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Abstract The ultimate goal of calibrated fMRI is the quantitative imaging of oxygen metabolism (CMRO 2), and this has been the focus of numerous methods and approaches. However, one underappreciated aspect of this quest is that in the drive to measure CMRO 2 , many other physiological parameters of interest are often acquired along the way. This can significantly increase the value of the dataset, providing greater information that is clinically relevant, or detail that can disambiguate the cause of signal variations. This can also be somewhat of a double-edged sword: calibrated fMRI experiments combine multiple parameters into a physiological model that requires multiple steps, thereby providing more opportunity for error propagation and increasing the noise and error of the final derived values. As with all measurements, there is a trade-off between imaging time, spatial resolution, coverage, and accuracy. In this review, we provide a brief overview of the benefits and pitfalls of extracting multiparametric measurements of cerebral physiology through calibrated fMRI experiments. Highlights • Calibrated fMRI experiments produce multiple physiological parameter maps. • Combining multiparametric physiological data provides useful clinical information. • Image acquisition schemes and model assumptions must be carefully considered. [ABSTRACT FROM AUTHOR]

Abstract Cerebral Autoregulation (CA), defined as the ability of the cerebral vasculature to maintain stable levels of blood flow despite changes in systemic blood pressure, is a critical factor in neurophysiological health. Magnetic resonance imaging (MRI) is a powerful technique for investigating cerebrovascular function, offering high spatial resolution and wide fields of view (FOV), yet it is relatively underutilized as a tool for assessment of CA. The aim of this study was to demonstrate the potential of using MRI to measure changes in cerebrovascular resistance in response to lower body negative pressure (LBNP). A Pulsed Arterial Spin Labeling (PASL) approach with short inversion times (TI) was used to estimate cerebral arterial blood volume (CBV a) in eight healthy subjects at baseline and −40 mmHg LBNP. We estimated group mean CBV a values of 3.13 ± 1.00 and 2.70 ± 0.38 for baseline and lbnp respectively, which were the result of a differential change in CBV a during −40 mmHg LBNP that was dependent on baseline CBV a. These data suggest that the PASL CBV a estimates are sensitive to the complex cerebrovascular response that occurs during the moderate orthostatic challenge delivered by LBNP, which we speculatively propose may involve differential changes in vascular tone within different segments of the arterial vasculature. These novel data provide invaluable insight into the mechanisms that regulate perfusion of the brain, and establishes the use of MRI as a tool for studying CA in more detail. [ABSTRACT FROM AUTHOR]

Abstract: Negative BOLD signals that are synchronous with resting state fluctuations have been observed in large vessels in the cortical sulci and surrounding the ventricles. In this study, we investigated the origin of these negative BOLD signals by applying a Cued Deep Breathing (CDB) task to create transient hypocapnia and a resultant global fMRI signal decrease. We hypothesized that a global stimulus would amplify the effect in large vessels and that using a global negative (vasoconstrictive) stimulus would test whether these voxels exhibit either inherently negative or simply anti-correlated BOLD responses. Significantly anti-correlated, but positive, BOLD signal changes during respiratory challenges were identified in voxels primarily located near edges of brain spaces containing CSF. These positive BOLD responses occurred earlier than the negative CDB response across most of gray matter voxels. These findings confirm earlier suggestions that in some brain regions, local, fractional changes in CSF volume may overwhelm BOLD-related signal changes, leading to signal anti-correlation. We show that regions with CDB anti-correlated signals coincide with most, but not all, of the regions with negative BOLD signal changes observed during a visual and motor stimulus task. Thus, the addition of a physiological challenge to fMRI experiments can help identify which negative BOLD signals are passive physiological anti-correlations and which may have a putative neuronal origin. [Copyright &y& Elsevier]

