In vivo monitoring of renal tubule volume fraction using dynamic parametric MRI.

PURPOSE: The increasing incidence of kidney diseases is a global concern, and current biomarkers and treatments are inadequate. Changes in renal tubule luminal volume fraction (TVF) serve as a rapid biomarker for kidney disease and improve understanding of renal (patho)physiology. This study uses the amplitude of the long T2 component as a surrogate for TVF in rats, by applying multiexponential analysis of the T2-driven signal decay to examine micromorphological changes in renal tissue. METHODS: Simulations were conducted to identify a low mean absolute error (MAE) protocol and an accelerated protocol customized for the in vivo study of T2 mapping of the rat kidney at 9.4 T. We then validated our bi-exponential approach in a phantom mimicking the relaxation properties of renal tissue. This was followed by a proof-of-principle demonstration using in vivo data obtained during a transient increase of renal pelvis and tubular pressure. RESULTS: Using the low MAE protocol, our approach achieved an accuracy of MAE < 1% on the mechanical phantom. The T2 mapping protocol customized for in vivo study achieved an accuracy of MAE < 3%. Transiently increasing pressure in the renal pelvis and tubules led to significant changes in TVF in renal compartments: ΔTVFcortex = 4.9%, ΔTVFouter_medulla = 4.5%, and ΔTVFinner_medulla = -14.6%. CONCLUSION: These results demonstrate that our approach is promising for research into quantitative assessment of renal TVF in in vivo applications. Ultimately, these investigations have the potential to help reveal mechanism in acute renal injury that may lead to chronic kidney disease, which will support research into renal disorders.

B1 inhomogeneity correction of RARE MRI at low SNR: Quantitative in vivo 19 F MRI of mouse neuroinflammation with a cryogenically-cooled transceive surface radiofrequency probe.

PURPOSE: Low SNR in fluorine-19 (19 F) MRI benefits from cryogenically-cooled transceive surface RF probes (CRPs), but strong B1 inhomogeneities hinder quantification. Rapid acquisition with refocused echoes (RARE) is an SNR-efficient method for MRI of neuroinflammation with perfluorinated compounds but lacks an analytical signal intensity equation to retrospectively correct B1 inhomogeneity. Here, a workflow was proposed and validated to correct and quantify 19 F-MR signals from the inflamed mouse brain using a 19 F-CRP. METHODS: In vivo 19 F-MR images were acquired in a neuroinflammation mouse model with a quadrature 19 F-CRP using an imaging setup including 3D-printed components to acquire co-localized anatomical and 19 F images. Model-based corrections were validated on a uniform 19 F phantom and in the neuroinflammatory model. Corrected 19 F-MR images were benchmarked against reference images and overlaid on in vivo 1 H-MR images. Computed concentration uncertainty maps using Monte Carlo simulations served as a measure of performance of the B1 corrections. RESULTS: Our study reports on the first quantitative in vivo 19 F-MR images of an inflamed mouse brain using a 19 F-CRP, including in vivo T1 calculations for 19 F-nanoparticles during pathology and B1 corrections for 19 F-signal quantification. Model-based corrections markedly improved 19 F-signal quantification from errors > 50% to < 10% in a uniform phantom (p < 0.001). Concentration uncertainty maps ex vivo and in vivo yielded uncertainties that were generally < 25%. Monte Carlo simulations prescribed SNR ≥ 10.1 to reduce uncertainties < 10%, and SNR ≥ 4.25 to achieve uncertainties < 25%. CONCLUSION: Our model-based correction method facilitated 19 F signal quantification in the inflamed mouse brain when using the SNR-boosting 19 F-CRP technology, paving the way for future low-SNR 19 F-MRI applications in vivo.

Performance of compressed sensing for fluorine-19 magnetic resonance imaging at low signal-to-noise ratio conditions.

