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IntroductionPost-acute COVID syndrome (PACS) is a growing concern, given its impact on mental health and quality of life. However, its effects on cerebral white matter remain poorly understood, particularly in non-hospitalized cohorts. The goals of this cross-sectional, observational study were to examine (1) whether PACS was associated with distinct alterations in white matter microstructure, compared to symptom-matched non-COVID viral infection; and (2) whether microstructural alterations correlated with indices of post-COVID emotional health.MethodsData were collected for 54 symptomatic individuals who tested positive for COVID-19 (mean age 41 ± 12 yrs., 36 female) and 14 controls who tested negative for COVID-19 (mean age 41 ± 14 yrs., 8 female), with both groups assessed an average of 4–5 months after COVID testing. Diffusion magnetic resonance imaging data were collected, and emotional health was assessed via the NIH emotion toolbox, with summary scores indexing social satisfaction, well-being and negative affect.ResultsDespite similar symptoms, the COVID-19 group had reduced mean and axial diffusivity, along with increased mean kurtosis and neurite dispersion, in deep white matter. After adjusting for social satisfaction, higher levels of negative affect in the COVID-19 group were also correlated with increased mean kurtosis and reduced free water in white matter.DiscussionThese results provide preliminary evidence that indices of white matter microstructure distinguish PACS from symptomatic non-COVID infection. Moreover, white matter effects seen in PACS correlate with the severity of emotional sequelae, providing novel insights into this highly prevalent disorder.

IntroductionPhysiological 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.MethodsIn 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).ResultsOur 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.DiscussionWhile 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.

Abstract Introduction Post‐acute coronavirus disease 2019 (COVID‐19) syndrome (PACS) is a growing concern, with headache being a particularly debilitating symptom with high prevalence. The long‐term effects of COVID‐19 and post‐COVID headache on brain function remain poorly understood, particularly among non‐hospitalized individuals. This study focused on the power‐law scaling behavior of functional brain dynamics, indexed by the Hurst exponent (H). This measure is suppressed during physiological and psychological distress and was thus hypothesized to be reduced in individuals with post‐COVID syndrome, with greatest reductions among those with persistent headache. Methods Resting‐state blood oxygenation level‐dependent (BOLD) functional magnetic resonance imaging data were collected for 57 individuals who had COVID‐19 (32 with no headache, 14 with ongoing headache, 11 recovered) and 17 controls who had cold and flu‐like symptoms but tested negative for COVID‐19. Individuals were assessed an average of 4–5 months after COVID testing, in a cross‐sectional, observational study design. Results No significant differences in H values were found between non‐headache COVID‐19 and control groups., while those with ongoing headache had significantly reduced H values, and those who had recovered from headache had elevated H values, relative to non‐headache groups. Effects were greatest in temporal, sensorimotor, and insular brain regions. Reduced H in these regions was also associated with decreased BOLD activity and local functional connectivity. Conclusions These findings provide new insights into the neurophysiological mechanisms that underlie persistent post‐COVID headache, with reduced BOLD scaling as a potential biomarker that is specific to this debilitating condition.

IntroductionThe long-term impact of COVID-19 on brain function remains poorly understood, despite growing concern surrounding post-acute COVID-19 syndrome (PACS). The goal of this cross-sectional, observational study was to determine whether there are significant alterations in resting brain function among non-hospitalized individuals with PACS, compared to symptomatic individuals with non-COVID infection.MethodsData were collected for 51 individuals who tested positive for COVID-19 (mean age 41±12 yrs., 34 female) and 15 controls who had cold and flu-like symptoms but tested negative for COVID-19 (mean age 41±14 yrs., 9 female), with both groups assessed an average of 4-5 months after COVID testing. None of the participants had prior neurologic, psychiatric, or cardiovascular illness. Resting brain function was assessed via functional magnetic resonance imaging (fMRI), and self-reported symptoms were recorded.ResultsIndividuals with COVID-19 had lower temporal and subcortical functional connectivity relative to controls. A greater number of ongoing post-COVID symptoms was also associated with altered functional connectivity between temporal, parietal, occipital and subcortical regions.DiscussionThese results provide preliminary evidence that patterns of functional connectivity distinguish PACS from non-COVID infection and correlate with the severity of clinical outcome, providing novel insights into this highly prevalent disorder.