Abstract: Cerebrovascular reactivity (CVR) can be mapped using BOLD fMRI to provide a clinical insight into vascular health that can be used to diagnose cerebrovascular disease. Breath-holds are a readily accessible method for producing the required arterial CO2 increases but their implementation into clinical studies is limited by concerns that patients will demonstrate highly variable performance of breath-hold challenges. This study assesses the repeatability of CVR measurements despite poor task performance, to determine if and how robust results could be achieved with breath-holds in patients. Twelve healthy volunteers were scanned at 3T. Six functional scans were acquired, each consisting of 6 breath-hold challenges (10, 15, or 20s duration) interleaved with periods of paced breathing. These scans simulated the varying breath-hold consistency and ability levels that may occur in patient data. Uniform ramps, time-scaled ramps, and end-tidal CO2 data were used as regressors in a general linear model in order to measure CVR at the grey matter, regional, and voxelwise level. The intraclass correlation coefficient (ICC) quantified the repeatability of the CVR measurement for each breath-hold regressor type and scale of interest across the variable task performances. The ramp regressors did not fully account for variability in breath-hold performance and did not achieve acceptable repeatability (ICC<0.4) in several regions analysed. In contrast, the end-tidal CO2 regressors resulted in “excellent” repeatability (ICC=0.82) in the average grey matter data, and resulted in acceptable repeatability in all smaller regions tested (ICC>0.4). Further analysis of intra-subject CVR variability across the brain (ICCspatial and voxelwise correlation) supported the use of end-tidal CO2 data to extract robust whole-brain CVR maps, despite variability in breath-hold performance. We conclude that the incorporation of end-tidal CO2 monitoring into scanning enables robust, repeatable measurement of CVR that makes breath-hold challenges suitable for routine clinical practice. [Copyright &y& Elsevier]

Abstract: Differing noise variance across study populations has been shown to cause artifactual group differences in functional connectivity measures. In this study, we investigate the use of short echo time functional MRI data to correct for these noise sources in blood oxygenation level dependent (BOLD)-weighted time series. A dual‐echo sequence was used to simultaneously acquire data at both a short (TE=3.3ms) and a BOLD-weighted (TE=35ms) echo time. This approach is effectively “free,” using dead-time in the pulse sequence to collect an additional echo without affecting overall scan time or temporal resolution. The proposed correction method uses voxelwise regression of the short TE data from the BOLD-weighted data to remove noise variance. In addition to a typical resting state scan, non-compliant behavior associated with patient groups was simulated via increased head motion or physiological fluctuations in 10 subjects. Short TE data showed significant correlations with the traditional motion-related and physiological noise regressors used in current connectivity analyses. Following traditional preprocessing, the extent of significant additional variance explained by the short TE data regressors was significantly correlated with the average head motion across the scan in the resting data (r 2 =0.93, p <0.0001). The reduction in data variance following the inclusion of short TE regressors was also correlated with scan head motion (r 2 =0.48, p =0.027). Task-related data were used to demonstrate the effects of the short TE correction on BOLD activation time series with known temporal structure; the size and strength of the activation were significantly decreased, but it is not clear whether this reflects BOLD contamination in the short TE data or correlated changes in blood volume. Finally, functional connectivity maps of the default mode network were constructed using a seed correlation approach. The effects of short TE correction and low-pass filtering on the resulting correlations maps were compared. Results suggest that short TE correction more accurately differentiates artifactual correlations from the correlations of interest in conditions of amplified noise. [Copyright &y& Elsevier]

Cerebrovascular reactivity to vasodilatory hypercapnic and vasoconstrictive hypocapnic challenges is known to be altered in several hemodynamic disorders, which is often attributable to changes in smooth muscle-mediated vascular compliance. Recently, attenuated reactivity to hypercapnia but enhanced reactivity to hypocapnia was observed in patients with chronic stroke. We hypothesize that the latter observation could be explained by a change in the basal vascular tone. In particular, reduced cerebral perfusion pressure, as is prevalent in these patients, may cause vasodilation through autoregulatory mechanisms, and this compensatory baseline condition may alter reactivity to vasoconstrictive hypocapnic challenges. To test this hypothesis, a predilated vascular condition was created in young, healthy subjects (n=11; age=23 to 36 years) using inhalation of 4% CO2. Using blood oxygenation level-dependent functional magnetic resonance imaging at 3 T, breath holding and cued deep breathing respiratory challenges were administered to assess hypercapnia and hypocapnia reactivity, respectively. During the predilated condition, vasoconstrictive reactivity to hypocapnia was significantly (21.1%, P=0.016) enhanced throughout the gray matter, whereas there was no significant change (6.4%, P=0.459) in hypercapnic vasodilatory reactivity. This suggests that baseline vasodilation may explain the enhanced hypocapnia reactivity observed in some stroke patients, and that hypocapnia challenges may help identify the level of vascular compliance in patients with reduced cerebral perfusion pressure. [ABSTRACT FROM AUTHOR]

