Vascular autorescaling of fMRI (VasA fMRI) improves sensitivity of population studies : A pilot study

The blood oxygenation level-dependent (BOLD) signal is widely used for functional magnetic resonance imaging (fMRI) of brain function in health and disease. The statistical power of fMRI group studies is significantly hampered by high inter-subject variance due to differences in baseline vascular physiology. Several methods have been proposed to account for physiological vascularization differences between subjects and hence improve the sensitivity in group studies. However, these methods require the acquisition of additional reference scans (such as a full resting-state fMRI session or ASL-based calibrated BOLD). We present a vascular autorescaling (VasA) method, which does not require any additional reference scans. VasA is based on the observation that slow oscillations (<0.1Hz) in arterial blood CO2 levels occur naturally due to changes in respiration patterns. These oscillations yield fMRI signal changes whose amplitudes reflect the blood oxygenation levels and underlying local vascularization and vascular responsivity. VasA estimates proxies of the amplitude of these CO2-driven oscillations directly from the residuals of task-related fMRI data without the need for reference scans. The estimates are used to scale the amplitude of task-related fMRI responses, to account for vascular differences. The VasA maps compared well to cerebrovascular reactivity (CVR) maps and cerebral blood volume maps based on vascular space occupancy (VASO) measurements in four volunteers, speaking to the physiological vascular basis of VasA. VasA was validated in a wide variety of tasks in 138 volunteers. VasA increased t-scores by up to 30% in specific brain areas such as the visual cortex. The number of activated voxels was increased by up to 200% in brain areas such as the orbital frontal cortex while still controlling the nominal false-positive rate. VasA fMRI outperformed previously proposed rescaling approaches based on resting-state fMRI data and can be readily applied to any task-related fMRI data set, even retrospectively

A system for accurate and automated injection of hyperpolarized substrate with minimal dead time and scalable volumes over a large range

Over recent years hyperpolarization by dissolution dynamic nuclear polarization has become an established technique for studying metabolism in vivo in animal models. Temporal signal plots obtained from the injected metabolite and daughter products, e.g. pyruvate and lactate, can be fitted to compartmental models to estimate kinetic rate constants. Modeling and physiological parameter estimation can be made more robust by consistent and reproducible injections through automation. An injection system previously developed by us was limited in the injectable volume to between 0.6 and 2.4 ml and injection was delayed due to a required syringe filling step. An improved MR-compatible injector system has been developed that measures the pH of injected substrate, uses flow control to reduce dead volume within the injection cannula and can be operated over a larger volume range. The delay time to injection has been minimized by removing the syringe filling step by use of a peristaltic pump. For 100 ll to 10.000 ml, the volume range typically used for mice to rabbits, the average delivered volume was 97.8% of the demand volume. The standard deviation of delivered volumes was 7 ll for 100 ll and 20 ll for 10.000 ml demand volumes (mean S.D. was 9 ul in this range). In three repeat injections through a fixed 0.96 mm O.D. tube the coefficient of variation for the area under the curve was 2%. For in vivo injections of hyperpolarized pyruvate in tumor-bearing rats, signal was first detected in the input femoral vein cannula at 3–4 s post-injection trigger signal and at 9–12 s in tumor tissue. The pH of the injected pyruvate was 7.1 ± 0.3 (mean ± S.D., n = 10). For small injection volumes, e.g. less than 100 ll, the internal diameter of the tubing contained within the peristaltic pump could be reduced to improve accuracy. Larger injection volumes are limited only by the size of the receiving vessel connected to the pump

Physiological basis of vascular autocalibration (VasA): Comparison to hypercapnia calibration methods

PURPOSE: The statistical power of functional MRI (fMRI) group studies is significantly hampered by high intersubject spatial and magnitude variance. We recently presented a vascular autocalibration method (VasA) to account for vascularization differences between subjects and hence improve the sensitivity in group studies. Here, we validate the novel calibration method by means of direct comparisons of VasA with more established measures of baseline venous blood volume (and indirectly vascular reactivity), the M-value. METHODS: Seven healthy volunteers participated in two 7 T (T) fMRI experiments to compare M-values with VasA estimates: (i) a hypercapnia experiment to estimate voxelwise M-value maps, and (ii) an fMRI experiment using visual stimulation to estimate voxelwise VasA maps. RESULTS: We show that VasA and M-value calibration maps show the same spatial profile, providing strong evidence that VasA is driven by local variations in vascular reactivity as reflected in the M-value. CONCLUSION: The agreement of vascular reactivity maps obtained with VasA when compared with M-value maps confirms empirically the hypothesis that the VasA method is an adequate tool to account for variations in fMRI response amplitudes caused by vascular reactivity differences in healthy volunteers. VasA can therefore directly account for them and increase the statistical power of group studies. The VasA toolbox is available as a statistical parametric mapping (SPM) toolbox, facilitating its general application. Magn Reson Med, 2016. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine

Optimizing data for modeling neuronal responses

In this technical note, we address an unresolved challenge in neuroimaging statistics: how to determine which of several datasets is the best for inferring neuronal responses. Comparisons of this kind are important for experimenters when choosing an imaging protocol—and for developers of new acquisition methods. However, the hypothesis that one dataset is better than another cannot be tested using conventional statistics (based on likelihood ratios), as these require the data to be the same under each hypothesis. Here we present Bayesian data comparison (BDC), a principled framework for evaluating the quality of functional imaging data, in terms of the precision with which neuronal connectivity parameters can be estimated and competing models can be disambiguated. For each of several candidate datasets, neuronal responses are modeled using Bayesian (probabilistic) forward models, such as General Linear Models (GLMs) or Dynamic Casual Models (DCMs). Next, the parameters from subject-specific models are summarized at the group level using a Bayesian GLM. A series of measures, which we introduce here, are then used to evaluate each dataset in terms of the precision of (group-level) parameter estimates and the ability of the data to distinguish similar models. To exemplify the approach, we compared four datasets that were acquired in a study evaluating multiband fMRI acquisition schemes, and we used simulations to establish the face validity of the comparison measures. To enable people to reproduce these analyses using their own data and experimental paradigms, we provide general-purpose Matlab code via the SPM software