PURPOSE: To examine the performance of compressed sensing (CS) in reconstructing low signal-to-noise ratio (SNR) 19 F MR signals that are close to the detection threshold and originate from small signal sources with no a priori known location. METHODS: Regularization strength was adjusted automatically based on noise level. As performance metrics, root-mean-square deviations, true positive rates (TPRs), and false discovery rates were computed. CS and conventional reconstructions were compared at equal measurement time and evaluated in relation to high-SNR reference data. 19 F MR data were generated from a purpose-built phantom and benchmarked against simulations, as well as from the experimental autoimmune encephalomyelitis mouse model. We quantified the signal intensity bias and introduced an intensity calibration for in vivo data using high-SNR ex vivo data. RESULTS: Low-SNR 19 F MR data could be reliably reconstructed. Detection sensitivity was consistently improved and data fidelity was preserved for undersampling and averaging factors of α = 2 or = 3. Higher α led to signal blurring in the mouse model. The improved TPRs at α = 3 were comparable to a 2.5-fold increase in measurement time. Whereas CS resulted in a downward bias of the 19 F MR signal, Fourier reconstructions resulted in an unexpected upward bias of similar magnitude. The calibration corrected signal-intensity deviations for all reconstructions. CONCLUSION: CS is advantageous whenever image features are close to the detection threshold. It is a powerful tool, even for low-SNR data with sparsely distributed 19 F signals, to improve spatial and temporal resolution in 19 F MR applications.

Cardiorenal sodium MRI in small rodents using a quadrature birdcage volume resonator at 9.4 T.

OBJECTIVE: Design, implementation, evaluation and application of a quadrature birdcage radiofrequency (RF) resonator tailored for renal and cardiac sodium (23Na) magnetic resonance imaging (MRI) in rats at 9.4 T. MATERIALS AND METHODS: A low pass birdcage resonator (16 rungs, din = 62 mm) was developed. The transmission field (B1+) was examined with EMF simulations. The scattering parameter (S-parameter) and the quality factor (Q-factor) were measured. For experimental validation B1+-field maps were acquired with the double-angle method. In vivo sodium imaging of the heart (spatial resolution: (1 × 1 × 5) mm3) and kidney (spatial resolution: (1 × 1 × 10) mm3) was performed with a FLASH technique. RESULTS: The RF resonator exhibits RF characteristics, transmission field homogeneity and penetration that afford 23Na MR in vivo imaging of the kidney and heart at 9.4 T. For the renal cortex and medulla a SNRs of 8 and 13 were obtained and a SNRs of 14 and 15 were observed for the left and right ventricle. DISCUSSION: These initial results obtained in vivo in rats using the quadrature birdcage volume RF resonator for 23Na MRI permit dedicated studies on experimental models of cardiac and renal diseases, which would contribute to translational research of the cardiorenal syndrome.

Toward 19F magnetic resonance thermometry: spin-lattice and spin-spin-relaxation times and temperature dependence of fluorinated drugs at 9.4 T.

OBJECTIVE: This study examines the influence of the environmental factor temperature on the 19F NMR characteristics of fluorinated compounds in phantom studies and in tissue. MATERIALS AND METHODS: 19F MR mapping and MR spectroscopy techniques were used to characterize the 19F NMR characteristics of perfluoro-crown ether (PFCE), isoflurane, teriflunomide, and flupentixol. T1 and T2 mapping were performed, while temperature in the samples was changed (T = 20-60 °C) and monitored using fiber optic measurements. In tissue, T1 of PFCE nanoparticles was determined at physiological temperatures and compared with the T1-measured at room temperature. RESULTS: Studies on PFCE, isoflurane, teriflunomide, and flupentixol showed a relationship between temperature and their physicochemical characteristics, namely, chemical shift, T1 and T2. T1 of PFCE nanoparticles was higher at physiological body temperatures compared to room temperature. DISCUSSION: The impact of temperature on the 19F NMR parameters of fluorinated compounds demonstrated in this study not only opens a trajectory toward 19F MR-based thermometry, but also indicates the need for adapting MR sequence parameters according to environmental changes such as temperature. This will be an absolute requirement for detecting fluorinated compounds by 19F MR techniques in vivo.

Fluorine-19 MRI at 21.1 T: enhanced spin-lattice relaxation of perfluoro-15-crown-5-ether and sensitivity as demonstrated in ex vivo murine neuroinflammation.