Abstract: We offer a new method for characterizing the magnitude and dynamics of the vascular response to changes in arterial gas tensions using non-invasive blood oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) and paradigms appropriate for clinical settings. A novel respiratory task, “Cued Deep Breathing” (CDB), consisting of two consecutive cycles of cued breaths, has been developed to cause transient hypocapnia, and consequently a strong, short-lived BOLD signal decrease. Data from CDB hypocapnia paradigms and traditional breath-holding hypercapnia paradigms were analyzed on a voxel-wise basis to map regional heterogeneity in magnitude and timing parameters. The tasks caused comparable absolute BOLD percent signal changes (∼0.5–3.0% in gray matter) and both datasets suggested consistent regional heterogeneity in the response timing: parts of the basal ganglia, particularly the putamen, and bilateral areas of medial cortex reached their maximum signal change several seconds earlier than remaining cortical gray matter voxels. This phenomenon and a slightly delayed response in posterior cortical regions were present in group-maps of ten healthy subjects. An auxiliary experiment in different subjects measured end-tidal CO2 changes associated with the new CDB task and quantitatively compared the resulting reactivity maps with those acquired using a traditional hypercapnia challenge of 4% CO2 gas inspiration. The CDB task caused average end-tidal CO2 decreases between 6.0±1.1 and 10.5±2.6 mm Hg, with levels returning to baseline after approximately three breaths, giving evidence that the task indeed causes transient mild hypocapnia. Similarity between resulting reactivity maps suggest CDB offers an alternative method for mapping cerebrovascular reactivity. [Copyright &y& Elsevier]

We present the first evidence for vascular regulation driving fMRI signals in specific functional brain networks. Using concurrent neuronal and vascular stimuli, we collected 30 BOLD fMRI datasets in 10 healthy individuals: a working memory task, flashing checkerboard stimulus, and CO 2 inhalation challenge were delivered in concurrent but orthogonal paradigms. The resulting imaging data were averaged together and decomposed using independent component analysis, and three "neuronal networks" were identified as demonstrating maximum temporal correlation with the neuronal stimulus paradigms: Default Mode Network, Task Positive Network, and Visual Network. For each of these, we observed a second network component with high spatial overlap. Using dual regression in the original 30 datasets, we extracted the time-series associated with these network pairs and calculated the percent of variance explained by the neuronal or vascular stimuli using a normalized R2 parameter. In each pairing, one network was dominated by the appropriate neuronal stimulus, and the other was dominated by the vascular stimulus as represented by the end-tidal CO 2 time-series recorded in each scan. We acquired a second dataset in 8 of the original participants, where no CO 2 challenge was delivered and CO 2 levels fluctuated naturally with breathing variations. Although splitting of functional networks was not robust in these data, performing dual regression with the network maps from the original analysis in this new dataset successfully replicated our observations. Thus, in addition to responding to localized metabolic changes, the brain's vasculature may be regulated in a coordinated manner that mimics (and potentially supports) specific functional brain networks. Multi-modal imaging and advances in fMRI acquisition and analysis could facilitate further study of the dual nature of functional brain networks. It will be critical to understand network-specific vascular function, and the behavior of a coupled vascular-neural network, in future studies of brain pathology. • Using neural and vascular stimuli we find pairs of spatially similar brain networks. • One network timeseries reflects a neural stimulus, the other a vascular stimulus. • The brain's vasculature may be regulated to support specific functional networks. • Understanding network-specific vascular function is critical to interpreting fMRI. [ABSTRACT FROM AUTHOR]

