring of PETCO2, cardiac pulsation and respiratory volume. In 16 healthy adults, we compare our quantitative CVR estimation technique to the prospective CO2-targeting based CVR quantification approach (qCVR, the "standard"). We also compare our rs-CVR to non-quantitative alternatives including the resting-state fluctuation amplitude (RSFA), amplitude of low-frequency fluctuation (ALFF) and global-signal regression. When all subjects were pooled, only RSFA and ALFF were significantly associated with qCVR. However, for characterizing regional CVR variations within each subject, only the PETCO2-based rs-qCVR measure is strongly associated with standard qCVR in 100% of the subjects (p≤0.1). In contrast, for the more qualitative CVR measures, significant within-subject association with qCVR was only achieved in 50-70% of the subjects. Our work establishes the feasibility of extracting quantitative CVR maps using rs-fMRI, opening the possibility of mapping functional connectivity and qCVR simultaneously.

The association between cerebrovascular reactivity and resting-state fMRI functional connectivity in healthy adults: The influence of basal carbon dioxide.

Although widely used in resting-state fMRI (fMRI) functional connectivity measurement (fcMRI), the BOLD signal is only an indirect measure of neuronal activity, and is inherently modulated by both neuronal activity and vascular physiology. For instance, cerebrovascular reactivity (CVR) varies widely across individuals irrespective of neuronal function, but the implications for fcMRI are currently unknown. This knowledge gap compromises our ability to correctly interpret fcMRI measurements. In this work, we investigate the relationship between CVR and resting fcMRI measurements in healthy young adults, in both the motor and the executive-control networks. We modulate CVR within each individual by subtly increasing and decreasing resting vascular tension through baseline end-tidal CO2 (PETCO2), and measure fcMRI during these hypercapnic, hypocapnic and normocapnic states. Furthermore, we assess the association between CVR and fcMRI within and across individuals. Within individuals, resting PETCO2 is found to significantly influence both CVR and resting fcMRI values. In addition, we find resting fcMRI to be significantly and positively associated with CVR across the group in both networks. This relationship is potentially mediated by concomitant alterations in BOLD signal fluctuation amplitude. This work clearly demonstrates and quantifies a major vascular modulator of resting fcMRI, one that is also subject and regional dependent. We suggest that individualized correction for CVR effects in fcMRI measurements is essential for fcMRI studies of healthy brains, and can be even more important in studying diseased brains.

Constrained by our connections: white matter's key role in interindividual variability in visual working memory capacity.

Visual working memory (VWM) plays an essential role in many perceptual and higher-order cognitive processes. Despite its reliance on a broad network of brain regions, VWM has a capacity limited to a few objects. This capacity varies substantially across individuals and relates closely to measures of overall cognitive function (Luck and Vogel, 2013). The mechanisms underlying these properties are not completely understood, although the amplitude of neural signal oscillations (Vogel and Machizawa, 2004) and brain activation in specific cortical regions (Todd and Marois, 2004) have been implicated. Variability in VWM performance may also reflect variability in white matter structural properties. However, data based primarily on diffusion tensor imaging approaches remain inconclusive. Here, we investigate the relationship between white matter and VWM capacity in human subjects using an advanced diffusion imaging technique, diffusion kurtosis imaging. Diffusion kurtosis imaging provides several novel quantitative white mater metrics, among them the axonal water fraction (f(axon)), an index of axonal density and caliber. Our results show that 59% of individual variability in VWM capacity may be explained by variations in f(axon) within a widely distributed network of white matter tracts. Increased f(axon) associates with increased VWM capacity. An additional 12% in VWM capacity variance may be explained by diffusion properties of the extra-axonal space. These data demonstrate, for the first time, the key role of white matter in limiting VWM capacity in the healthy adult brain and suggest that white matter may represent an important therapeutic target in disorders of impaired VWM and cognition.

Mapping the end-tidal CO2 response function in the resting-state BOLD fMRI signal: spatial specificity, test-retest reliability and effect of fMRI sampling rate.

The blood oxygenation level dependent (BOLD) signal measures brain function indirectly through physiological processes and hence is susceptible to global physiological changes. Specifically, fluctuations in end-tidal CO2 (PETCO2), in addition to cardiac rate variation (CRV), and respiratory volume per time (RVT) variations, have been known to confound the resting-state fMRI (rs-fMRI) signal. Previous studies addressed the resting-state fMRI response function to CRV and RVT, but no attempt has been made to directly estimate the voxel-wise response function to PETCO2. Moreover, the potential interactions among PETCO2, CRV, and RVT necessitate their simultaneous inclusion in a multi-regression model to estimate the PETCO2 response. In this study, we use such a model to estimate the voxel-wise PETCO2 response functions directly from rs-fMRI data of nine healthy subjects. We also characterized the effect of sampling rate (TR=2seconds vs. 323ms) on the temporal and spatial variability of the PETCO2 response function in addition to that of CRV and RVT. In addition, we assess the test-retest reproducibility of the response functions to PETCO2, CRV and RVT. We found that despite overlaps across their spatial patterns, PETCO2 explains a unique portion of the rs-fMRI signal variance compared to RVT and CRV. We also found the shapes of the estimated responses are very similar between long- and short-TR data, although responses estimated from short-TR data have higher reproducibility.

