Publications

Transverse relaxation in MRI is modulated by magnetic field variations arising from tissue microstructure, offering a potential window into the underlying chemical composition and structural organization at the cellular scale. However, the transverse relaxation rate in white matter depends on both echo time and the orientation of axons relative to the external field. Such anisotropy complicates the interpretation of transverse relaxation in general and as a biomarker for neurodegenerative disease. Understanding this anisotropy is therefore crucial for accurately analyzing MRI signals. While previous modeling studies have investigated these effects, they often relied on simplified or idealized tissue geometries. In this study, we investigate magnetic field variance and intra-axonal transverse relaxation using realistic axonal microstructure extracted from 3D electron microscopy, incorporating myelinated axons with embedded spherical susceptibility sources. We derive how transverse relaxation depends on the angle between axons and the external magnetic field. Simulations show that the time-dependent power-law signature arising from white matter structural disorder is weak and may be difficult to detect at currently achievable noise levels, echo times, and field strengths. This is because the power-law curvature over a typical range of echo times deviates only slightly from a linear trend. Our findings highlight the influence of axonal geometry on intra-axonal transverse relaxation and suggest that accounting for both time and orientation dependence may facilitate the development of more precise neuroimaging biomarkers.

Background Network reorganization following ischemic stroke is thought to play a role in recovery. Although cortico-cortical reorganization is widely established, changes in interhemispheric striatal connections following ischemia remain poorly understood, even when stroke occurs in motor areas. Given the importance of the striatum to motor function, we investigated network-level striatal coupling in stroke using ultrafast resting-state fMRI, which has recently been shown to facilitate the dissection of synchronous oscillatory activity better than its conventional 1 sec time-resolution counterparts.Methods A cohort of (N=18) sedated rats were randomized and N=9 rats underwent unilateral photothrombotic ischemic lesioning in motor cortex. One week after the lesion, when plasticity and recovery are well established, all animals were scanned on a 9.4T MRI scanner using a cryogenic coil using an ultrafast resting-state functional MRI sequence with temporal resolution of 90 ms. Data were collected for 24 minutes, and spectral power, phase locking, and functional connectivity were quantified. Histology was performed to confirm lesion extent.Results While cortico-cortical power, connectivity and synchrony were diminished one week post-stroke as expected, we surprisingly found increased striato-striatal power, synchrony and functional connectivity in the stroked group compared with the control group. In stroked animals, the spectral power in the ultraslow oscillation frequency band (0.02-0.4 Hz) significantly increased in the striatum while decreasing in the cortex. When data were undersampled to textquotedblleftconventionaltextquotedblright fMRI temporal resolution (900 ms), the striatal effects were lost, revealing the power of ultrafast fMRI approaches in unveiling such phenomena.Conclusions Increased striato-striatal coupling, in the form of increased synchrony, spectral power, and functional connectivity, was revealed by ultrafast resting-state fMRI, but not conventional temporal resolution resting-state fMRI. Our findings suggest more involvement of subcortical areas in network reorganization than previously thought.Competing Interest StatementNS serves on the Scientific Advisory Board of Bruker.

Purpose: This study aims to determine the feasibility, image quality, intra-subject repeatability and inter-reader variability of Diffusion tensor (DTI) and Diffusion kurtosis imaging (DKI) for pancreatic imaging using different protocols and report normative values in healthy individuals. Methods: Single-institution prospective study performed on healthy volunteers in a clinical 3T scanner, using two different protocols (6/16 diffusion directions). Acquisitions were repeated twice to assess intra-subject repeatability. To assess inter-reader variability, Mean diffusivity (MD), Axial diffusivity (AD), Radial diffusivity (RD), Apparent diffusion coefficient (ADC) and Mean kurtosis (MK) values were extracted from segmented pancreas by two radiologists. A Likert scale was used by both readers to assess subjective image quality. Results: Twelve healthy volunteers were recruited for each MRI protocol. The 6 diffusion directions protocol was shorter: 7 min vs. 14 min (corresponding to 4 min vs. 7.5 min for a DTI only reconstruction). No differences in image quality were found between protocols. Only MK maps showed implausible estimates, leading to the exclusion of median 16% and 17.7% pixels for the 6- and 16-direction protocols, respectively. Intra-subject repeatability was determined with negligible coefficients of repeatability for DTI; however, MK presented slightly higher values. Inter-reader agreement was excellent for all maps (ICC > 0.9). Conclusions: DTI and DKI of the pancreas are feasible in clinical settings, with excellent inter-observer agreement and good image quality. Intra-subject repeatability is excellent for DTI, but some variability was observed with DKI. A 6-directions protocol may be preferred due to faster acquisition without quantitatively compromising estimates. MK inaccuracies prompt further research for improving artifact correction.