OBJECTIVE: Fluorine MR would benefit greatly from enhancements in signal-to-noise ratio (SNR). This study examines the sensitivity gain of 19F MR that can be practically achieved when moving from 9.4 to 21.1 T. MATERIALS AND METHODS: We studied perfluoro-15-crown-5-ether (PFCE) at both field strengths (B0), as a pure compound, in the form of nanoparticles (NP) as employed to study inflammation in vivo, as well as in inflamed tissue. Brains, lymph nodes (LNs) and spleens were obtained from mice with experimental autoimmune encephalomyelitis (EAE) that had been administered PFCE NPs. All samples were measured at both B0 with 2D-RARE and 2D-FLASH using 19F volume radiofrequency resonators together. T1 and T2 of PFCE were measured at both B0 strengths. RESULTS: Compared to 9.4 T, an SNR gain of > 3 was observed for pure PFCE and > 2 for PFCE NPs at 21.1 T using 2D-FLASH. A dependency of 19F T1 and T2 relaxation on B0 was demonstrated. High spatially resolved 19F MRI of EAE brains and LNs at 21.1 T revealed signals not seen at 9.4 T. DISCUSSION: Enhanced SNR and T1 shortening indicate the potential benefit of in vivo 19F MR at higher B0 to study inflammatory processes with greater detail.

Probabilistic delay differential equation modeling of event-related potentials.

"Dynamic causal models" (DCMs) are a promising approach in the analysis of functional neuroimaging data due to their biophysical interpretability and their consolidation of functional-segregative and functional-integrative propositions. In this theoretical note we are concerned with the DCM framework for electroencephalographically recorded event-related potentials (ERP-DCM). Intuitively, ERP-DCM combines deterministic dynamical neural mass models with dipole-based EEG forward models to describe the event-related scalp potential time-series over the entire electrode space. Since its inception, ERP-DCM has been successfully employed to capture the neural underpinnings of a wide range of neurocognitive phenomena. However, in spite of its empirical popularity, the technical literature on ERP-DCM remains somewhat patchy. A number of previous communications have detailed certain aspects of the approach, but no unified and coherent documentation exists. With this technical note, we aim to close this gap and to increase the technical accessibility of ERP-DCM. Specifically, this note makes the following novel contributions: firstly, we provide a unified and coherent review of the mathematical machinery of the latent and forward models constituting ERP-DCM by formulating the approach as a probabilistic latent delay differential equation model. Secondly, we emphasize the probabilistic nature of the model and its variational Bayesian inversion scheme by explicitly deriving the variational free energy function in terms of both the likelihood expectation and variance parameters. Thirdly, we detail and validate the estimation of the model with a special focus on the explicit form of the variational free energy function and introduce a conventional nonlinear optimization scheme for its maximization. Finally, we identify and discuss a number of computational issues which may be addressed in the future development of the approach.

Simulating rigid head motion artifacts on brain magnitude MRI data-Outcome on image quality and segmentation of the cerebral cortex.

Magnetic Resonance Imaging (MRI) datasets from epidemiological studies often show a lower prevalence of motion artifacts than what is encountered in clinical practice. These artifacts can be unevenly distributed between subject groups and studies which introduces a bias that needs addressing when augmenting data for machine learning purposes. Since unreconstructed multi-channel k-space data is typically not available for population-based MRI datasets, motion simulations must be performed using signal magnitude data. There is thus a need to systematically evaluate how realistic such magnitude-based simulations are. We performed magnitude-based motion simulations on a dataset (MR-ART) from 148 subjects in which real motion-corrupted reference data was also available. The similarity of real and simulated motion was assessed by using image quality metrics (IQMs) including Coefficient of Joint Variation (CJV), Signal-to-Noise-Ratio (SNR), and Contrast-to-Noise-Ratio (CNR). An additional comparison was made by investigating the decrease in the Dice-Sørensen Coefficient (DSC) of automated segmentations with increasing motion severity. Segmentation of the cerebral cortex was performed with 6 freely available tools: FreeSurfer, BrainSuite, ANTs, SAMSEG, FastSurfer, and SynthSeg+. To better mimic the real subject motion, the original motion simulation within an existing data augmentation framework (TorchIO), was modified. This allowed a non-random motion paradigm and phase encoding direction. The mean difference in CJV/SNR/CNR between the real motion-corrupted images and our modified simulations (0.004±0.054/-0.7±1.8/-0.09±0.55) was lower than that of the original simulations (0.015±0.061/0.2±2.0/-0.29±0.62). Further, the mean difference in the DSC between the real motion-corrupted images was lower for our modified simulations (0.03±0.06) compared to the original simulations (-0.15±0.09). SynthSeg+ showed the highest robustness towards all forms of motion, real and simulated. In conclusion, reasonably realistic synthetic motion artifacts can be induced on a large-scale when only magnitude MR images are available to obtain unbiased data sets for the training of machine learning based models.

