Water removal in MR spectroscopic imaging with Casorati singular value decomposition.

PURPOSE: Water removal is one of the computational bottlenecks in the processing of high-resolution MRSI data. The purpose of this work is to propose an approach to reduce the computing time required for water removal in large MRS data. METHODS: In this work, we describe a singular value decomposition-based approach that uses the partial position-time separability and the time-domain linear predictability of MRSI data to reduce the computational time required for water removal. Our approach arranges MRS signals in a Casorati matrix form, applies low-rank approximations utilizing singular value decomposition, removes residual water from the most prominent left-singular vectors, and finally reconstructs the water-free matrix using the processed left-singular vectors. RESULTS: We have demonstrated the effectiveness of our proposed algorithm for water removal using both simulated and in vivo data. The proposed algorithm encompasses a pip-installable tool ( https://pypi.org/project/CSVD/), available on GitHub ( https://github.com/amirshamaei/CSVD), empowering researchers to use it in future studies. Additionally, to further promote transparency and reproducibility, we provide comprehensive code for result replication. CONCLUSIONS: The findings of this study suggest that the proposed method is a promising alternative to existing water removal methods due to its low processing time and good performance in removing water signals.

Model-informed unsupervised deep learning approaches to frequency and phase correction of MRS signals.

PURPOSE: A supervised deep learning (DL) approach for frequency and phase correction (FPC) of MRS data recently showed encouraging results, but obtaining transients with labels for supervised learning is challenging. This work investigates the feasibility and efficiency of unsupervised deep learning-based FPC. METHODS: Two novel deep learning-based FPC methods (deep learning-based Cr referencing and deep learning-based spectral registration), which use a priori physics domain knowledge, are presented. The proposed networks were trained, validated, and evaluated using simulated, phantom, and publicly accessible in vivo MEGA-edited MRS data. The performance of our proposed FPC methods was compared with other generally used FPC methods, in terms of precision and time efficiency. A new measure was proposed in this study to evaluate the FPC method performance. The ability of each of our methods to carry out FPC at varying SNR levels was evaluated. A Monte Carlo study was carried out to investigate the performance of our proposed methods. RESULTS: The validation using low-SNR manipulated simulated data demonstrated that the proposed methods could perform FPC comparably with other methods. The evaluation showed that the deep learning-based spectral registration over a limited frequency range method achieved the highest performance in phantom data. The applicability of the proposed method for FPC of GABA-edited in vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. CONCLUSIONS: The proposed physics-informed deep neural networks trained in an unsupervised manner with complex data can offer efficient FPC of large MRS data in a shorter time.

Diffusion kurtosis imaging detects the time-dependent progress of pathological changes in the oral rotenone mouse model of Parkinson's disease.

Clinical diagnosis of Parkinson's disease (PD) occurs typically when a substantial proportion of dopaminergic neurons in the substantia nigra (SN) already died, and the first motor symptoms appear. Therefore, tools enabling the early diagnosis of PD are essential to identify early-stage PD patients in which neuroprotective treatments could have a significant impact. Here, we test the utility and sensitivity of the diffusion kurtosis imaging (DKI) in detecting progressive microstructural changes in several brain regions of mice exposed to chronic intragastric administration of rotenone, a mouse model that mimics the spatiotemporal progression of PD-like pathology from the ENS to the SN as described by Braak's staging. Our results show that DKI, especially kurtosis, can detect the progression of pathology-associated changes throughout the CNS. Increases in mean kurtosis were first observed in the dorsal motor nucleus of the vagus (DMV) after 2 months of exposure to rotenone and before the loss of dopaminergic neurons in the SN occurred. Remarkably, we also show that limited exposure to rotenone for 2 months is enough to trigger the progression of the disease in the absence of the environmental toxin, thus suggesting that once the first pathological changes in one region appear, they can self-perpetuate and progress within the CNS. Overall, our results show that DKI can be a useful radiological marker for the early detection and monitoring of PD pathology progression in patients with the potential to improve the clinical diagnosis and the development of neuroprotective treatments.