Functional Magnetic Resonance Imaging (fMRI) in rodents is pivotal for understanding the mechanisms underlying Blood Oxygen Level-Dependent (BOLD) signals and phenotyping animal models of disorders, among other applications. Despite its growing use, comparing rodent fMRI results across different research sites remains challenging due to variations in experimental protocols. Here, we aggregated and analyzed 22 sensory-evoked rat fMRI datasets from 12 imaging centers, totaling scans from 220 rats, to get a snapshot of the current acquisitions in the field. This retrospective analysis highlights common practices and parameters to inform future cross-laboratory standardization efforts. We applied a standardized preprocessing pipeline and evaluated the impact of different hemodynamic response function models on group- and individual-level activity patterns. Our analysis revealed inter-dataset variability attributed to differences in experimental design, anesthesia protocols, and imaging parameters. We identified robust activation clusters in all (22/22) datasets. The comparison between stock human models implemented in software and rat-specific models showed significant variations in the resulting statistical maps. Our findings emphasize the necessity for standardized protocols and collaborative efforts to improve the reproducibility and reliability of rodent fMRI studies. We provide open access to all datasets and analysis code to foster transparency and further research in the field.

The role of subcortical structures in binaural integration is of great interest for auditory processing. The inferior colliculus (IC) is the main auditory midbrain center where ascending and descending auditory projections converge, which was suggested to encode auditory information via a pushpull mechanism (a coordinated antagonistic neural mechanism for adaptive response control) between the two ICs. However, the origin of this pushpull mechanism in the brain and how it interacts with other upstream/downstream subcortical areas are still a matter of great debate. Here, we harness functional MRI (fMRI) in combination with IC lesions in the rat to dissect the pushpull interaction from a pathway-wide perspective. We find evidence for the pushpull mechanism in IC through opposing negative/positive fMRI signals in the ipsilateral/contralateral ICs upon monaural stimulation. By unilaterally lesioning the corresponding contralateral IC, we demonstrate the necessity of collicular integrity and intercollicular interactions for the pushpull interaction. Using binaural stimulation and IC lesions, we show that the pushpull interaction is exerted also in binaural processing. Finally, we demonstrate that, at least at the population level revealed by fMRI, the main pushpull interactions occur first at the IC level, and not earlier, and that the outcome of the pushpull \u201ccalculation\u201d is relayed downstream to the medial geniculate body (MGB). This dissection of the pushpull interaction sheds light into subcortical auditory function.

Parkinson's disease (PD) is a complex progressive neurodegenerative disorder involving hallmarks such as α[jls-end-space/]-Synuclein ( α[jls-end-space/]Syn) aggregation and dopaminergic dysfunction that affect brain-wide neural activity. Although movement disorders are prominent in PD, sensory impairments also occur relatively early on, mainly in olfactory and, to a lesser extent visual systems. While these deficits have been described mainly at the behavioral and molecular levels, the underlying network-level activity remains poorly understood. Here, we harnessed a human α[jls-end-space/]Syn transgenic mouse model of PD with in vivo functional MRI (fMRI) to map evoked activity in the visual and olfactory pathways, along with pseudo-Continuous Arterial Spin Labeling (pCASL) and c-FOS measurements to disentangle vascular from neuronal effects. Upon stimulation with either odors or flickering lights, we found significant decreases in fMRI responses along both olfactory and visual pathways, in multiple cortical and subcortical sensory areas. Average Cerebral Blood Flow rates were decreased by ∼10% in the α[jls-end-space/]Syn group, while c-FOS levels were reduced by over 50%, suggesting a strong neural driver for the dysfunction, along with more modest vascular contributions. Our study provides insight into brain-level activity in an α[jls-end-space/]Syn-based model, and suggests a novel target for biomarking via quantification of simple sensory evoked responses.

