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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.

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.

Conventional functional MRI (fMRI) with blood-oxygenation level dependent (BOLD) contrast is an important tool for mapping human brain activity non-invasively. Recent interest in quantitative fMRI has renewed the importance of oxidative neuroenergetics as reflected by cerebral metabolic rate of oxygen consumption (CMRO2) to support brain function. Dynamic CMRO2 mapping by calibrated fMRI require multi-modal measurements of BOLD signal along with cerebral blood flow (CBF) and/or volume (CBV). In human subjects this “calibration” is typically performed using a gas mixture containing small amounts of carbon dioxide and/or oxygen-enriched medical air, which are thought to produce changes in CBF (and CBV) and BOLD signal with minimal or no CMRO2 changes. However non-human studies have demonstrated that the “calibration” can also be achieved without gases, revealing good agreement between CMRO2 changes and underlying neuronal activity (e.g., multi-unit activity and local field potential). Given the simpler set-up of gas-free calibrated fMRI, there is evidence of recent clinical applications for this less intrusive direction. This up-to-date review emphasizes technological advances for such translational gas-free calibrated fMRI experiments, also covering historical progression of the calibrated fMRI field that is impacting neurological and neurodegenerative investigations of the human brain.

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.

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.

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.

Neurological symptoms associated with coronavirus disease 2019 (COVID-19), such as fatigue and smell/taste changes, persist beyond infection. However, little is known of brain physiology in the post-COVID-19 timeframe.To determine whether adults who experienced flu-like symptoms due to COVID-19 would exhibit cerebral blood flow (CBF) alterations in the weeks/months beyond infection, relative to controls who experienced flu-like symptoms but tested negative for COVID-19.Prospective observational.A total of 39 adults who previously self-isolated at home due to COVID-19 (41.9 ± 12.6 years of age, 59% female, 116.5 ± 62.2 days since positive diagnosis) and 11 controls who experienced flu-like symptoms but had a negative COVID-19 diagnosis (41.5 ± 13.4 years of age, 55% female, 112.1 ± 59.5 since negative diagnosis).A 3.0 T; T1-weighted magnetization-prepared rapid gradient and echo-planar turbo gradient-spin echo arterial spin labeling sequences.Arterial spin labeling was used to estimate CBF. A self-reported questionnaire assessed symptoms, including ongoing fatigue. CBF was compared between COVID-19 and control groups and between those with (n = 11) and without self-reported ongoing fatigue (n = 28) within the COVID-19 group.Between-group and within-group comparisons of CBF were performed in a voxel-wise manner, controlling for age and sex, at a family-wise error rate of 0.05.Relative to controls, the COVID-19 group exhibited significantly decreased CBF in subcortical regions including the thalamus, orbitofrontal cortex, and basal ganglia (maximum cluster size = 6012 voxels and maximum t-statistic = 5.21). Within the COVID-19 group, significant CBF differences in occipital and parietal regions were observed between those with and without self-reported on-going fatigue.These cross-sectional data revealed regional CBF decreases in the COVID-19 group, suggesting the relevance of brain physiology in the post-COVID-19 timeframe. This research may help elucidate the heterogeneous symptoms of the post-COVID-19 condition.2.Stage 3.

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, most commonly carbon dioxide (CO

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.

Diffusion tensor imaging (DTI) consistently detects increased mean diffusivity and decreased fractional anisotropy with advancing age in regions of primarily single white matter (WM) fiber populations, but findings have been inconsistent in regions of more complex fiber architecture. Given that DTI remains more common for characterizing aging WM than advanced diffusion MRI models due to DTI's simplicity, robustness, and efficiency, it is critical to strive to maximize the information extracted from DTI across the entire WM. The present study uses an orthogonal diffusion tensor decomposition based on the 3 eigenvalue moments (mean diffusivity, norm of anisotropy, and mode of anisotropy), yielding clear voxelwise degeneration patterns across the WM, including regions of complex fiber architecture. This indicates that the previous challenges of DTI in these regions were due to the choice of tensor decomposition rather than the DTI model itself. This study therefore presents a revised view of DTI of aging WM and indicates how age-related degeneration in complex fiber architecture can manifest in forms other than decreased fractional anisotropy.