In vivo macromolecule signals in rat brain 1 H-MR spectra at 9.4T: Parametrization, spline baseline estimation, and T2 relaxation times.

PURPOSE: Reliable detection and fitting of macromolecules (MM) are crucial for accurate quantification of brain short-echo time (TE) 1 H-MR spectra. An experimentally acquired single MM spectrum is commonly used. Higher spectral resolution at ultra-high field (UHF) led to increased interest in using a parametrized MM spectrum together with flexible spline baselines to address unpredicted spectroscopic components. Herein, we aimed to: (1) implement an advanced methodological approach for post-processing, fitting, and parametrization of 9.4T rat brain MM spectra; (2) assess the concomitant impact of the LCModel baseline and MM model (ie, single vs parametrized); and (3) estimate the apparent T2 relaxation times for seven MM components. METHODS: A single inversion recovery sequence combined with advanced AMARES prior knowledge was used to eliminate the metabolite residuals, fit, and parametrize 10 MM components directly from 9.4T rat brain in vivo 1 H-MR spectra at different TEs. Monte Carlo simulations were also used to assess the concomitant influence of parametrized MM and DKNTMN parameter in LCModel. RESULTS: A very stiff baseline (DKNTMN ≥ 1 ppm) in combination with a single MM spectrum led to deviations in metabolite concentrations. For some metabolites the parametrized MM showed deviations from the ground truth for all DKNTMN values. Adding prior knowledge on parametrized MM improved MM and metabolite quantification. The apparent T2 ranged between 12 and 24 ms for seven MM peaks. CONCLUSION: Moderate flexibility in the spline baseline was required for reliable quantification of real/experimental spectra based on in vivo and Monte Carlo data. Prior knowledge on parametrized MM improved MM and metabolite quantification.

Contribution of macromolecules to brain 1 H MR spectra: Experts' consensus recommendations.

Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper.

Thermo- and ROS-Responsive Self-Assembled Polymer Nanoparticle Tracers for 19F MRI Theranostics.

Fluorine-19 magnetic resonance imaging (19F MRI) enables detailed in vivo tracking of fluorine-containing tracers and is therefore becoming a particularly useful tool in noninvasive medical imaging. In previous studies, we introduced biocompatible polymers based on the hydrophilic monomer N-(2-hydroxypropyl)methacrylamide (HPMA) and the thermoresponsive monomer N-(2,2-difluoroethyl)acrylamide (DFEA). These polymers have abundant magnetically equivalent fluorine atoms and advantageous properties as 19F MRI tracers. Furthermore, in this pilot study, we modified these polymers by introducing a redox-responsive monomer. As a result, our polymers changed their physicochemical properties once exposed to an oxidative environment. Reactive oxygen species (ROS)-responsive polymers were prepared by incorporating small amounts (0.9-4.5 mol %) of the N-[2-(ferrocenylcarboxamido)ethyl]acrylamide (FcCEA) monomer, which is hydrophobic and diamagnetic in the reduced electroneutral (Fe(II), ferrocene) state but hydrophilic and paramagnetic in the oxidized (Fe(III), ferrocenium cation) state. This property can be useful for theranostic purposes (therapy and diagnostic purposes), especially, in terms of ROS-responsive drug-delivery systems. In the reduced state, these nanoparticles remain self-assembled with the encapsulated drug but release the drug upon oxidation in ROS-rich tumors or inflamed tissues.

Simple compensation method for improved half-pulse excitation profile with rephasing gradient.

PURPOSE: To improve the slice profile quality obtained by RF half-pulse excitation for 2D-UTE applications. METHODS: The overall first-order and zero-order phase errors along the slice-selection direction were obtained with the help of an optimization task to minimize the out-of-slice signal contamination from the calibration 1-dimenisonal (1D) profile data. The time-phase-error evolution was approximated from the k-space readout data, which were acquired primarily for correction of the readout trajectories during data regridding to the rectilinear grids. The correction of the slice profile was achieved by rephasing gradient pulses applied immediately after the end of excitation. The total prescan calibration typically took less than 2 minutes. RESULTS: The improved image quality using the proposed calibration method was demonstrated both on phantoms and on ankle images obtained from healthy volunteers. It was demonstrated that calibration can be performed either as a separate water phantom measurement or directly as a prescan procedure. CONCLUSION: The slice-profile distortion from the half-pulse excitation could substantially affect the overall fidelity of 2D-UTE images. The presented experiments proved that the image quality could be substantially increased by application of the proposed slice-correction method.