Accelerated Simultaneous T2 and T2* Mapping of Multiple Sclerosis Lesions Using Compressed Sensing Reconstruction of Radial RARE-EPI MRI.

(1) Background: Radial RARE-EPI MRI facilitates simultaneous T2 and T2* mapping (2in1-RARE-EPI). With modest undersampling (R = 2), the speed gain of 2in1-RARE-EPI relative to Multi-Spin-Echo and Multi-Gradient-Recalled-Echo references is limited. Further reduction in scan time is crucial for clinical studies investigating T2 and T2* as imaging biomarkers. We demonstrate the feasibility of further acceleration, utilizing compressed sensing (CS) reconstruction of highly undersampled 2in1-RARE-EPI. (2) Methods: Two-fold radially-undersampled 2in1-RARE-EPI data from phantoms, healthy volunteers (n = 3), and multiple sclerosis patients (n = 4) were used as references, and undersampled (Rextra = 1-12, effective undersampling Reff = 2-24). For each echo time, images were reconstructed using CS-reconstruction. For T2 (RARE module) and T2* mapping (EPI module), a linear least-square fit was applied to the images. T2 and T2* from CS-reconstruction of undersampled data were benchmarked against values from CS-reconstruction of the reference data. (3) Results: We demonstrate accelerated simultaneous T2 and T2* mapping using undersampled 2in1-RARE-EPI with CS-reconstruction is feasible. For Rextra = 6 (TA = 01:39 min), the overall MAPE was ≤8% (T2*) and ≤4% (T2); for Rextra = 12 (TA = 01:06 min), the overall MAPE was <13% (T2*) and <5% (T2). (4) Conclusion: Substantial reductions in scan time are achievable for simultaneous T2 and T2* mapping of the brain using highly undersampled 2in1-RARE-EPI with CS-reconstruction.

Advanced Radio Frequency Applicators for Thermal Magnetic Resonance Theranostics of Brain Tumors.

Thermal Magnetic Resonance (ThermalMR) is a theranostic concept that combines diagnostic magnetic resonance imaging (MRI) with targeted thermal therapy in the hyperthermia (HT) range using a radiofrequency (RF) applicator in an integrated system. ThermalMR adds a therapeutic dimension to a diagnostic MRI device. Focused, targeted RF heating of deep-seated brain tumors, accurate non-invasive temperature monitoring and high-resolution MRI are specific requirements of ThermalMR that can be addressed with novel concepts in RF applicator design. This work examines hybrid RF applicator arrays combining loop and self-grounded bow-tie (SGBT) dipole antennas for ThermalMR of brain tumors, at magnetic field strengths of 7.0 T, 9.4 T and 10.5 T. These high-density RF arrays improve the feasible transmission channel count, and provide additional degrees of freedom for RF shimming not afforded by using dipole antennas only, for superior thermal therapy and MRI diagnostics. These improvements are especially relevant for ThermalMR theranostics of deep-seated brain tumors because of the small surface area of the head. ThermalMR RF applicators with the hybrid loop+SGBT dipole design outperformed applicators using dipole-only and loop-only designs, with superior MRI performance and targeted RF heating. Array variants with a horse-shoe configuration covering an arc (270°) around the head avoiding the eyes performed better than designs with 360° coverage, with a 1.3 °C higher temperature rise inside the tumor while sparing healthy tissue. Our EMF and temperature simulations performed on a virtual patient with a clinically realistic intracranial tumor provide a technical foundation for implementation of advanced RF applicators tailored for ThermalMR theranostics of brain tumors.