Objectives Detecting premalignant lesions for pancreatic ductal adenocarcinoma, mainly pancreatic intraepithelial neoplasia (PanIN), is critical for early diagnosis and for understanding PanIN biology. Based on PanIN's histology, we hypothesized that diffusion tensor imaging (DTI) and T2∗ could detect PanIN. Materials and Methods DTI was explored for the detection and characterization of PanIN in genetically engineered mice (KC, KPC). Following in vivo DTI, ex vivo ultrahigh-field (16.4 T) MR microscopy using DTI, T2∗ was performed with histological validation. Sources of MR contrasts and histological features were investigated, including histological scoring for disease burden (lesion span) and severity (adjusted score). To test if findings in mice can be translated to humans, human pancreas specimens were imaged. Results DTI detected PanIN and pancreatic ductal adenocarcinoma in vivo (6 KPC, 4 KC, 6 controls) with high discriminative ability: fractional anisotropy (FA) and radial diffusivity with area under the curve = 0.983 (95% confidence interval: 0.932-1.000); mean diffusivity and axial diffusivity (AD) with area under the curve = 1 (95% confidence interval: 1.000-1.000). MR microscopy with histological correlation (20 KC/KPC; 5 controls) revealed that sources of MR contrasts likely arise from microarchitectural signatures: high FA, AD in fibrotic areas surrounding lesions, high diffusivities within cysts, and high T2∗ within lesions' stroma. The strongest histological correlations for lesion span and adjusted score were obtained with AD (R = 0.708, P < 0.001; R = 0.789, P < 0.001, respectively). Ex vivo observations in 5 human pancreases matched our findings in mice, revealing substantial contrast between PanIN and normal pancreas. Conclusions DTI and T2∗ are useful for detecting and characterizing PanIN in genetically engineered mice and in the human pancreas, especially with AD and FA. These are encouraging findings for future clinical applications of pancreatic imaging.

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents \u201cPart 2\u201d of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

Driven by a period of accelerated progress and recent technical breakthroughs, whole-brain functional neuroimaging in rodents offers exciting new possibilities for addressing basic questions about brain function and its alterations. In response to lessons learned from the human neuroimaging community, leading scientists and researchers in the field convened to address existing barriers and outline ambitious goals for the future. This article captures these discussions, highlighting a shared vision to advance rodent functional neuroimaging into an era of increased impact.

Visual perception can operate in two distinct vision modesstatic and dynamicthat have been associated with different neural activity regimes in the superior colliculus (SC). However, the associated pathway-wide mechanisms remain poorly understood, especially in terms of corticotectal and tectotectal feedback upon encoding the continuity illusion during the dynamic vision mode. Here, we harness functional MRI combined with rat brain lesions to investigate whole-pathway neural interactions in the dynamic vision mode. We find a push-pull mechanism embodying contralateral suppression of SC activity opposing positive ipsilateral neural activation upon monocular visual stimulation. Cortical amplification is confirmed through cortical lesions, while further lesioning the ipsilateral SC leads to a boost in the contralateral SC negative signals, suggesting a tectal origin for the push-pull interaction. These results highlight hitherto unreported frequency-dependent modulations in the tectotectal pathway and further challenge the notion that intertectal connections solely serve as reciprocal inhibitory mechanisms for avoiding visual blur during saccades.

Deciphering the directionality of information flow in cortical circuits is essential for understanding brain dynamics, learning, and neuroplasticity after injury. However, current non-invasive methods cannot distinguish feedforward (FF) from feedback (FB) signals across entire networks, including deep brain regions. Here, we present a novel approach UltraFast Layer-Resolved Encoding (uFLARE) that develops ultrahigh spatiotemporal resolution fMRI and a Layer-based Connective Field (lCF) model to disentangle FF from FB signaling. Our findings reveal that lCF size, an indicator of information integration, differentiates FF and FB activity through distinct layer-specific connectivity patterns during spontaneous activity, challenging the notion that FF signals are solely stimulus-driven. FF connectivity follows an inverted U-shape, peaking in layer IV, while FB exhibits a U-shaped pattern, with peaks in layers I and VI. These profiles generalize across sensory pathways (visual, somatosensory, and motor) and reveal injury-induced network reorganization, such as LGN bypassing V1 to provide direct FF input to higher visual areas.