Upper extremity motor paradigms during spinal cord functional magnetic resonance imaging (fMRI) can provide insight into the functional organization of the cord. Hand‐grasping is an important daily function with clinical significance, but previous studies of similar squeezing movements have not reported consistent areas of activity and are limited by sample size and simplistic analysis methods. Here, we study spinal cord fMRI activation using a unimanual isometric hand‐grasping task that is calibrated to participant maximum voluntary contraction (MVC). Two task modeling methods were considered: (1) a task regressor derived from an idealized block design (Ideal) and (2) a task regressor based on the recorded force trace normalized to individual MVC (%MVC). Across these two methods, group motor activity was highly lateralized to the hemicord ipsilateral to the side of the task. Activation spanned C5–C8 and was primarily localized to the C7 spinal cord segment. Specific differences in spatial distribution are also observed, such as an increase in C8 and dorsal cord activity when using the %MVC regressor. Furthermore, we explored the impact of data quantity and spatial smoothing on sensitivity to hand‐grasp motor task activation. This analysis shows a large increase in number of active voxels associated with the number of fMRI runs, sample size, and spatial smoothing, demonstrating the impact of experimental design choices on motor activation. [ABSTRACT FROM AUTHOR]

• We estimated relative hemodynamic delay in gray matter tissue in a healthy cohort. • We assessed the agreement of delay in fMRI data with and without a breathing task. • Agreement is highly dependent on which brain voxels are included. • Stronger agreement was seen in voxels that more closely resembled the GM average. • Voxelwise delay estimates from different fMRI data may not be interchangeable. The blood flow response to a vasoactive stimulus demonstrates regional heterogeneity across both the healthy brain and in cerebrovascular pathology. The timing of a regional hemodynamic response is emerging as an important biomarker of cerebrovascular dysfunction, as well as a confound within fMRI analyses. Previous research demonstrated that hemodynamic timing is more robustly characterized when a larger systemic vascular response is evoked by a breathing challenge, compared to when only spontaneous fluctuations in vascular physiology are present (i.e., in resting-state data). However, it is not clear whether hemodynamic delays in these two conditions are physiologically interchangeable, and how methodological signal-to-noise factors may limit their agreement. To address this, we generated whole-brain maps of hemodynamic delays in nine healthy adults. We assessed the agreement of voxel-wise gray matter (GM) hemodynamic delays between two conditions: resting-state and breath-holding. We found that delay values demonstrated poor agreement when considering all GM voxels, but increasingly greater agreement when limiting analyses to voxels showing strong correlation with the GM mean time-series. Voxels showing the strongest agreement with the GM mean time-series were primarily located near large venous vessels, however these voxels explain some, but not all, of the observed agreement in timing. Increasing the degree of spatial smoothing of the fMRI data enhanced the correlation between individual voxel time-series and the GM mean time-series. These results suggest that signal-to-noise factors may be limiting the accuracy of voxel-wise timing estimates and hence their agreement between the two data segments. In conclusion, caution must be taken when using voxel-wise delay estimates from resting-state and breathing-task data interchangeably, and additional work is needed to evaluate their relative sensitivity and specificity to aspects of vascular physiology and pathology. [ABSTRACT FROM AUTHOR]

The article focuses on a demonstration of fine-scale dependence of oscillatory power of neurons on arterial carbon dioxide concentrations and the impact of alpha, beta, and gamma power with mild levels of hypercapnia. It states a graded hypercapnia paradigm and feedback by participants was used to rule out sensory causes and demonstrated natural changes in arterial carbon dioxide was sufficient to influence neuronal oscillatory power. It suggests resting-state brain research should be revisited.