First in vivo fluorine-19 magnetic resonance imaging of the multiple sclerosis drug siponimod.

Theranostic imaging methods could greatly enhance our understanding of the distribution of CNS-acting drugs in individual patients. Fluorine-19 magnetic resonance imaging (19F MRI) offers the opportunity to localize and quantify fluorinated drugs non-invasively, without modifications and without the application of ionizing or other harmful radiation. Here we investigated siponimod, a sphingosine 1-phosphate (S1P) receptor antagonist indicated for secondary progressive multiple sclerosis (SPMS), to determine the feasibility of in vivo 19F MR imaging of a disease modifying drug. Methods: The 19F MR properties of siponimod were characterized using spectroscopic techniques. Four MRI methods were investigated to determine which was the most sensitive for 19F MR imaging of siponimod under biological conditions. We subsequently administered siponimod orally to 6 mice and acquired 19F MR spectra and images in vivo directly after administration, and in ex vivo tissues. Results: The 19F transverse relaxation time of siponimod was 381 ms when dissolved in dimethyl sulfoxide, and substantially reduced to 5 ms when combined with serum, and to 20 ms in ex vivo liver tissue. Ultrashort echo time (UTE) imaging was determined to be the most sensitive MRI technique for imaging siponimod in a biological context and was used to map the drug in vivo in the stomach and liver. Ex vivo images in the liver and brain showed an inhomogeneous distribution of siponimod in both organs. In the brain, siponimod accumulated predominantly in the cerebrum but not the cerebellum. No secondary 19F signals were detected from metabolites. From a translational perspective, we found that acquisitions done on a 3.0 T clinical MR scanner were 2.75 times more sensitive than acquisitions performed on a preclinical 9.4 T MR setup when taking changes in brain size across species into consideration and using equivalent relative spatial resolution. Conclusion: Siponimod can be imaged non-invasively using 19F UTE MRI in the form administered to MS patients, without modification. This study lays the groundwork for more extensive preclinical and clinical investigations. With the necessary technical development, 19F MRI has the potential to become a powerful theranostic tool for studying the time-course and distribution of CNS-acting drugs within the brain, especially during pathology.

Denoising for Improved Parametric MRI of the Kidney: Protocol for Nonlocal Means Filtering.

In order to tackle the challenges caused by the variability in estimated MRI parameters (e.g., T2* and T2) due to low SNR a number of strategies can be followed. One approach is postprocessing of the acquired data with a filter. The basic idea is that MR images possess a local spatial structure that is characterized by equal, or at least similar, noise-free signal values in vicinities of a location. Then, local averaging of the signal reduces the noise component of the signal. In contrast, nonlocal means filtering defines the weights for averaging not only within the local vicinity, bur it compares the image intensities between all voxels to define "nonlocal" weights. Furthermore, it generally compares not only single-voxel intensities but small spatial patches of the data to better account for extended similar patterns. Here we describe how to use an open source NLM filter tool to denoise 2D MR image series of the kidney used for parametric mapping of the relaxation times T2* and T2.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers.

Data Preparation Protocol for Low Signal-to-Noise Ratio Fluorine-19 MRI.

Fluorine-19 MRI shows great promise for a wide range of applications including renal imaging, yet the typically low signal-to-noise ratios and sparse signal distribution necessitate a thorough data preparation.This chapter describes a general data preparation workflow for fluorine MRI experiments. The main processing steps are: (1) estimation of noise level, (2) correction of noise-induced bias and (3) background subtraction. The protocol is supplemented by an example script and toolbox available online.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concept and experimental procedure.

Functional Imaging Using Fluorine (19F) MR Methods: Basic Concepts.

Kidney-associated pathologies would greatly benefit from noninvasive and robust methods that can objectively quantify changes in renal function. In the past years there has been a growing incentive to develop new applications for fluorine (19F) MRI in biomedical research to study functional changes during disease states. 19F MRI represents an instrumental tool for the quantification of exogenous 19F substances in vivo. One of the major benefits of 19F MRI is that fluorine in its organic form is absent in eukaryotic cells. Therefore, the introduction of exogenous 19F signals in vivo will yield background-free images, thus providing highly selective detection with absolute specificity in vivo. Here we introduce the concept of 19F MRI, describe existing challenges, especially those pertaining to signal sensitivity, and give an overview of preclinical applications to illustrate the utility and applicability of this technique for measuring renal function in animal models.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction chapter is complemented by two separate chapters describing the experimental procedure and data analysis.