Glioblastomas are aggressive brain tumors with dismal prognosis. One of the main bottlenecks for developing more effective therapies for glioblastoma stems from their histologic and molecular heterogeneity, leading to distinct tumor microenvironments and disease phenotypes. Effectively characterizing these features would improve the clinical management of glioblastoma. Glucose flux rates through glycolysis and mitochondrial oxidation have been recently shown to quantitatively depict glioblastoma proliferation in mouse models (GL261 and CT2A tumors) using dynamic glucose-enhanced (DGE) deuterium spectroscopy. However, the spatial features of tumor microenvironment phenotypes remain hitherto unresolved. Here, we develop a DGE Deuterium Metabolic Imaging (DMI) approach for profiling tumor microenvironments through glucose conversion kinetics. Using a multimodal combination of tumor mouse models, novel strategies for spectroscopic imaging and noise attenuation, and histopathological correlations, we show that tumor lactate turnover mirrors phenotype differences between GL261 and CT2A mouse glioblastoma, whereas recycling of the peritumoral glutamate-glutamine pool is a potential marker of invasion capacity in pooled cohorts, linked to secondary brain lesions. These findings were validated by histopathological characterization of each tumor, including cell density and proliferation, peritumoral invasion and distant migration, and immune cell infiltration. Our study bodes well for precision neuro-oncology, highlighting the importance of mapping glucose flux rates to better understand the metabolic heterogeneity of glioblastoma and its links to disease phenotypes.

Deciphering the directionality of information flow in cortical circuits is essential for understanding brain dynamics, learning, and neuroplasticity after injury. However, current non-invasive methods cannot distinguish feedforward (FF) from feedback (FB) signals across entire networks, including deep brain regions. Here, we present a novel approach combining ultrafast fMRI with a Layer-based Connective Field (lCF) model to disentangle FF from FB signaling. Our findings reveal that lCF size, an indicator of spatial information integration, differentiates FF and FB activity through distinct layer-specific connectivity patterns during spontaneous activity, challenging the notion that FF signals are solely stimulus-driven. FF connectivity follows an inverted U-shape, peaking in layer IV, while FB exhibits a U-shaped pattern, with peaks in layers I and VI. These profiles generalize across sensory pathways (visual, somatosensory, and motor) and reveal injury-induced network reorganization, such as LGN bypassing V1 to provide direct FF input to higher visual areas.Competing Interest StatementThe authors have declared no competing interest.

Adequate perfusion is critical for maintaining normal brain function and aberrations thereof are hallmarks of many diseases. Pseudo-Continuous Arterial Spin Labeling (pCASL) MRI enables noninvasive quantitative perfusion mapping without contrast agent injection and with a higher signal-to-noise ratio (SNR) than alternative methods. Despite its great potential, pCASL remains challenging, unstable, and relatively low-resolution in rodents especially in mice thereby limiting the investigation of perfusion properties in many transgenic or other relevant rodent models of disease. Here, we address this gap by developing a novel experimental setup for high-resolution pCASL imaging in mice and combining it with the enhanced SNR of cryogenic probes. We show that our new experimental setup allows for optimal positioning of the carotids within the cryogenic coil, rendering labeling reproducible. With the proposed methodology, we managed to increase the spatial resolution of pCASL perfusion images by a factor of 15 in mice; a factor of 6 in rats is gained compared to the state of the art just by virtue of the cryogenic coil. We also show that the improved pCASL perfusion imaging allows much better delineation of specific brain areas, both in healthy animals as well as in rat and mouse models of stroke. Our results bode well for future high-definition pCASL perfusion imaging in rodents.