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 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.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been extensively used to study brain aging, but the age effect on the frequency content of the rs-fMRI signal has scarcely been examined. Moreover, the neuronal implications of such age effects and age-sex interaction remain unclear. In this study, we examined the effects of age and sex on the rs-fMRI signal frequency using the Leipzig mind-brain-body data set. Over a frequency band of up to 0.3 Hz, we found that the rs-fMRI fluctuation frequency is higher in the older adults, although the fluctuation amplitude is lower. The rs-fMRI signal frequency is also higher in men than in women. Both age and sex effects on fMRI frequency vary with the frequency band examined but are not found in the frequency of physiological-noise components. This higher rs-fMRI frequency in older adults is not mediated by the electroencephalograph (EEG)-frequency increase but a likely link between fMRI signal frequency and EEG entropy, which vary with age and sex. Additionally, in different rs-fMRI frequency bands, the fMRI-EEG amplitude ratio is higher in young adults. This is the first study to investigate the neuronal contribution to age and sex effects in the frequency dimension of the rs-fMRI signal and may lead to the development of new, frequency-based rs-fMRI metrics. Our study demonstrates that Fourier analysis of the fMRI signal can reveal novel information about aging. Furthermore, fMRI and EEG signals reflect different aspects of age- and sex-related brain differences, but the signal frequency and complexity, instead of amplitude, may hold their link.

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.

Studies of healthy brain aging traditionally report diffusivity patterns associated with white matter degeneration using diffusion tensor imaging (DTI), which assumes that diffusion measured at typical b-values (approximately 1000 s/mm

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.

Heart rate variability (HRV) is a well-established surrogate of cardiac and emotional health that reflects the balance between sympathetic and parasympathetic activity of the autonomic nervous system. We examined the impact of manipulating exhalation to inhalation ratio (E:I) on HRV, without altering the intrinsic breathing rate of healthy individuals. We hypothesized that a longer exhalation relative to inhalation (E:I > 1) would shift HRV metrics in a direction consistent with increased parasympathetic activity. Twenty-eight individuals (16 young [6M, age = 21-28];12 older adults [6M, age = 66-80]) completed a task during which they paced breathing according to their intrinsic respiratory rate, but altered onset of exhalation and inhalation according to 1:1 sound cue (equal exhalation and inhalation duration) or 2:1 cue (exhalation twice as long as inhalation). Paced 1:1 breathing followed these task conditions to examine residual effects. Estimates of actual E:I ratio based on thoracic movement were 1.08(0.16) for 1:1 task and 1.33(0.20) for 2:1 task, which were significantly different from one another. HRV metrics derived from electrocardiogram included root mean square of the successive differences between normal heartbeats (RMSSD) and high-frequency (HF) HRV. Analyses of HRV metrics by block showed that RMSSD and HF-HRV were higher in the 2:1 task condition compared to 1:1. Time series analysis showed that HF-HRV increased after the end of the 2:1 task block and remained elevated for four minutes. These findings suggest that longer duration of exhalation relative to inhalation, without altering breathing rate, acutely increased RMSSD and HF-HRV, consistent with enhancement of cardiac vagal tone.