Analysis Protocols for MRI Mapping of the Blood Oxygenation-Sensitive Parameters T2* and T2 in the Kidney.

Renal hypoxia is generally accepted as a key pathophysiologic event in acute kidney injury of various origins and has also been suggested to play a role in the development of chronic kidney disease. Here we describe step-by-step data analysis protocols for MRI monitoring of renal oxygenation in rodents via the deoxyhemoglobin concentration sensitive MR parameters T2* and T2-a contrast mechanism known as the blood oxygenation level dependent (BOLD) effect.This chapter describes how to use the analysis tools provided by vendors of animal and clinical MR systems, as well as how to develop an analysis software. Aspects covered are: data quality checks, data exclusion, model fitting, fitting algorithm, starting values, effects of multiecho imaging, and result validation.This chapter is based upon work from the PARENCHIMA COST Action, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This experimental protocol chapter is complemented by two separate chapters describing the basic concept and data analysis.

In vivo detection of teriflunomide-derived fluorine signal during neuroinflammation using fluorine MR spectroscopy.

Background: Magnetic resonance imaging (MRI) is indispensable for diagnosing neurological conditions such as multiple sclerosis (MS). MRI also supports decisions regarding the choice of disease-modifying drugs (DMDs). Determining in vivo tissue concentrations of DMDs has the potential to become an essential clinical tool for therapeutic drug monitoring (TDM). The aim here was to examine the feasibility of fluorine-19 (19F) MR methods to detect the fluorinated DMD teriflunomide (TF) during normal and pathological conditions. Methods: We used 19F MR spectroscopy to detect TF in the experimental autoimmune encephalomyelitis (EAE) mouse model of multiple sclerosis (MS) in vivo. Prior to the in vivo investigations we characterized the MR properties of TF in vitro. We studied the impact of pH and protein binding as well as MR contrast agents. Results: We could detect TF in vivo and could follow the 19F MR signal over different time points of disease. We quantified TF concentrations in different tissues using HPLC/MS and showed a significant correlation between ex vivo TF levels in serum and the ex vivo19F MR signal. Conclusion: This study demonstrates the feasibility of 19F MR methods to detect TF during neuroinflammation in vivo. It also highlights the need for further technological developments in this field. The ultimate goal is to add 19F MR protocols to conventional 1H MRI protocols in clinical practice to guide therapy decisions.

Pentafluorosulfanyl (SF5) as a Superior 19F Magnetic Resonance Reporter Group: Signal Detection and Biological Activity of Teriflunomide Derivatives.

Fluorine (19F) magnetic resonance imaging (MRI) is severely limited by a low signal-to noise ratio (SNR), and tapping it for 19F drug detection in vivo still poses a significant challenge. However, it bears the potential for label-free theranostic imaging. Recently, we detected the fluorinated dihydroorotate dehydrogenase (DHODH) inhibitor teriflunomide (TF) noninvasively in an animal model of multiple sclerosis (MS) using 19F MR spectroscopy (MRS). In the present study, we probed distinct modifications to the CF3 group of TF to improve its SNR. This revealed SF5 as a superior alternative to the CF3 group. The value of the SF5 bioisostere as a 19F MRI reporter group within a biological or pharmacological context is by far underexplored. Here, we compared the biological and pharmacological activities of different TF derivatives and their 19F MR properties (chemical shift and relaxation times). The 19F MR SNR efficiency of three MRI methods revealed that SF5-substituted TF has the highest 19F MR SNR efficiency in combination with an ultrashort echo-time (UTE) MRI method. Chemical modifications did not reduce pharmacological or biological activity as shown in the in vitro dihydroorotate dehydrogenase enzyme and T cell proliferation assays. Instead, SF5-substituted TF showed an improved capacity to inhibit T cell proliferation, indicating better anti-inflammatory activity and its suitability as a viable bioisostere in this context. This study proposes SF5 as a novel superior 19F MR reporter group for the MS drug teriflunomide.