Low-level fat fraction quantification at 3 T: comparative study of different tools for water-fat reconstruction and MR spectroscopy.

OBJECTIVES: Chemical Shift Encoded Magnetic Resonance Imaging (CSE-MRI)-based quantification of low-level (< 5% of proton density fat fraction-PDFF) fat infiltration requires highly accurate data reconstruction for the assessment of hepatic or pancreatic fat accumulation in diagnostics and biomedical research. MATERIALS AND METHODS: We compare three software tools available for water/fat image reconstruction and PDFF quantification with MRS as the reference method. Based on the algorithm exploited in the tested software, the accuracy of fat fraction quantification varies. We evaluate them in phantom and in vivo MRS and MRI measurements. RESULTS: The signal model of Intralipid 20% emulsion used for phantoms was established for 3 T and 9.4 T fields. In all cases, we noticed a high coefficient of determination (R-squared) between MRS and MRI-PDFF measurements: in phantoms <0.9924-0.9990>; and in vivo <0.8069-0.9552>. Bland-Altman analysis was applied to phantom and in vivo measurements. DISCUSSION: Multi-echo MRI in combination with an advanced algorithm including multi-peak spectrum modeling appears as a valuable and accurate method for low-level PDFF quantification over large FOV in high resolution, and is much faster than MRS methods. The graph-cut algorithm (GC) showed the fewest water/fat swaps in the PDFF maps, and hence stands out as the most robust method of those tested.

Processing tracking in jMRUI software for magnetic resonance spectra quantitation reproducibility assurance.

BACKGROUND: Proton magnetic resonance spectroscopy is a non-invasive measurement technique which provides information about concentrations of up to 20 metabolites participating in intracellular biochemical processes. In order to obtain any metabolic information from measured spectra a processing should be done in specialized software, like jMRUI. The processing is interactive and complex and often requires many trials before obtaining a correct result. This paper proposes a jMRUI enhancement for efficient and unambiguous history tracking and file identification. RESULTS: A database storing all processing steps, parameters and files used in processing was developed for jMRUI. The solution was developed in Java, authors used a SQL database for robust storage of parameters and SHA-256 hash code for unambiguous file identification. The developed system was integrated directly in jMRUI and it will be publically available. A graphical user interface was implemented in order to make the user experience more comfortable. The database operation is invisible from the point of view of the common user, all tracking operations are performed in the background. CONCLUSIONS: The implemented jMRUI database is a tool that can significantly help the user to track the processing history performed on data in jMRUI. The created tool is oriented to be user-friendly, robust and easy to use. The database GUI allows the user to browse the whole processing history of a selected file and learn e.g. what processing lead to the results, where the original data are stored, to obtain the list of all processing actions performed on spectra.

Quantum-mechanical simulations for in vivo MR spectroscopy: Principles and possibilities demonstrated with the program NMRScopeB.

Current possibilities and limitations of the simulation of in vivo magnetic resonance spectroscopic signals are demonstrated from the point of view of a simulation software user as well as its programmer. A brief review of the quantum-mechanical background addresses the specific needs of simulation implementation and in vivo MR spectroscopy in general. Practical application examples demonstrate how flexible simulation software, such as NMRScopeB, can be utilized not only for the preparation of metabolite basis signals for quantification of metabolite concentrations, but also in pulse sequence development, assessment of artifacts and analyzing mechanism leading to unexpected signal phenomena.

Early and progressive microstructural brain changes in mice overexpressing human α-Synuclein detected by diffusion kurtosis imaging.

