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Breath-holding (BH) tasks during functional magnetic resonance imaging (fMRI) acquisitions are gaining popularity for non-invasive mapping of carbon-dioxide (CO2) driven cerebrovascular reactivity (CVR), which is a valuable clinical marker of vascular function. However, compliance to BH tasks is often unclear, and the ability to record end-tidal CO2often limited, rendering the optimal analysis of BH fMRI data a challenge. In this work, we demonstrate an adaptive data-driven approach for estimating CVR from BH fMRI data that minimizes errors due to subject non-compliance and regional CVR time delay variability. Building on previous work, we propose a frequency-domain-based approach for CVR estimation without the need for end-tidal CO2(PETCO2) recordings. CVR amplitude is estimated in units of %ΔBOLD directly from the data-driven BH frequency. Serious deviations from the designed task paradigm were suppressed and thus did not bias the estimated CVR values. We demonstrate our method in detecting regional CVR amplitude and time-lag differences in a group of 56 individuals, consisting of healthy (CTL), hypertensive (HT) and diabetic-hypertensive (DM+HT) groups of similar ages and sex ratios. The CVR amplitude was lowest in HT+DM, and HT had a lower CVR amplitude than CTL regionally but the voxelwise comparison did not yield statistical significance. Notably, we demonstrate that the voxelwise CVR time delay estimated in Fourier domain is a more sensitive marker of vascular dysfunction than CVR amplitude. While HT+DM seems to confer longer CVR delays, HT seems to confer shorter delays than CTL. These are the first MRI-based observations of CVR time delay differences between diabetic-hypertensive patients and healthy controls. These results demonstrate the feasibility of extracting CVR amplitude and CVR time delay using BH challenges without PETCO2recordings, and the unique clinical value of CVR time-delay information.

Physiological nuisance contributions by cardiac and respiratory signals has 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. 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 each method’s ability to remove cardiac and respiratory signal power, as well as its ability to preserve low-frequency signals and age-related functional connectivity (fcMRI) differences. Our findings revealed that ICA-AROMA and GSR consistently remove more heart-beat and respiratory frequencies, but also the most low-frequency signals. Our results confirm that the ICA-AROMA and GSR removed the most physiological noise at the expense of meaningful age-related fcMRI differences. On the other hand, aCompCor and tCompCor seem to provide a good balance between removing physiological signals and preserving fcMRI information. Lastly, methods differ in performance on young- and older-adult data sets. While this study cautions direct comparisons of fcMRI results based on different denoising methods in the study of aging, it also informs the choice of denoising method for broader fcMRI applications.

Progressive age-related changes in white matter morphometry and microstructure have been found throughout the brain. Both declines in white matter (WM) volume and deterioration of microstructural integrity have been observed. Predicting these changes across WM tracts and building an integrated model of age-related WM trajectories has proven challenging. While tractwise differences in volume and microstructural declines are common targets of investigation, there has been relatively little exploration into other attributes of tract morphology or its relation to microstructural measures in vivo. This study seeks to examine ten WM tracts for tract-wise differences in WM volume, length, the ratio of volume to length (VLR), and microstructural integrity as measured by fractional anisotropy (FA) and mean diffusivity (MD) using diffusion MRI data from the Human Connectome Project in Aging (HCP-A). From these measures, we analyzed relationships between morphometry and microstructure in the aging brain with the goal of laying the foundation for a unified model of age-related changes that relates WM microstructure/morphometry and developmental trajectories. Results indicated wide variation in rates and patterns of decline between tracts, as well as tract-specific interactions between tract VLR and microstructure. Robust sex differences were also identified. Our findings demonstrate the need for further exploration of the mechanisms behind both macro- and microstructural differences across the aging brain.

There has been growing attention on the effect of COVID-19 on white-matter microstructure, especially among those that self-isolated after being infected. There is also immense scientific interest and potential clinical utility to evaluate the sensitivity of single-shell diffusion MRI methods for detecting such effects. In this work, the sensitivities of three single-shell-compatible diffusion MRI modeling methods are compared for detecting the effect of COVID-19, including diffusion-tensor imaging, diffusion-tensor decomposition of orthogonal moments and correlated diffusion imaging. Imaging was performed on self-isolated patients at baseline and 3-month follow-up, along with age- and sex-matched controls. We demonstrate through simulations and experimental data that correlated diffusion imaging is associated with far greater sensitivity, being the only one of the three single-shell methods to demonstrate COVID-19-related brain effects. Results suggest less restricted diffusion in the frontal lobe in COVID-19 patients, but also more restricted diffusion in the cerebellar white matter, in agreement with several existing studies highlighting the vulnerability of the cerebellum to COVID-19 infection. These results, taken together with the simulation results, suggest that a significant proportion of COVID-19 related white-matter microstructural pathology manifests as a change in water diffusivity. Interestingly, different b-values also confer different sensitivities to the effects. No significant difference was observed in patients at the 3-month follow-up, likely due to the limited size of the follow-up cohort. To summarize, correlated diffusion imaging is shown to be a sensitive single-shell diffusion analysis approach that allows us to uncover opposing patterns of diffusion changes in the frontal and cerebellar regions of COVID-19 patients, suggesting the two regions react differently to viral infection.