Background: Subjective cognitive decline (SCD) is associated with increased risk of developing Alzheimer’s disease (AD). However, the underlying mechanisms for this association remain unclear. Neuroimaging studies suggest the earliest AD-related changes are large-scale network disruptions, beginning in the posterior default mode (pDMN) network. Objective: To examine the association between SCD and pDMN network connectivity with medial temporal lobe (MTL) regions using resting-state functional magnetic resonance imaging. Methods: Forty-nine participants with either SCD (n = 23, 12 females; mean age: 70.7 (5.5)) or who were cognitively unimpaired (CU; n = 26, 16 females, mean age: 71.42 (7.3)) completed the Memory Functioning Questionnaire, a measure of subjective memory, and underwent resting state functional MRI at 3 Tesla. Functional connectivity between the posterior cingulate cortex (PCC), as the key pDMN node, and MTL regions were compared between SCD and CU groups. Further, the association between pDMN-MTL connectivity and the Frequency of Forgetting subscale of the Memory Functioning Questionnaire was examined. Results: Connectivity between the PCC-MTL was observed in the CU group but was absent in SCD (t(47) = 2.69, p = 0.01). Across all participants, self-perception of frequency of forgetting, but not objective memory, was strongly correlated with connectivity between the PCC-left parahippocampal gyrus (r = 0.43, p = 0.002). Conclusion: These findings support the hypothesis that increased AD risk in SCD may be mediated by disrupted pDMN-parahippocampal connectivity. In addition, these findings suggest that frequency of forgetting may serve as a potential biomarker of SCD due to incipient AD.

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.

γ-Aminobutyric acid (GABA), a primary inhibitory neurotransmitter in the brain, plays a significant role in aging and in neurodegenerative disorders, including Alzheimer’s disease (AD). We investigated the relationship between GABA levels in the dorsomedial/dorsoanterolateral prefrontal cortex (DM/DA-PFC) and memory in high-AD risk participants. Thirty-eight participants (14 Cognitively Normal [CN], 11 with Subjective Cognitive Decline (SCD), and 13 Mild Cognitive Impairment [MCI]) underwent magnetic resonance spectroscopy at 3 Tesla. SCD and MCI participants were grouped together to form a single high-AD risk group (N = 24) for the purposes of statistical analyses. Partial correlations of GABA+/Cr level with verbal memory, assessed on California Verbal Learning Test-II, and nonverbal memory, assessed on Brief Visuospatial Memory Test and Rey-Osterrieth test, were examined separately within the high-AD risk and CN groups. GABA+/Cr levels were positively correlated with long-delayed verbal memory (r = 0.69, P = 0.009) and immediate nonverbal memory (r = 0.97, P = 0.03) in high-AD risk, but not in CN participants. These results remained significant after controlling for depression. These preliminary findings, which require replication due to the limited sample sizes, are the first report of an association between GABA+/Cr levels within the DM/DA-PFC and memory performance in high-AD risk individuals.

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.

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 CO

Diffusion tensor imaging (DTI) has been used extensively to investigate white matter (WM) microstructural changes during healthy adult aging. However, WM fibres are known to shrink throughout the lifespan, leading to larger interstitial spaces with age. This could allow more extracellular free water molecules to bias DTI metrics, which are relied upon to provide WM microstructural information. Using a cohort of 212 participants, we demonstrate that WM microstructural changes in aging are potentially less pronounced than previously reported once the free water compartment is eliminated. After free water elimination, DTI parameters show age-related differences that match histological evidence of myelin degradation and debris accumulation. The fraction of free water is further shown to associate better with age than any of the conventional DTI parameters. Our findings suggest that DTI analyses involving free water are likely to yield novel insight from retrospective re-analysis of data and to answer new questions in ongoing DTI studies of brain aging.

Neuroplasticity accompanying learning is a key mediator of stroke rehabilitation. Training in playing music in healthy populations and patients with movement disorders requires resources within motor, sensory, cognitive, and affective systems, and coordination among these systems. We investigated effects of music-supported therapy (MST) in chronic stroke on motor, cognitive, and psychosocial functions compared to conventional physical training (GRASP). Twenty-eight adults with unilateral arm and hand impairment were randomly assigned to MST (n = 14) and GRASP (n = 14) and received 30 h of training over a 10-week period. The assessment was conducted at four time points: before intervention, after 5 weeks, after 10 weeks, and 3 months after training completion. As for two of our three primary outcome measures concerning motor function, all patients slightly improved in Chedoke-McMaster Stroke Assessment hand score, while the time to complete Action Research Arm Test became shorter in the MST group. The third primary outcome measure for well-being, Stroke Impact Scale, was improved for emotion and social communication earlier in MST and coincided with the improved executive function for task switching and music rhythm perception. The results confirmed previous findings and expanded the potential usage of MST for enhancing quality of life in community-dwelling chronic-stage survivors.