Diffusion kurtosis imaging (DKI) is sensitive in detecting α-Synuclein (α-Syn) accumulation-associated microstructural changes at late stages of the pathology in α-Syn overexpressing TNWT-61 mice. The aim of this study was to perform DKI in young TNWT-61 mice when α-Syn starts to accumulate and to compare the imaging results with an analysis of motor and memory impairment and α-Syn levels. Three-month-old (3mo) and six-month-old (6mo) mice underwent DKI scanning using the Bruker Avance 9.4T magnetic resonance imaging system. Region of interest (ROI) analyses were performed in the gray matter; tract-based spatial statistics (TBSS) analyses were performed in the white matter. In the same mice, α-Syn expression was evaluated using quantitative immunofluorescence. Mean kurtosis (MK) was the best differentiator between TNWT-61 mice and wildtype (WT) mice. We found increases in MK in 3mo TNWT-61 mice in the striatum and thalamus but not in the substantia nigra (SN), hippocampus, or sensorimotor cortex, even though the immunoreactivity of human α-Syn was similar or even higher in the latter regions. Increases in MK in the SN were detected in 6mo mice. These findings indicate that α-Syn accumulation-associated changes may start in areas with a high density of dopaminergic nerve terminals. We also found TBSS changes in white matter only at 6mo, suggesting α-Syn accumulation-associated changes start in the gray matter and later progress to the white matter. MK is able to detect microstructural changes induced by α-Syn overexpression in TNWT-61 mice and could be a useful clinical tool for detecting early-stage Parkinson's disease in human patients.

Rhodamine bound maghemite as a long-term dual imaging nanoprobe of adipose tissue-derived mesenchymal stromal cells.

In the last few years, magnetically labeled cells have been intensively explored, and non-invasive cell tracking and magnetic manipulation methods have been tested in preclinical studies focused on cell transplantation. For clinical applications, it is desirable to know the intracellular pathway of nanoparticles, which can predict their biocompatibility with cells and the long-term imaging properties of labeled cells. Here, we quantified labeling efficiency, localization, and fluorescence properties of Rhodamine derivatized superparamagnetic maghemite nanoparticles (SAMN-R) in mesenchymal stromal cells (MSC). We investigated the stability of SAMN-R in the intracellular space during a long culture (20 days). Analyses were based on advanced confocal microscopy accompanied by atomic absorption spectroscopy (AAS) and magnetic resonance imaging. SAMN-R displayed excellent cellular uptake (24 h of labeling), and no toxicity of SAMN-R labeling was found. 83% of SAMN-R nanoparticles were localized in lysosomes, only 4.8% were found in mitochondria, and no particles were localized in the nucleus. On the basis of the MSC fluorescence measurement every 6 days, we also quantified the continual decrease of SAMN-R fluorescence in the average single MSC during 18 days. An additional set of analyses showed that the intracellular SAMN-R signal decrease was minimally caused by fluorophore degradation or nanoparticles extraction from the cells, main reason is a cell division. The fluorescence of SAMN-R nanoparticles within the cells was detectable minimally for 20 days. These observations indicate that SAMN-R nanoparticles have a potential for application in transplantation medicine.

Late-stage α-synuclein accumulation in TNWT-61 mouse model of Parkinson's disease detected by diffusion kurtosis imaging.