The role of vascular risk in age-related brain degeneration has long been the subject of intense study. As a sub-category of vascular risk, obesity has an increasingly recognized role in influencing brain health and health-care strategies, but its association with brain health remains under-studied. Notably, no prior study has addressed sex differences in the association between adiposity and white-matter microstructural integrity, an important early marker of brain degeneration, despite known sex differences in fat storage and usage. This study focuses on the associations between adiposity (abdominal fat ratio: AFR, and liver proton density fat fraction: PDFF) and brain microstructural health (measures of white-matter microstructure using diffusion-tensor imaging, DTI). We found that fluid intelligence and reaction time are indeed associated with body fat differently in men and women. We also found significant differences in the associations of AFR with DTI metrics between sexes. These sex differences are mirrored in the associations of SBP and age with DTI metrics. Moreover, these sex differences in the AFR and SBP associations with DTI metrics persist when controlling for age. Taken together, these findings suggest that there are inherent sex-driven differences in how brain health is associated with vascular risk factors such as obesity.

Conventionally, cerebrovascular reactivity (CVR) is estimated as the amplitude of the hemodynamic response to vascular stimuli. While the CVR amplitude has established clinical utility, the temporal characteristics of CVR have been increasingly explored and may yield even more pathology-sensitive parameters. This work is motivated by the current need to evaluate the feasibility of dCVR modeling in various noise conditions. In this work, we present a comparison of several recently published model-based deconvolution approaches for estimating h(t), including maximum a posterior likelihood (MAP), inverse logit (IL), canonical correlation analysis (CCA), and basis expansion (using Gamma and Laguerre basis sets). To aid the comparison, we devised a novel simulation framework that allowed us to target a wide range of SNRs, ranging from 10 to −7 dB, representative of both task and resting-state CO2 changes. In addition, we built ground-truth h(t) into our simulation framework, overcoming the practical limitation that the true h(t) is unknown in methodological evaluations. Moreover, to best represent realistic noise found in fMRI scans, we extracted it from in-vivo resting-state scans. Furthermore, we introduce a simple optimization of the CCA method (CCAopt) and compare its performance to these existing methods. Our findings suggest that model-based methods can reasonably estimate dCVR even amidst high noise, and in a manner that is largely independent of the underlying model assumptions for each method. We also provide a quantitative basis for making methodological choices, based on the desired dCVR parameters, the estimation accuracy and computation time. The BEL method provided the highest accuracy and robustness, followed by the CCAopt and IL methods. Of the three, the CCAopt method required the lowest computational time. These findings lay the foundation for wider adoption of dCVR estimation in CVR mapping.

The long-term consequences of coronavirus disease 2019 (COVID-19) on brain physiology and function are not yet well understood. From the recently described NeuroCOVID-19 study, we examined cerebral blood flow (CBF) in 50 participants recruited to one of two groups: 1) adults who previously self-isolated at home due to COVID-19 (n = 39; 116.5 ± 62.2 days since positive diagnosis), or 2) controls who experienced flu-like symptoms but had a negative COVID-19 diagnosis (n = 11). Participants underwent arterial spin labeling magnetic resonance imaging at 3 T to yield measures of CBF. Voxel-wise analyses of CBF were performed to assess for between-group differences, after controlling for age and sex. Relative to controls, the COVID-19 group exhibited decreased CBF in the thalamus, orbitofrontal cortex, and regions of the basal ganglia. Within the COVID-19 group, CBF differences in occipital and parietal regions were observed between those with (n = 11) and without (n = 28) self-reported on-going fatigue. These results suggest long-term changes in brain physiology in adults across the post-COVID-19 timeframe. Moreover, CBF may aid in understanding the heterogeneous symptoms of the post-COVID-19 condition. Future longitudinal studies are needed to further characterize the consequences of COVID-19 on the brain.