Interaction of an active electronic implant such as a deep brain stimulation (DBS) system and magnetic resonance imaging (MRI) radiofrequency (RF) fields can induce excessive tissue heating, limiting MRI accessibility. Efforts to quantify RF heating mostly rely on electromagnetic (EM) simulations to assess individualized specific absorption rate (SAR), but such simulations require extensive computational resources. Here, we investigate if a predictive model using machine learning (ML) can predict the local SAR in the tissue around tips of implanted leads from the distribution of the tangential component of the MRI incident electric field, Etan. A dataset of 260 unique patient-derived and artificial DBS lead trajectories was constructed, and the 1 g-averaged SAR, 1 g SARmax, at the lead-tip during 1.5 T MRI was determined by EM simulations. Etan values along each lead's trajectory and the simulated SAR values were used to train and test the ML algorithm. The resulting predictions of the ML algorithm indicated that the distribution of Etan could effectively predict 1 g SARmax at the DBS lead-tip (R = 0.82). Our results indicate that ML has the potential to provide a fast method for predicting MR-induced power absorption in the tissue around tips of implanted leads such as those in active electronic medical devices. [ABSTRACT FROM AUTHOR]

Objectives: To assess the feasibility of using an optimised ultra-high-field high-spatial-resolution low-distortion arterial spin labelling (ASL) MRI acquisition to measure focal haemodynamic pathology in cortical lesions (CLs) in multiple sclerosis (MS).Methods: Twelve MS patients (eight female, mean age 50 years; range 35-64 years) gave informed consent and were scanned on a 7 Tesla Philips Achieva scanner. Perfusion data were collected at multiple post-labelling delay times using a single-slice flow-sensitive alternating inversion recovery ASL protocol with a balanced steady-state free precession readout scheme. CLs were identified using a high-resolution Phase-Sensitive Inversion Recovery (PSIR) scan. Significant differences in perfusion within CLs compared to immediately surrounding normal appearing grey matter (NAGMlocal) and total cortical normal appearing grey matter (NAGMcortical) were assessed using paired t-tests.Results: Forty CLs were identified in PSIR scans that overlapped with the ASL acquisition coverage. After excluding lesions due to small size or intravascular contamination, 27 lesions were eligible for analysis. Mean perfusion was 40 ± 25 ml/100 g/min in CLs, 53 ± 12 ml/100 g/min in NAGMlocal, and 53 ± 8 ml/100 g/min in NAGMcortical. CL perfusion was significantly reduced by 23 ± 9% (mean ± SE, p = 0.013) and 26 ± 9% (p = 0.006) relative to NAGMlocal and NAGMcortical perfusion, respectively.Conclusion: This is the first ASL MRI study quantifying CL perfusion in MS at 7 Tesla, demonstrating that an optimised ASL acquisition is sensitive to focal haemodynamic pathology previously observed using dynamic susceptibility contrast MRI. ASL requires no exogenous contrast agent, making it a more appropriate tool to monitor longitudinal perfusion changes in MS, providing a new window to study lesion development.Key Points: • Perfusion can be quantified within cortical lesions in multiple sclerosis using an optimised high spatial resolution arterial spin Labelling MRI acquisition at ultra-high-field. • The majority of cortical lesions assessed using arterial spin labelling are hypo-perfused compared to normal appearing grey matter, in agreement with dynamic susceptibility contrast MRI literature. • Arterial spin labelling MRI, which does not involve the injection of a contrast agent, is a safe and appropriate technique for repeat scanning of an individual patient. [ABSTRACT FROM AUTHOR]