Comparing cerebrovascular reactivity measured using BOLD and cerebral blood flow MRI: The effect of basal vascular tension on vasodilatory and vasoconstrictive reactivity.

Cerebrovascular reactivity (CVR) is an important metric of cerebrovascular health. While the BOLD fMRI method in conjunction with carbon-dioxide (CO2) based vascular manipulation has been the most commonly used, the BOLD signal is not a direct measure of vascular changes, and the use of arterial-spin labeling (ASL) cerebral blood flow (CBF) imaging is increasingly advocated. Nonetheless, given the differing dependencies of BOLD and CBF on vascular baseline conditions and the diverse CO2 manipulation types currently used in the literature, knowledge of potential biases introduced by each technique is critical for the interpretation of CVR measurements. In this work, we use simultaneous BOLD-CBF acquisitions during both vasodilatory (hypercapnic) and vasoconstrictive (hypocapnic) stimuli to measure CVR. We further imposed different levels of baseline vascular tension by inducing hypercapnic and hypocapnic baselines, separately from normocapnia by 4mmHg. We saw significant and diverse dependencies on vascular stimulus and baseline condition in both BOLD and CBF CVR measurements: (i) BOLD-based CVR is more sensitive to basal vascular tension than CBF-based CVR; (ii) the use of a combination of vasodilatory and vasoconstrictive stimuli maximizes the sensitivity of CBF-based CVR to vascular tension changes; (iii) the BOLD and CBF vascular response delays are both significantly lengthened at predilated baseline. As vascular tension can often be altered by potential pathology, our findings are important considerations when interpreting CVR measurements in health and disease.

Comparing data-driven physiological denoising approaches for resting-state fMRI: implications for the study of aging.

Introduction: Physiological nuisance contributions by cardiac and respiratory signals have a significant impact on resting-state fMRI data quality. As these physiological signals are often not recorded, data-driven denoising methods are commonly used to estimate and remove physiological noise from fMRI data. To investigate the efficacy of these denoising methods, one of the first steps is to accurately capture the cardiac and respiratory signals, which requires acquiring fMRI data with high temporal resolution. Methods: In this study, we used such high-temporal resolution fMRI data to evaluate the effectiveness of several data-driven denoising methods, including global-signal regression (GSR), white matter and cerebrospinal fluid regression (WM-CSF), anatomical (aCompCor) and temporal CompCor (tCompCor), ICA-AROMA. Our analysis focused on the consequence of changes in low-frequency, cardiac and respiratory signal power, as well as age-related differences in terms of functional connectivity (fcMRI). Results: Our results confirm that the ICA-AROMA and GSR removed the most physiological noise but also more low-frequency signals. These methods are also associated with substantially lower age-related fcMRI differences. On the other hand, aCompCor and tCompCor appear to be better at removing high-frequency physiological signals but not low-frequency signal power. These methods are also associated with relatively higher age-related fcMRI differences, whether driven by neuronal signal or residual artifact. These results were reproduced in data downsampled to represent conventional fMRI sampling frequency. Lastly, methods differ in performance depending on the age group. Discussion: While this study cautions direct comparisons of fcMRI results based on different denoising methods in the study of aging, it also enhances the understanding of different denoising methods in broader fcMRI applications.

Performance of Temporal and Spatial Independent Component Analysis in Identifying and Removing Low-Frequency Physiological and Motion Effects in Resting-State fMRI.

Effective separation of signal from noise (including physiological processes and head motion) is one of the chief challenges for improving the sensitivity and specificity of resting-state fMRI (rs-fMRI) measurements and has a profound impact when these noise sources vary between populations. Independent component analysis (ICA) is an approach for addressing these challenges. Conventionally, due to the lower amount of temporal than spatial information in rs-fMRI data, spatial ICA (sICA) is the method of choice. However, with recent developments in accelerated fMRI acquisitions, the temporal information is becoming enriched to the point that the temporal ICA (tICA) has become more feasible. This is particularly relevant as physiological processes and motion exhibit very different spatial and temporal characteristics when it comes to rs-fMRI applications, leading us to conduct a comparison of the performance of sICA and tICA in addressing these types of noise. In this study, we embrace the novel practice of using theory (simulations) to guide our interpretation of empirical data. We find empirically that sICA can identify more noise-related signal components than tICA. However, on the merit of functional-connectivity results, we find that while sICA is more adept at reducing whole-brain motion effects, tICA performs better in dealing with physiological effects. These interpretations are corroborated by our simulation results. The overall message of this study is that if ICA denoising is to be used for rs-fMRI, there is merit in considering a hybrid approach in which physiological and motion-related noise are each corrected for using their respective best-suited ICA approach.