Variational Bayesian Parameter Estimation Techniques for the General Linear Model.

Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation techniques are rarely covered in introductory statistical texts. Because of the widespread practical use of VB, VML, ReML, and ML in the neuroimaging community, we reasoned that a theoretical treatment of their relationships and their application in a basic modeling scenario may be helpful for both neuroimaging novices and practitioners alike. In this technical study, we thus revisit the conceptual and formal underpinnings of VB, VML, ReML, and ML and provide a detailed account of their mathematical relationships and implementational details. We further apply VB, VML, ReML, and ML to the general linear model (GLM) with non-spherical error covariance as commonly encountered in the first-level analysis of fMRI data. To this end, we explicitly derive the corresponding free energy objective functions and ensuing iterative algorithms. Finally, in the applied part of our study, we evaluate the parameter and model recovery properties of VB, VML, ReML, and ML, first in an exemplary setting and then in the analysis of experimental fMRI data acquired from a single participant under visual stimulation.

Enhanced Fluorine-19 MRI Sensitivity using a Cryogenic Radiofrequency Probe: Technical Developments and Ex Vivo Demonstration in a Mouse Model of Neuroinflammation.

Neuroinflammation can be monitored using fluorine-19 (19F)-containing nanoparticles and 19F MRI. Previously we studied neuroinflammation in experimental autoimmune encephalomyelitis (EAE) using room temperature (RT) 19F radiofrequency (RF) coils and low spatial resolution 19F MRI to overcome constraints in signal-to-noise ratio (SNR). This yielded an approximate localization of inflammatory lesions. Here we used a new 19F transceive cryogenic quadrature RF probe ( 19 F-CRP) that provides the SNR necessary to acquire superior spatially-resolved 19F MRI. First we characterized the signal-transmission profile of the 19 F-CRP. The 19 F-CRP was then benchmarked against a RT 19F/1H RF coil. For SNR comparison we used reference compounds including 19F-nanoparticles and ex vivo brains from EAE mice administered with 19F-nanoparticles. The transmit/receive profile of the 19 F-CRP diminished with increasing distance from the surface. This was counterbalanced by a substantial SNR gain compared to the RT coil. Intraparenchymal inflammation in the ex vivo EAE brains was more sharply defined when using 150 µm isotropic resolution with the 19 F-CRP, and reflected the known distribution of EAE histopathology. At this spatial resolution, most 19F signals were undetectable using the RT coil. The 19 F-CRP is a valuable tool that will allow us to study neuroinflammation with greater detail in future in vivo studies.

A normative inference approach for optimal sample sizes in decisions from experience.

"Decisions from experience" (DFE) refers to a body of work that emerged in research on behavioral decision making over the last decade. One of the major experimental paradigms employed to study experience-based choice is the "sampling paradigm," which serves as a model of decision making under limited knowledge about the statistical structure of the world. In this paradigm respondents are presented with two payoff distributions, which, in contrast to standard approaches in behavioral economics, are specified not in terms of explicit outcome-probability information, but by the opportunity to sample outcomes from each distribution without economic consequences. Participants are encouraged to explore the distributions until they feel confident enough to decide from which they would prefer to draw from in a final trial involving real monetary payoffs. One commonly employed measure to characterize the behavior of participants in the sampling paradigm is the sample size, that is, the number of outcome draws which participants choose to obtain from each distribution prior to terminating sampling. A natural question that arises in this context concerns the "optimal" sample size, which could be used as a normative benchmark to evaluate human sampling behavior in DFE. In this theoretical study, we relate the DFE sampling paradigm to the classical statistical decision theoretic literature and, under a probabilistic inference assumption, evaluate optimal sample sizes for DFE. In our treatment we go beyond analytically established results by showing how the classical statistical decision theoretic framework can be used to derive optimal sample sizes under arbitrary, but numerically evaluable, constraints. Finally, we critically evaluate the value of deriving optimal sample sizes under this framework as testable predictions for the experimental study of sampling behavior in DFE.