Movement is traditionally viewed as a process that involves motor brain regions. However, movement also implicates non-motor regions such as prefrontal and parietal cortex, regions whose integrity may thus be important for motor recovery after stroke. Importantly, focal brain damage can affect neural functioning within and between distinct brain networks implicated in the damage. The aim of this study is to investigate how resting state connectivity (rs-connectivity) within and between motor and frontoparietal networks are affected post-stroke in correlation with motor outcome. Twenty-seven participants with chronic stroke with unilateral upper limb deficits underwent motor assessments and magnetic resonance imaging. Participants completed the Chedoke-McMaster Stroke Assessment as a measure of arm (CMSA-Arm) and hand (CMSA-Hand) impairment and the Action Research Arm Test (ARAT) as a measure of motor function. We used a seed-based rs-connectivity approach defining the motor (seed = contralesional primary motor cortex (M1)) and frontoparietal (seed = contralesional dorsolateral prefrontal cortex (DLPFC)) networks. We analyzed the rs-connectivity within each network (intra-network connectivity) and between both networks (inter-network connectivity), and performed correlations between: a) intra-network connectivity and motor assessment scores; b) inter-network connectivity and motor assessment scores. We found: a) Participants with high rs-connectivity within the motor network (between M1 and supplementary motor area) have higher CMSA-Hand stage (z = 3.62, p = 0.003) and higher ARAT score (z = 3.41, p = 0.02). Rs-connectivity within the motor network was not significantly correlated with CMSA-Arm stage (z = 1.83, p > 0.05); b) Participants with high rs-connectivity within the frontoparietal network (between DLPFC and mid-ventrolateral prefrontal cortex) have higher CMSA-Hand stage (z = 3.64, p = 0.01). Rs-connectivity within the frontoparietal network was not significantly correlated with CMSA-Arm stage (z = 0.93, p = 0.03) or ARAT score (z = 2.53, p = 0.05); and c) Participants with high rs-connectivity between motor and frontoparietal networks have higher CMSA-Hand stage (rs = 0.54, p = 0.01) and higher ARAT score (rs = 0.54, p = 0.009). Rs-connectivity between the motor and frontoparietal networks was not significantly correlated with CMSA-Arm stage (rs = 0.34, p = 0.13). Taken together, the connectivity within and between the motor and frontoparietal networks correlate with motor outcome post-stroke. The integrity of these regions may be important for an individual's motor outcome. Motor-frontoparietal connectivity may be a potential biomarker of motor recovery post-stroke.

Resting‐state functional magnetic resonance imaging (rs‐fMRI) is frequently used to study brain function; but, it is unclear whether BOLD‐signal fluctuation amplitude and functional connectivity are associated with vascular factors, and how vascular‐health factors are reflected in rs‐fMRI metrics in the healthy population. As arterial stiffening is a known age‐related cardiovascular risk factor, we investigated the associations between aortic stiffening (as measured using pulse‐wave velocity [PWV]) and rs‐fMRI metrics. We used cardiac MRI to measure aortic PWV (an established indicator of whole‐body vascular stiffness), as well as dual‐echo pseudo‐continuous arterial‐spin labeling to measure BOLD and CBF dynamics simultaneously in a group of generally healthy adults. We found that: (1) higher aortic PWV is associated with lower variance in the resting‐state BOLD signal; (2) higher PWV is also associated with lower BOLD‐based resting‐state functional connectivity; (3) regions showing lower connectivity do not fully overlap with those showing lower BOLD variance with higher PWV; (4) CBF signal variance is a significant mediator of the above findings, only when averaged across regions‐of‐interest. Furthermore, we found no significant association between BOLD signal variance and systolic blood pressure, which is also a known predictor of vascular stiffness. Age‐related vascular stiffness, as measured by PWV, provides a unique scenario to demonstrate the extent of vascular bias in rs‐fMRI signal fluctuations and functional connectivity. These findings suggest that a substantial portion of age‐related rs‐fMRI differences may be driven by vascular effects rather than directly by brain function.

