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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Olanzapine exposure diminishes perfusion and decreases volume of sensorimotor cortex in rats.

BACKGROUND: Olanzapine is a frequently used atypical antipsychotic drug known to exert structural brain alterations in animals. This study investigated whether chronic olanzapine exposure alters regional blood brain perfusion assessed by Arterial Spin Labelling (ASL) magnetic resonance imaging (MRI) in a validated model of olanzapine-induced metabolic disturbances. An effect of acute olanzapine exposure on brain perfusion was also assessed for comparison. METHODS: Adult Sprague-Dawley female rats were treated by intramuscular depot olanzapine injections (100 mg/kg every 14 days) or vehicle for 8 weeks. ASL scanning was performed on a 9.4 T Bruker BioSpec 94/30USR scanner under isoflurane anesthesia. Serum samples were used to assay leptin and TNF-α level while brains were sliced for histology. Another group received only one non-depot intraperitoneal dose of olanzapine (7 mg/kg) during MRI scanning, thus exposing its acute effect on brain perfusion. RESULTS: Both acute and chronic dosing of olanzapine resulted in decreased perfusion in the sensorimotor cortex, while no effect was observed in the piriform cortex or hippocampus. Furthermore, in the chronically treated group decreased cortex volume was observed. Chronic olanzapine dosing led to increased body weight, adipose tissue mass and leptin level, confirming its expected metabolic effects. CONCLUSION: This study demonstrates region-specific decreases in blood perfusion associated with olanzapine exposure present already after the first dose. These findings extend our understanding of olanzapine-induced functional and structural brain changes.

Diffusion Kurtosis Imaging Detects Microstructural Changes in a Methamphetamine-Induced Mouse Model of Parkinson's Disease.

Methamphetamine (METH) abuse is known to increase the risk of Parkinson's disease (PD) due to its dopaminergic neurotoxicity. This is the rationale for the METH model of PD developed by toxic METH dosing (10 mg/kg four times every 2 h) which features robust neurodegeneration and typical motor impairment in mice. In this study, we used diffusion kurtosis imaging to reveal microstructural brain changes caused by METH-induced neurodegeneration. The METH-treated mice and saline-treated controls underwent diffusion kurtosis imaging scanning using the Bruker Avance 9.4 Tesla MRI system at two time-points: 5 days and 1 month to capture both early and late changes induced by METH. At 5 days, we found a decrease in kurtosis in substantia nigra, striatum and sensorimotor cortex, which is likely to indicate loss of DAergic neurons. At 1 month, we found an increase of kurtosis in striatum and sensorimotor cortex and hippocampus, which may reflect certain recovery processes. Furthermore, we performed tract-based spatial statistics analysis in the white matter and at 1 month, we observed increased kurtosis in ventral nucleus of the lateral lemniscus and some of the lateral thalamic nuclei. No changes were present at the early stage. This study confirms the ability of diffusion kurtosis imaging to detect microstructural pathological processes in both grey and white matter in the METH model of PD. The exact mechanisms underlying the kurtosis changes remain to be elucidated but kurtosis seems to be a valuable biomarker for tracking microstructural brain changes in PD and potentially other neurodegenerative disorders.

Blind deconvolution estimation of an arterial input function for small animal DCE-MRI.

PURPOSE: One of the main obstacles for reliable quantitative dynamic contrast-enhanced (DCE) MRI is the need for accurate knowledge of the arterial input function (AIF). This is a special challenge for preclinical small animal applications where it is very difficult to measure the AIF without partial volume and flow artifacts. Furthermore, using advanced pharmacokinetic models (allowing estimation of blood flow and permeability-surface area product in addition to the classical perfusion parameters) poses stricter requirements on the accuracy and precision of AIF estimation. This paper addresses small animal DCE-MRI with advanced pharmacokinetic models and presents a method for estimation of the AIF based on blind deconvolution. METHODS: A parametric AIF model designed for small animal physiology and use of advanced pharmacokinetic models is proposed. The parameters of the AIF are estimated using multichannel blind deconvolution. RESULTS: Evaluation on simulated data show that for realistic signal to noise ratios blind deconvolution AIF estimation leads to comparable results as the use of the true AIF. Evaluation on real data based on DCE-MRI with two contrast agents of different molecular weights showed a consistence with the known effects of the molecular weight. CONCLUSION: Multi-channel blind deconvolution using the proposed AIF model specific for small animal DCE-MRI provides reliable perfusion parameter estimates under realistic signal to noise conditions.