IntroductionIn the context of functional magnetic resonance imaging (fMRI), carbon dioxide (CO2) is a well-known vasodilator that has been widely used to monitor and interrogate vascular physiology. Moreover, spontaneous fluctuations in end-tidal carbon dioxide (PETCO2) reflects changes in arterial CO2 and has been demonstrated as the largest physiological noise source for denoising the low-frequency range of the resting-state fMRI (rs-fMRI) signal. However, the majority of rs-fMRI studies do not involve CO2 recordings, and most often only heart rate and respiration are recorded. While the intrinsic link between these latter metrics and CO2 led to suggested possible analytical models, they have not been widely applied.MethodsIn this proof-of-concept study, we propose a deep-learning (DL) approach to reconstruct CO2 and PETCO2 data from respiration waveforms in the resting state.ResultsWe demonstrate that the one-to-one mapping between respiration and CO2 recordings can be well predicted using fully convolutional networks (FCNs), achieving a Pearson correlation coefficient (r) of 0.946 ± 0.056 with the ground truth CO2. Moreover, dynamic PETCO2 can be successfully derived from the predicted CO2, achieving r of 0.512 ± 0.269 with the ground truth. Importantly, the FCN-based methods outperform previously proposed analytical methods. In addition, we provide guidelines for quality assurance of respiration recordings for the purposes of CO2 prediction.DiscussionOur results demonstrate that dynamic CO2 can be obtained from respiration-volume using neural networks, complementing the still few reports in DL of physiological fMRI signals, and paving the way for further research in DL based bio-signal processing.

The influence of the apolipoprotein E ε4 allele (APOE4) on brain microstructure of cognitively normal older adults remains incompletely understood, in part due to heterogeneity within study populations. In this study, we examined white-matter microstructural integrity in cognitively normal older adults as a function of APOE4 carrier status using conventional diffusion-tensor imaging (DTI) and the novel orthogonal-tensor decomposition (DT-DOME), accounting for the effects of age and sex. Age associations with white-matter microstructure did not significantly depend on APOE4 status, but did differ between sexes, emphasizing the importance of accounting for sex differences in APOE research. Moreover, we found the DT-DOME to be more sensitive than conventional DTI metrics to such age-related and sex effects, especially in crossing WM fiber regions, and suggest their use in further investigation of white matter microstructure across the life span in health and disease.

Conventionally, cerebrovascular reactivity (CVR) is estimated as the amplitude of the hemodynamic response to vascular stimuli, most commonly carbon dioxide (CO2). While the CVR amplitude has established clinical utility, the temporal characteristics of CVR (dCVR) 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 experimental conditions. In this work, we present a comparison of several recently published/utilized model-based deconvolution (response estimation) approaches for estimating the CO2 response function h(t), including maximum a posteriori 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 incorporates 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 conventional limitation that the true h(t) is unknown. Moreover, to best represent realistic noise found in fMRI scans, we extracted noise 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 accurately estimate dCVR even amidst high noise (i.e. resting-state), 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 has the lowest computational requirements. These findings lay the foundation for wider adoption of dCVR estimation in CVR mapping.

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.

The desire to enhance the sensitivity and specificity of resting-state (rs-fMRI) measures has prompted substantial recent research into removing noise components. Chief among contributions to noise in rs-fMRI are physiological processes, and the neuronal implications of respiratory-volume variability (RVT), a main rs-fMRI-relevant physiological process, is incompletely understood. The potential implications of RVT in modulating and being modulated by autonomic nervous regulation, has yet to be fully understood by the rs-fMRI community. In this work, we use high-density electroencephalography (EEG) along with simultaneously acquired RVT recordings to help address this question. We hypothesize that (1) there is a significant relationship between EEG and RVT in multiple EEG bands, and (2) that this relationship varies by brain region. Our results confirm our first hypothesis, although all brain regions are shown to be equally implicated in RVT-related EEG-signal fluctuations. The lag between RVT and EEG is consistent with previously reported values. However, an interesting finding is related to the polarity of the correlation between RVT and EEG. Our results reveal potentially two main regimes of EEG-RVT association, one in which EEG leads RVT with a positive association between the two, and one in which RVT leads EEG but with a negative association between the two. We propose that these two patterns can be interpreted differently in terms of the involvement of higher cognition. These results further suggest that treating RVT simply as noise is likely a questionable practice, and that more work is needed to avoid discarding cognitively relevant information when performing physiological correction rs-fMRI.