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.

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 positive 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.

Prospective motion correction is a promising candidate solution to suppress the effects of head motion during fMRI, ideally allowing the imaging plane to remain fixed with respect to the moving head. Residual signal artifacts may remain, however, because head motion in relation to a fixed multi-channel receiver coil (with non-uniform sensitivity maps) can potentially introduce unwanted signal variations comparable to the weak fMRI BOLD signal (~1%-4% at 1.5-3.0T). The present work aimed to investigate the magnitude of these residual artifacts, and characterize the regime over which prospective motion correction benefits from adjusting sensitivity maps to reflect relative positional change between the head and the coil. Numerical simulations were used to inform human fMRI experiments. The simulations indicated that for axial imaging within a commonly used 12-channel head coil, 5° of head rotation in-plane produced artifact signal changes of ~3%. Subsequently, six young adults were imaged with and without overt head motions of approximately this extent, with and without prospective motion correction using the Prospective Acquisition CorrEction (PACE) method, and with and without sensitivity map adjustments. Sensitivity map adjustments combined with PACE strongly protected against the artifacts of interest, as indicated by comparing three metrics of data quality (number of activated voxels, Dice coefficient of activation overlap, temporal standard deviation of baseline fMRI timeseries data) across the different experimental conditions. It is concluded that head motion in relation to a fixed multi-channel coil can adversely affect fMRI with prospective motion correction, and that sensitivity map adjustment can mitigate this effect at 3.0T.

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

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.

The objective of this study was to determine whether physiological fluctuations in white matter (PFWM) on resting-state functional magnetic resonance images could be used as an index of neurodegeneration and Alzheimer's disease (AD). Using resting-state functional magnetic resonance image data from participants in the Alzheimer's Disease Neuroimaging Initiative, PFWM was compared across cohorts: cognitively healthy, mild cognitive impairment, or probable AD. Secondary regression analyses were conducted between PFWM and neuroimaging, cognitive, and cerebrospinal fluid biomarkers. There was an effect of cohort on PFWM (t = 5.08, degree of freedom [df] = 424, p5.7 × 10(-7)), after accounting for nuisance effects from head displacement and global signal (t6.16). From the neuroimaging data, PFWM was associated with glucose metabolism (t = -2.93, df = 96, p = 0.004) but not ventricular volume (p0.49) or hippocampal volume (p0.44). From the cognitive data, PFWM was associated with composite memory (t = -3.24, df = 149, p = 0.0015) but not executive function (p0.21). PFWM was not associated with cerebrospinal fluid biomarkers. In one final omnibus model to explain PFWM (n = 124), glucose metabolism (p = 0.04) and cohort (p = 0.008) remained significant, as were global and head motion root-mean-square terms, whereas memory was not (p = 0.64). PFWM likely reflects end-arteriole intracranial pulsatility effects that may provide additional diagnostic potential in the context of AD neurodegeneration.

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.

The blood oxygenation level-dependent (BOLD) phenomenon has profoundly revolutionized neuroscience, with applications ranging from normal brain development and aging, to brain disorders and diseases. While the BOLD effect represents an invaluable tool to map brain function, it does not measure neural activity directly; rather, it reflects changes in blood oxygenation resulting from the relative balance between cerebral oxygen metabolism (through neural activity) and oxygen supply (through cerebral blood flow and volume). As such, there are cases in which BOLD signals might be dissociated from neural activity, leading to misleading results. The emphasis of this review is to develop a critical perspective for interpreting BOLD results, through a comprehensive consideration of BOLD's metabolic and vascular underpinnings. We demonstrate that such an understanding is especially important under disease or resting conditions. We also describe state-of-the-art acquisition and analytical techniques to reveal physiological information on the mechanisms underlying measured BOLD signals. With these goals in mind, this review is structured to provide a fundamental understanding of: 1) the physiological and physical sources of the BOLD contrast; 2) the extraction of information regarding oxidative metabolism and cerebrovascular reactivity from the BOLD signal, critical to investigating neuropathology; and 3) the fundamental importance of metabolic and vascular mechanisms for interpreting resting-state BOLD measurements.