Diffusion kurtosis imaging (DKI) by measuring non-Gaussian diffusion allows an accurate estimation of the distribution of water molecule displacement and may correctly characterize microstructural brain changes caused by neurodegeneration. The aim of this study was to evaluate the ability of DKI to detect changes induced by α-synuclein (α-syn) accumulation in α-syn over-expressing transgenic mice (TNWT-61) in both gray matter (GM) and white matter (WM) using region of interest (ROI) and tract-based spatial statistics analyses, respectively, and to explore the relationship between α-syn accumulation and DKI metrics in our regions of interest. Fourteen-month-old TNWT-61 mice and wild-type (WT) littermates underwent in vivo DKI scanning using the Bruker Avance 9.4 Tesla magnetic resonance imaging system. ROI analysis in the GM regions substantia nigra, striatum, hippocampus, sensorimotor cortex, and thalamus and tract-based spatial statistics analysis in WM were performed. Immunohistochemistry for α-syn was performed in TNWT-61 mice and correlated with DKI findings. We found increased kurtosis and decreased diffusivity values in GM regions such as the thalamus and sensorimotor cortex, and in WM regions such as the external and internal capsule, mamillothalamic tract, anterior commissure, cingulum, and corpus callosum in TNWT-61 mice as compared to WT mice. Furthermore, we report for the first time that α-syn accumulation is positively correlated with kurtosis and negatively correlated with diffusivity in the thalamus. The study provides evidence of an association between the amount of α-syn and the magnitude of DKI metric changes in the ROIs, with the potential of improving the clinical diagnosis of Parkinson's disease. We propose diffusion kurtosis imaging as a sensitive method for detecting human α-synuclein accumulation-induced changes in brain tissue, which may be reflective of Parkinson disease stage. Boxplots show the averaged mean kurtosis (orange) and mean diffusivity (blue) under the results of the analysis (*p < 0.05) in brains of wild-type (WT) and α-synuclein over-expressing (TNWT-61) mice. This approach might represent a novel biomarker for the early diagnosis of Parkinson's disease. Read the Editorial Highlight for this article on page 1117.

Distributed capillary adiabatic tissue homogeneity model in parametric multi-channel blind AIF estimation using DCE-MRI.

PURPOSE: One of the main challenges in quantitative dynamic contrast-enhanced (DCE) MRI is estimation of the arterial input function (AIF). Usually, the signal from a single artery (ignoring contrast dispersion, partial volume effects and flow artifacts) or a population average of such signals (also ignoring variability between patients) is used. METHODS: Multi-channel blind deconvolution is an alternative approach avoiding most of these problems. The AIF is estimated directly from the measured tracer concentration curves in several tissues. This contribution extends the published methods of multi-channel blind deconvolution by applying a more realistic model of the impulse residue function, the distributed capillary adiabatic tissue homogeneity model (DCATH). In addition, an alternative AIF model is used and several AIF-scaling methods are tested. RESULTS: The proposed method is evaluated on synthetic data with respect to the number of tissue regions and to the signal-to-noise ratio. Evaluation on clinical data (renal cell carcinoma patients before and after the beginning of the treatment) gave consistent results. An initial evaluation on clinical data indicates more reliable and less noise sensitive perfusion parameter estimates. CONCLUSION: Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup.

Blind deconvolution decreases requirements on temporal resolution of DCE-MRI: Application to 2nd generation pharmacokinetic modeling.

PURPOSE: Dynamic Contrast-Enhanced (DCE) MRI with 2nd generation pharmacokinetic models provides estimates of plasma flow and permeability surface-area product in contrast to the broadly used 1st generation models (e.g. the Tofts models). However, the use of 2nd generation models requires higher frequency with which the dynamic images are acquired (around 1.5 s per image). Blind deconvolution can decrease the demands on temporal resolution as shown previously for one of the 1st generation models. Here, the temporal-resolution requirements achievable for blind deconvolution with a 2nd generation model are studied. METHODS: The 2nd generation model is formulated as the distributed-capillary adiabatic-tissue-homogeneity (DCATH) model. Blind deconvolution is based on Parker's model of the arterial input function. The accuracy and precision of the estimated arterial input functions and the perfusion parameters is evaluated on synthetic and real clinical datasets with different levels of the temporal resolution. RESULTS: The estimated arterial input functions remained unchanged from their reference high-temporal-resolution estimates (obtained with the sampling interval around 1 s) when increasing the sampling interval up to about 5 s for synthetic data and up to 3.6-4.8 s for real data. Further increasing of the sampling intervals led to systematic distortions, such as lowering and broadening of the 1st pass peak. The resulting perfusion-parameter estimation error was below 10% for the sampling intervals up to 3 s (synthetic data), in line with the real data perfusion-parameter boxplots which remained unchanged up to the sampling interval 3.6 s. CONCLUSION: We show that use of blind deconvolution decreases the demands on temporal resolution in DCE-MRI from about 1.5 s (in case of measured arterial input functions) to 3-4 s. This can be exploited in increased spatial resolution or larger organ coverage.

Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data.

PURPOSE: While the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results; however, supervised learning requires ground truth fitted spectra, which is not practical. Moreover, this work investigates the feasibility and efficiency of the LCM-based self-supervised DL method for the analysis of MRS data. METHOD: We present a novel DL-based method for the quantification of relative metabolite concentrations, using quantum-mechanics simulated metabolite responses and neural networks. We trained, validated, and evaluated the proposed networks with simulated and publicly accessible in-vivo human brain MRS data and compared the performance with traditional methods. A novel adaptive macromolecule fitting algorithm is included. We investigated the performance of the proposed methods in a Monte Carlo (MC) study. RESULT: The validation using low-SNR simulated data demonstrated that the proposed methods could perform quantification comparably to other methods. The applicability of the proposed method for the quantification of in-vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. CONCLUSION: The proposed model-constrained deep neural networks trained in a self-supervised manner can offer fast and efficient quantification of MRS and MRSI data. Our proposed method has the potential to facilitate clinical practice by enabling faster processing of large datasets such as high-resolution MRSI datasets, which may have thousands of spectra.

Laterality in functional and metabolic state of the bulbectomised rat brain detected by ASL and 1H MRS: A pilot study.

OBJECTIVES: Pilot study validating the animal model of depression - the bilateral olfactory bulbectomy in rats - by two nuclear magnetic resonance methods, indirectly detecting the metabolic state of the brain. Furthermore, the study focussed on potential differences in brain laterality. METHODS: Arterial spin labelling assessed cerebral brain flow in prefrontal, sensorimotor, and piriform cortices, nucleus accumbens, hippocampus, thalamus, circle of Willis, and whole brain. Proton magnetic resonance spectroscopy provided information about relative metabolite concentrations in the cortex and hippocampus. RESULTS: Arterial spin labelling found no differences in cerebral perfusion in the group comparison but revealed lateralisation in the thalamus of the control group and the sensorimotor cortex of the bulbectomized rats. Lower Cho/tCr and Cho/NAA levels were found in the right hippocampus in bulbectomized rats. The differences in lateralisation were shown in the hippocampus: mI/tCr in the control group, Cho/NAA, NAA/tCr, Tau/tCr in the model group, and in the cortex: NAA/tCr, mI/tCr in the control group. CONCLUSION: Olfactory bulbectomy affects the neuronal and biochemical profile of the rat brain laterally and, as a model of depression, was validated by two nuclear magnetic resonance methods.

Feasibility of Hepatic Fat Quantification Using Proton Density Fat Fraction by Multi-Echo Chemical-Shift-Encoded MRI at 7T.

Fat fraction quantification and assessment of its distribution in the hepatic tissue become more important with the growing epidemic of obesity, and the increasing prevalence of diabetes mellitus type 2 and non-alcoholic fatty liver disease. At 3Tesla, the multi-echo, chemical-shift-encoded magnetic resonance imaging (CSE-MRI)-based acquisition allows the measurement of proton density fat-fraction (PDFF) even in clinical protocols. Further improvements in SNR can be achieved by the use of phased array coils and increased static magnetic field. The purpose of the study is to evaluate the feasibility of PDFF imaging using a multi-echo CSE-MRI technique at ultra-high magnetic field (7Tesla). Thirteen volunteers (M/F) with a broad range of age, body mass index, and hepatic PDFF were measured at 3 and 7T by multi-gradient-echo MRI and single-voxel spectroscopy MRS. All measurements were performed in breath-hold (exhalation); the MRI protocols were optimized for a short measurement time, thus minimizing motion-related problems. 7T data were processed off-line using Matlab® (MRI:multi-gradient-echo) and jMRUI (MRS), respectively. For quantitative validation of the PDFF results, a similar protocol was performed at 3T, including on-line data processing provided by the system manufacturer, and correlation analyses between 7 and 3T data were performed off-line. The multi-echo CSE-MRI measurements at 7T with a phased-array coil configuration and an optimal post-processing yielded liver volume coverage ranging from 30 to 90% for high- and low-BMI subjects, respectively. PDFFs ranged between 1 and 20%. We found significant correlations between 7T MRI and -MRS measurements (R2 ≅ 0.97; p < 0.005), and between MRI-PDFF at 7T and 3T fields (R2 ≅ 0.94; p < 0.005) in the evaluated volumes. Based on the measurements and analyses performed, the multi-echo CSE-MRI method using a 32-channel coil at 7T showed its aptitude for MRI-based quantitation of PDFF in the investigated volumes. The results are the first step toward qMRI of the whole liver at 7T with further improvements in hardware.