Studies of healthy brain aging have reported diffusivity patterns associated with white matter degeneration using diffusion tensor imaging (DTI), which assumes that diffusion measured at the typical b-value (approximately 1000 s/mm2) is Gaussian. Diffusion kurtosis imaging (DKI) is an extension of DTI that measures non-Gaussian diffusion (kurtosis) to better capture microenvironmental changes by incorporating additional data at a higher b-value. In this study, using UK Biobank data (b values of 1000 and 2000 s/mm2), we investigate (1) the extent of novel information gained from adding diffusional kurtosis to diffusivity observations in aging, and (2) how conventional DTI metrics in aging compare with diffusivity metrics derived from DKI, which are corrected for kurtosis. We find a general pattern of lower kurtosis alongside higher diffusivity among older adults. We also find differences between diffusivity metrics derived from DTI and DKI, emphasizing the importance of accounting for non-Gaussian diffusion. This work highlights the utility of measuring diffusional kurtosis as a simple addition to conventional diffusion imaging of aging.

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.Impact StatementResting-state fMRI is influenced by low-frequency physiological noise and head motion. Independent component analysis (ICA) is becoming increasingly relied on for reducing these influences, but the utility of spatial and temporal ICA remains unclear. We conducted a comparison of the performance of these two ICA types, using physiological-noise and motion time courses as reference. We found that spatial ICA is more adept at reducing motion effects, while temporal ICA performs better in dealing with physiological effects. We believe these findings provide much-needed clarity on the role of ICA, and recommend using a hybrid of tICA and sICA as a paradigm shift in resting-state fMRI.

Slow and rhythmic spontaneous oscillations of cerebral blood flow are well known to have diagnostic utility, notably frequencies of 0.008-0.03 Hz (B-waves) and 0.05-0.15Hz (Mayer waves or M waves). However, intracranial measurements of these oscillations have been difficult. Oscillations in the cerebrospinal fluid (CSF), which are influenced by the cardiac pulse wave, represent a possible avenue for non-invasively tracking these oscillations using resting-state functional MRI (rs-fMRI), and have been used to correct for vascular oscillations in rs-fMRI functional connectivity calculations. However, the relationship between low-frequency CSF and vascular oscillations is unclear. In this study, we investigate this relationship using fast simultaneous multi-slice rs-fMRI coupled with fingertip photoplethysmography (PPG). We not only extract B-wave and M-wave range spectral power from the PPG signal, but also derive the pulse-intensity ratio (PIR, a surrogate of slow blood-pressure oscillations), the second-derivative of the PPG (SDPPG, a surrogate of arterial stiffness) and heart-rate variability (HRV). The main findings of this study are: (1) signals in different CSF regions (ROIs) are not equivalent in their vascular contributions or in their associations with vascular and tissue rs-fMRI signals; (2) the PPG signal contains the highest signal contribution from the M-wave range, while PIR contains the highest signal contribution from the B-wave range; (3) in the low-frequency range, PIR is more strongly associated with rs-fMRI signal in the CSF than PPG itself, and than HRV and SDPPG; (4) PPG-related vascular oscillations only contribute to < 20% of the CSF signal in rs-fMRI, insufficient support for the assumption that low-frequency CSF signal fluctuations directly reflect vascular oscillations. These findings caution the use of CSF as a monolithic region for extracting physiological nuisance regressors in rs-fMRI applications. They also pave the way for using rs-fMRI in the CSF as a potential tool for tracking cerebrovascular health through, for instance the strong relationship between PIR and the CSF signal.

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 acquisition: 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. 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.

PurposeTo introduce a novel magnetic-resonance fingerprinting (MRF) framework with single-shot echo-planar imaging (EPI) readout to simultaneously estimate tissue T2, T1 and T2*, and integrate B1 correction.MethodsSpin-echo EPI is combined with gradient-echo EPI to achieve T2 estimation as well as T1 and T2* quantification. In the dictionary matching step, the GE-EPI data segment provides estimates of tissue T1 and T2* with additional B1 information, which are then incorporated into the T2-matching step that uses the SE-EPI data segment. In this way, biases in T2 and T2* estimates do not affect each other.ResultsAn excellent correspondence was found between our T1, T2, and T2* estimates and results obtained from standard approaches in both phantom and human scans. In the phantom scan, a linear relationship with R2>0.96 was found for all parameter estimates. The maximum error in the T2 estimate was found to be below 6%. In the in-vivo scan, similar contrast was noted between MRF and standard approaches, and values found in a small region of interest (ROI) located in the grey matter (GM) were in line with previous measurements (T2MRF=88±7ms vs T2Ref=89±11ms, T1MRF=1153±154ms vs T1Ref=1122±52ms, T2*MRF=56±4ms vs T2*Ref=53±3ms).ConclusionAdding a spin echo data segment to EPI based MRF allows accurate and robust measurements of T2, T1 and T2* relaxation times. This MRF framework is easier to implement than spiral-based MRF. It doesn’t suffer from undersampling artifacts and seems to require a smaller dictionary size that can fasten the reconstruction process.