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.

Peripubertal cannabidiol treatment rescues behavioral and neurochemical abnormalities in the MAM model of schizophrenia.

In agreement with the neurodevelopmental hypothesis of schizophrenia, prenatal exposure of rats to the antimitotic agent methylazoxymethanol acetate (MAM) at gestational day 17 produced long-lasting behavioral alterations such as social withdrawal and cognitive impairment in the social interaction test and in the novel object recognition test, respectively. At the molecular level, an increased cannabinoid receptor type-1 (CB1) mRNA and protein expression, which might be due to reduction in DNA methylation at the gene promoter in the prefrontal cortex (PFC), coincided with deficits in the social interaction test and in the novel object recognition test in MAM rats. Both the schizophrenia-like phenotype and altered transcriptional regulation of CB1 receptors were reversed by peripubertal treatment (from PND 19 to PND 39) with the non-psychotropic phytocannabinoid cannabidiol (30 mg/kg/day), or, in part, by treatment with the cannabinoid CB1 receptor antagonist/inverse agonist AM251 (0.5 mg/kg/day), but not with haloperidol (0.6 mg/kg/day). These results suggest that early treatment with cannabidiol may prevent both the appearance of schizophrenia-like deficits as well as CB1 alterations in the PFC at adulthood, supporting that peripubertal cannabidiol treatment might be protective against MAM insult.

Influence of k-space trajectory corrections on proton density mapping with ultrashort echo time imaging: Application for imaging of short T2 components in white matter.

PURPOSE: To evaluate the impact of MR gradient system imperfections and limitations for the quantitative mapping of short T2* signals performed by ultrashort echo time (UTE) acquisition approach. MATERIALS AND METHODS: The measurement of short T2* signals from a phantom and a healthy volunteer study (8 subjects of average age 28 ±â€¯4 years) were performed on a 3T scanner. The characteristics of the gradient system were obtained using calibration method performed directly on the measured subject or phantom. This information was used to calculate the actual sampling trajectory with the help of a parametric eddy current model. The actual sample positions were used to reconstruct corrected images and compared with uncorrected data. RESULTS: Comparison of both approaches, i.e., without and with correction of k-space sampling trajectories revealed substantial improvement when correction was applied. The phantom experiments demonstrate substantial in-plane signal intensity variations for uncorrected sampling trajectories. In the case of the volunteer study, this led to significant differences in relative proton density (RPD) estimation between the uncorrected and corrected data (P = 0.0117 by Wilcoxon matched-pairs test) and provides for about ~15% higher values for short T2* components of white matter (WM) in the case of uncorrected images. CONCLUSION: The imperfection of the applied gradients could induce errors in k-space data sampling which further propagates into the fidelity of the UTE images and jeopardizes precision of quantification. However, the study proved that measurement of gradient errors together with correction of sample positions can contribute to increased accuracy and unbiased characterization of short T2* signals.

Poly(I:C) model of schizophrenia in rats induces sex-dependent functional brain changes detected by MRI that are not reversed by aripiprazole treatment.

BACKGROUND AND PURPOSE: One of the hallmarks of schizophrenia is altered brain structure, potentially due to antipsychotic treatment, the disorder itself or both. It was proposed that functional changes may precede the structural ones. In order to understand and potentially prevent this unwanted process, brain function assessment should be validated as a diagnostic tool. METHODS: We used Arterial Spin Labelling MRI technique for the evaluation of brain perfusion in several brain regions in a neurodevelopmental poly(I:C) model of schizophrenia (8mg/kg on a gestational day 15) in rats taking into account sex-dependent effects and chronic treatment with aripiprazole (30days), an atypical antipsychotic acting as a partial agonist on dopaminergic receptors. RESULTS: We found the sex of the animal to have a highly significant effect in all regions of interest, with females showing lower blood perfusion than males. However, both males and females treated prenatally with poly(I:C) showed enlargement of the lateral ventricles. Furthermore, we detected increased perfusion in the circle of Willis, hippocampus, and sensorimotor cortex, which was not influenced by chronic atypical antipsychotic aripiprazole treatment in male poly(I:C) rats. CONCLUSION: We hypothesize that perfusion alterations may be caused by the hyperdopaminergic activity in the poly(I:C) model, and the absence of aripiprazole effect on perfusion in brain regions related to schizophrenia may be due to its partial agonistic mechanism.