Altered dopamine D3 receptor gene expression in MAM model of schizophrenia is reversed by peripubertal cannabidiol treatment.

Gestational methylazoxymethanol acetate (MAM) treatment produces offspring with adult phenotype relevant to schizophrenia, including positive- and negative-like symptoms, cognitive deficits, dopaminergic dysfunction, structural and functional abnormalities. Here we show that adult rats prenatally treated with MAM at gestational day 17 display significant increase in dopamine D3 receptor (D3) mRNA expression in prefrontal cortex (PFC), hippocampus and nucleus accumbens, accompanied by increased expression of dopamine D2 receptor (D2) mRNA exclusively in the PFC. Furthermore, a significant change in the blood perfusion at the level of the circle of Willis and hippocampus, paralleled by the enlargement of lateral ventricles, was also detected by magnetic resonance imaging (MRI) techniques. Peripubertal treatment with the non-euphoric phytocannabinoid cannabidiol (30 mg/kg) from postnatal day (PND) 19 to PND 39 was able to reverse in MAM exposed rats: i) the up-regulation of the dopamine D3 receptor mRNA (only partially prevented by haloperidol 0.6 mg/kg/day); and ii) the regional blood flow changes in MAM exposed rats. Molecular modelling predicted that cannabidiol could bind preferentially to dopamine D3 receptor, where it may act as a partial agonist according to conformation of ionic-lock, which is highly conserved in GPCRs. In summary, our results demonstrate that the mRNA expression of both dopamine D2 and D3 receptors is altered in the MAM model; however only the transcript levels of D3 are affected by cannabidiol treatment, likely suggesting that this gene might not only contribute to the schizophrenia symptoms but also represent an unexplored target for the antipsychotic activity of cannabidiol.

Different effects of prenatal MAM vs. perinatal THC exposure on regional cerebral blood perfusion detected by Arterial Spin Labelling MRI in rats.

Clinical studies consistently report structural impairments (i.e.: ventricular enlargement, decreased volume of anterior cingulate cortex or hippocampus) and functional abnormalities including changes in regional cerebral blood flow in individuals suffering from schizophrenia, which can be evaluated by magnetic resonance imaging (MRI) techniques. The aim of this study was to assess cerebral blood perfusion in several schizophrenia-related brain regions using Arterial Spin Labelling MRI (ASL MRI, 9.4 T Bruker BioSpec 94/30USR scanner) in rats. In this study, prenatal exposure to methylazoxymethanol acetate (MAM, 22 mg/kg) at gestational day (GD) 17 and the perinatal treatment with Δ-9-tetrahydrocannabinol (THC, 5 mg/kg) from GD15 to postnatal day 9 elicited behavioral deficits consistent with schizophrenia-like phenotype, which is in agreement with the neurodevelopmental hypothesis of schizophrenia. In MAM exposed rats a significant enlargement of lateral ventricles and perfusion changes (i.e.: increased blood perfusion in the circle of Willis and sensorimotor cortex and decreased perfusion in hippocampus) were detected. On the other hand, the THC perinatally exposed rats did not show differences in the cerebral blood perfusion in any region of interest. These results suggest that although both pre/perinatal insults showed some of the schizophrenia-like deficits, these are not strictly related to distinct hemodynamic features.