• We compared breath-hold CVR maps made using CO 2 , RVT, and the global BOLD signal. • All show good agreement in CVR amplitude and delay provided CO 2 data are "sufficient". • We define "sufficient" CO 2 data quality as relative power >50% in the task frequency. • RVT or the global signal can map relative CVR parameters if CO 2 data are insufficient. • Offers solution to map breath-hold CVR when CO 2 data are unavailable or unreliable. Cerebrovascular reactivity (CVR), defined as the cerebral blood flow response to a vasoactive stimulus, is an imaging biomarker with demonstrated utility in a range of diseases and in typical development and aging processes. A robust and widely implemented method to map CVR involves using a breath-hold task during a BOLD fMRI scan. Recording end-tidal CO 2 (P ET CO 2) changes during the breath-hold task is recommended to be used as a reference signal for modeling CVR amplitude in standard units (%BOLD/mmHg) and CVR delay in seconds. However, obtaining reliable P ET CO 2 recordings requires equipment and task compliance that may not be achievable in all settings. To address this challenge, we investigated two alternative reference signals to map CVR amplitude and delay in a lagged general linear model (lagged-GLM) framework: respiration volume per time (RVT) and average gray matter BOLD response (GM-BOLD). In 8 healthy adults with multiple scan sessions, we compare spatial agreement of CVR maps from RVT and GM-BOLD to those generated with P ET CO 2. We define a threshold to determine whether a P ET CO 2 recording has "sufficient" quality for CVR mapping and perform these comparisons in 16 datasets with sufficient P ET CO 2 and 6 datasets with insufficient P ET CO 2. When P ET CO 2 quality is sufficient, both RVT and GM-BOLD produce CVR amplitude maps that are nearly identical to those from P ET CO 2 (after accounting for differences in scale), with the caveat they are not in standard units to facilitate between-group comparisons. CVR delays are comparable to P ET CO 2 with an RVT regressor but may be underestimated with the average GM-BOLD regressor. Importantly, when P ET CO 2 quality is insufficient, RVT and GM-BOLD CVR recover reasonable CVR amplitude and delay maps, provided the participant attempted the breath-hold task. Therefore, our framework offers a solution for achieving high quality CVR maps in both retrospective and prospective studies where sufficient P ET CO 2 recordings are not available and especially in populations where obtaining reliable measurements is a known challenge (e.g., children). Our results have the potential to improve the accessibility of CVR mapping and to increase the prevalence of this promising metric of vascular health. [ABSTRACT FROM AUTHOR]

• We propose adding a short breathing task to the start of a resting state fMRI scan • This protocol facilitates mapping of cerebrovascular reactivity and hemodynamic lag • This practical modification adds vascular insight to typical resting state fMRI • Protocol is sensitive to pathology, and modeling lag is crucial for interpretation Cerebrovascular reactivity (CVR), defined here as the Blood Oxygenation Level Dependent (BOLD) response to a CO 2 pressure change, is a useful metric of cerebrovascular function. Both the amplitude and the timing (hemodynamic lag) of the CVR response can bring insight into the nature of a cerebrovascular pathology and aid in understanding noise confounds when using functional Magnetic Resonance Imaging (fMRI) to study neural activity. This research assessed a practical modification to a typical resting-state fMRI protocol, to improve the characterization of cerebrovascular function. In 9 healthy subjects, we modelled CVR and lag in three resting-state data segments, and in data segments which added a 2–3 minute breathing task to the start of a resting-state segment. Two different breathing tasks were used to induce fluctuations in arterial CO 2 pressure: a breath-hold task to induce hypercapnia (CO 2 increase) and a cued deep breathing task to induce hypocapnia (CO 2 decrease). Our analysis produced voxel-wise estimates of the amplitude (CVR) and timing (lag) of the BOLD-fMRI response to CO 2 by systematically shifting the CO 2 regressor in time to optimize the model fit. This optimization inherently increases gray matter CVR values and fit statistics. The inclusion of a simple breathing task, compared to a resting-state scan only, increases the number of voxels in the brain that have a significant relationship between CO 2 and BOLD-fMRI signals, and improves our confidence in the plausibility of voxel-wise CVR and hemodynamic lag estimates. We demonstrate the clinical utility and feasibility of this protocol in an incidental finding of Moyamoya disease, and explore the possibilities and challenges of using this protocol in younger populations. This hybrid protocol has direct applications for CVR mapping in both research and clinical settings and wider applications for fMRI denoising and interpretation. [ABSTRACT FROM AUTHOR]

Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response. [ABSTRACT FROM AUTHOR]