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 = 2 seconds vs. 323 ms) 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.

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 measurement of cerebral blood volume (CBV) has been the topic of numerous neuroimaging studies. To date, however, most in vivo imaging approaches can only measure CBV summed over all types of blood vessels, including arterial, capillary and venous vessels in the microvasculature (i.e. total CBV or CBVtot). As different types of blood vessels have intrinsically different anatomy, function and physiology, the ability to quantify CBV in different segments of the microvascular tree may furnish information that is not obtainable from CBVtot, and may provide a more sensitive and specific measure for the underlying physiology. This review attempts to summarize major efforts in the development of MRI techniques to measure arterial (CBVa) and venous CBV (CBVv) separately. Advantages and disadvantages of each type of method are discussed. Applications of some of the methods in the investigation of flow-volume coupling in healthy brains, and in the detection of pathophysiological abnormalities in brain diseases such as arterial steno-occlusive disease, brain tumors, schizophrenia, Huntington's disease, Alzheimer's disease, and hypertension are demonstrated. We believe that the continual development of MRI approaches for the measurement of compartment-specific CBV will likely provide essential imaging tools for the advancement and refinement of our knowledge on the exquisite details of the microvasculature in healthy and diseased brains.

Neuroplasticity accompanying learning is a key mediator of stroke rehabilitation. Training in playing music in healthy populations and patients with movement disorders requires resources within motor, sensory, cognitive, and affective systems, and coordination among these systems. We investigated effects of music-supported therapy (MST) in chronic stroke on motor, cognitive, and psychosocial functions compared to conventional physical training (GRASP). Twenty-eight adults with unilateral arm and hand impairment were randomly assigned to MST (n = 14) and GRASP (n = 14) and received 30 h of training over a 10-week period. The assessment was conducted at four time points: before intervention, after 5 weeks, after 10 weeks, and 3 months after training completion. As for two of our three primary outcome measures concerning motor function, all patients slightly improved in Chedoke-McMaster Stroke Assessment hand score, while the time to complete Action Research Arm Test became shorter in the MST group. The third primary outcome measure for well-being, Stroke Impact Scale, was improved for emotion and social communication earlier in MST and coincided with the improved executive function for task switching and music rhythm perception. The results confirmed previous findings and expanded the potential usage of MST for enhancing quality of life in community-dwelling chronic-stage survivors.

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 seconds (long-TR) and 0.323 seconds (short-TR), and the data were processed using 8 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.

Brain aging and associated neurodegeneration constitute a major societal challenge as well as one for the neuroimaging community. A full understanding of the physiological mechanisms underlying neurodegeneration still eludes medical researchers, fuelling the development of in vivo neuroimaging markers. Hence it is increasingly recognized that our understanding of neurodegenerative processes likely will depend upon the available information provided by imaging techniques. At the same time, the imaging techniques are often developed in response to the desire to observe certain physiological processes. In this context, functional MRI (fMRI), which has for decades provided information on neuronal activity, has evolved into a large family of techniques well suited for in vivo observations of brain physiology. Given the rapid technical advances in fMRI in recent years, this review aims to summarize the physiological basis of fMRI observations in healthy aging as well as in age-related neurodegeneration. This review focuses on in-vivo human brain imaging studies in this review and on disease features that can be imaged using fMRI methods. In addition to providing detailed literature summaries, this review also discusses future directions in the study of brain physiology using fMRI in the clinical setting.

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.