Simultaneous Multislice Resting-State Functional Magnetic Resonance Imaging at 3 Tesla: Slice-Acceleration-Related Biases in Physiological Effects.

Simultaneous multislice echo-planar imaging (SMS-EPI) can enhance the spatiotemporal resolution of resting-state functional MRI (rs-fMRI) by encoding and simultaneously imaging "groups" of slices. However, phenomena, including respiration, cardiac pulsatility, respiration volume per time (RVT), and cardiac rate variation (CRV), referred to as "physiological processes," impact SMS-EPI rs-fMRI in a manner that is yet to be well characterized. In particular, physiological noise may incur aliasing and introduce spurious signals from one slice into another within the "slice group" in rs-fMRI data, resulting in a deleterious effect on resting-state functional connectivity MRI (rs-fcMRI) maps. In the present work, we aimed to quantitatively compare the effects of physiological noise on regular EPI and SMS-EPI in terms of rs-fMRI data and resulting functional connectivity measurements. We compare SMS-EPI and regular EPI data acquired from 11 healthy young adults with matching parameters. The physiological noise characteristics were compared between the two data sets through different combinations of physiological regression steps. We observed that the physiological noise characteristics differed between SMS-EPI and regular EPI, with cardiac pulsatility contributing more to noise in regular EPI data but low-frequency heart rate variability contributing more to SMS-EPI. In addition, a significant slice-group bias was observed in the functional connectivity density maps derived from SMS-EPI data. We conclude that making appropriate corrections for physiological noise is likely more important for SMS-EPI than for regular EPI acquisitions.

The Effect of Low-Frequency Physiological Correction on the Reproducibility and Specificity of Resting-State fMRI Metrics: Functional Connectivity, ALFF, and ReHo.

The resting-state fMRI (rs-fMRI) signal is affected by a variety of low-frequency physiological phenomena, including variations in cardiac-rate (CRV), respiratory-volume (RVT), and end-tidal CO2 (PETCO2). While these effects have become better understood in recent years, the impact that their correction has on the quality of rs-fMRI measurements has yet to be clarified. The objective of this paper is to investigate the effect of correcting for CRV, RVT and PETCO2 on the rs-fMRI measurements. Nine healthy subjects underwent a test-retest rs-fMRI acquisition using repetition times (TRs) of 2 s (long-TR) and 0.323 s (short-TR), and the data were processed using eight different physiological correction strategies. Subsequently, regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and resting-state connectivity of the motor and default-mode networks are calculated for each strategy. Reproducibility is calculated using intra-class correlation and the Dice Coefficient, while the accuracy of functional-connectivity measures is assessed through network separability, sensitivity and specificity. We found that: (1) the reproducibility of the rs-fMRI measures improved significantly after correction for PETCO2; (2) separability of functional networks increased after PETCO2 correction but was not affected by RVT and CRV correction; (3) the effect of physiological correction does not depend on the data sampling-rate; (4) the effect of physiological processes and correction strategies is network-specific. Our findings highlight limitations in our understanding of rs-fMRI quality measures, and underscore the importance of using multiple quality measures to determine the optimal physiological correction strategy.

Spin-Echo Resting-State Functional Connectivity in High-Susceptibility Regions: Accuracy, Reliability, and the Impact of Physiological Noise.

Gradient-echo (GE) echo-planar imaging (EPI) is the method of choice in blood-oxygenation level-dependent (BOLD) functional MRI (fMRI) studies, as it demonstrates substantially higher BOLD sensitivity than its spin-echo (SE) counterpart. However, it is also well known that the GE-EPI signal is prone to signal dropouts and shifts due to susceptibility effects near air-tissue interfaces. SE-EPI, in contrast, is minimally affected by these artifacts. In this study, we quantify, for the first time, the sensitivity and specificity of SE and GE EPI for resting-state fMRI functional connectivity (fcMRI) mapping, using the 1000-brain fcMRI atlas (Yeo et al., 2011 ) as the pseudoground truth. Moreover, we assess the influence of physiological processes on resting-state BOLD measured using both regular and ultrafast GE and SE acquisitions. Our work demonstrates that SE-EPI and GE-EPI are associated with similar sensitivities, specificities, and intersubject reproducibility in fcMRI for most brain networks, generated using both seed-based analysis and independent component analysis. More importantly, SE-based fcMRI measurements demonstrated significantly higher sensitivity, specificity, and intersubject reproducibility in high-susceptibility regions, spanning the limbic and frontal networks in the 1000-brain atlas. In addition, SE-EPI is significantly less sensitive to prominent sources of physiological noise, including low-frequency respiratory volume and heart rate variations. Our work suggests that SE-EPI should be increasingly adopted in the study of networks spanning susceptibility-affected brain regions, including those that are important to memory, language, and emotion.