Task and resting-state functional MRI (fMRI) is primarily based on the same blood-oxygenation level-dependent (BOLD) phenomenon that MRI-based cerebrovascular reactivity (CVR) mapping has most commonly relied upon. This technique is finding an ever-increasing role in neuroscience and clinical research as well as treatment planning. The estimation of CVR has unique applications in and associations with fMRI. In particular, CVR estimation is part of a family of techniques called calibrated BOLD fMRI, the purpose of which is to allow the mapping of cerebral oxidative metabolism (CMRO2) using a combination of BOLD and cerebral-blood flow (CBF) measurements. Moreover, CVR has recently been shown to be a major source of vascular bias in computing resting-state functional connectivity, in much the same way that it is used to neutralize the vascular contribution in calibrated fMRI. Furthermore, due to the obvious challenges in estimating CVR using gas challenges, a rapidly growing field of study is the estimation of CVR without any form of challenge, including the use of resting-state fMRI for that purpose. This review addresses all of these aspects in which CVR interacts with fMRI and the role of CVR in calibrated fMRI, provides an overview of the physiological biases and assumptions underlying hypercapnia-based CVR and calibrated fMRI, and provides a view into the future of non-invasive CVR measurement.

Damage to the fornix leads to significant memory impairment and executive dysfunction and is associated with dementia risk. We sought to identify if fornix integrity and fiber length are disrupted in mild cognitive impairment (MCI) and how they associate with cognition. Data from 14 healthy older adult controls (HCs) and 17 subjects with non-amnestic MCI (n-aMCI) were analyzed. Diffusion tensor imaging (DTI) at 1.5 Tesla MRI was performed to enable manual tracing of the fornix and calculation of DTI parameters. Higher fractional anisotropy of body and column of the fornix was associated with better executive functioning and memory, more strongly in the HC than in the n-aMCI group. Fornix fiber tract length (FTL) was associated with better executive function, more strongly in the n-aMCI than in the HC group, and with better memory, more strongly in the HC than in the n-aMCI group. These results highlight a decline in the contributions of the fornix to cognition in n-aMCI and suggest that maintenance of fornix FTL is essential for sustaining executive functioning in people with n-aMCI.

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.

The frequency characteristics of the resting-state BOLD fMRI (rs-fMRI) signal are of increasing scientific interest, as we discover more frequency-specific biological interpretations. In this work, we use variational mode decomposition (VMD) to precisely decompose the rs-fMRI time series into its intrinsic mode functions (IMFs) in a data-driven manner. The accuracy of the VMD decomposition of constituent IMFs is verified through simulations, with higher reconstruction accuracy and much-reduced mode mixing relative to previous methods. Furthermore, we examine the relative contribution of the VMD-derived modes (frequencies) to the rs-fMRI signal as well as functional connectivity measurements. Our primary findings are: (1) The rs-fMRI signal within the 0.01–0.25 Hz range can be consistently characterized by four intrinsic frequency clusters, centered at 0.028 Hz (IMF4), 0.080 Hz (IMF3), 0.15 Hz (IMF2) and 0.22 Hz (IMF1); (2) these frequency clusters were highly reproducible, and independent of rs-fMRI data sampling rate; (3) not all frequencies were associated with equivalent network topology, in contrast to previous findings. In fact, while IMF4 is most likely associated with physiological fluctuations due to respiration and pulse, IMF3 is most likely associated with metabolic processes, and IMF2 with vasomotor activity. Both IMF3 and IMF4 could produce the brain-network topology typically observed in fMRI, whereas IMF1 and IMF2 could not. These findings provide initial evidence of feasibility in decomposing the rs-fMRI signal into its intrinsic oscillatory frequencies in a reproducible manner.

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.