Background: Choroid plexus (CP) or blood-cerebrospinal fluid-barrier (BCSFB) is a unique functional tissue which lines the brains fluid-filled ventricles, with a crucial role in CSF production and clearance. BCSFB dysfunction is thought to contribute to toxic protein build-up in neurodegenerative disorders, including Alzheimers disease (AD). However, the dynamics of this process remain unknown, mainly due to the paucity of in-vivo methods for assessing CP function. Methods: We harness recent developments in Arterial Spin Labelling MRI to measure water delivery across the BCSFB as a proxy for CP function, as well as cerebral blood flow (CBF), at different stages of AD in the widely used triple transgenic mouse model (3xTg), with ages between 8 and 32 weeks. We further compared the MRI results with Y-maze behaviour testing, and histologically validated the expected pathological changes, which recapitulate both amyloid and tau deposition. Results: Total BCSFB-mediated water delivery is significantly higher in 3xTg mice (> 50%) from 8 weeks (preclinical stage), an increase which is not explained by differences in ventricular volumes, while tissue parameters such as CBF and T1 are not different between groups at all ages. Behaviour differences between the groups were observed starting at 20 weeks, especially in terms of locomotion, with 3xTg animals showing a significantly smaller number of arm entries in the Y-maze. Conclusions: Our work strongly suggests the involvement of CP in the early stages of AD, before the onset of symptoms and behavioural changes, providing a potential biomarker of pathology.

Magnetic susceptibility imaging may provide valuable information about chemical composition and microstructural organization of tissue. However, its estimation from the MRI signal phase is particularly difficult as it is sensitive to magnetic tissue properties ranging from the molecular to the macroscopic scale. The MRI Larmor frequency shift measured in white matter (WM) tissue depends on the myelinated axons and other magnetizable sources such as iron-filled ferritin. We have previously derived the Larmor frequency shift arising from a dense medium of cylinders with scalar susceptibility and arbitrary orientation dispersion. Here, we extend our model to include microscopic WM susceptibility anisotropy as well as spherical inclusions with scalar susceptibility to represent subcellular structures, biologically stored iron, and so forth. We validate our analytical results with computer simulations and investigate the feasibility of estimating susceptibility using simple iterative linear least squares without regularization or preconditioning. This is done in a digital brain phantom synthesized from diffusion MRI measurements of an ex vivo mouse brain at ultra-high field.

The visual continuity illusion involves a shift in visual perception from static to dynamic vision modes when the stimuli arrive at high temporal frequency, and is critical for recognizing objects moving in the environment. However, how this illusion is encoded across the visual pathway remains poorly understood, with disparate frequency thresholds at retinal, cortical, and behavioural levels suggesting the involvement of other brain areas. Here, we employ a multimodal approach encompassing behaviour, whole-brain functional MRI, and electrophysiological measurements, for investigating the encoding of the continuity illusion in rats. Behavioural experiments report a frequency threshold of 18±2 Hz. Functional MRI reveal that superior colliculus signals transition from positive to negative at the behaviourally-driven threshold, unlike thalamic and cortical areas. Electrophysiological recordings indicate that these transitions are underpinned by neural activation/suppression. Lesions in the primary visual cortex reveal this effect to be intrinsic to the superior colliculus (under a cortical gain effect). Our findings highlight the superior colliculus crucial involvement in encoding temporal frequency shifts, especially the change from static to dynamic vision modes.

Purpose: To extend quantitative susceptibility mapping to account for microstructure of white matter (WM) and demonstrate its effect on ex vivo mouse brain at 16.4T. Theory and Methods: Previous studies have shown that the MRI measured Larmor frequency also depends on local magnetic microstructure at the mesoscopic scale. Here, we include effects from WM microstructure using our previous results for the mesoscopic Larmor frequency (Formula presented.) of cylinders with arbitrary orientations. We scrutinize the validity of our model and QSM in a digital brain phantom including (Formula presented.) from a WM susceptibility tensor and biologically stored iron with scalar susceptibility. We also apply susceptibility tensor imaging to the phantom and investigate how the fitted tensors are biased from (Formula presented.). Last, we demonstrate how to combine multi-gradient echo and diffusion MRI images of ex vivo mouse brains acquired at 16.4T to estimate an apparent scalar susceptibility without sample rotations. Results: Our new model improves susceptibility estimation compared to QSM for the brain phantom. Applying susceptibility tensor imaging to the phantom with (Formula presented.) from WM axons with scalar susceptibility produces a highly anisotropic susceptibility tensor that mimics results from previous susceptibility tensor imaging studies. For the ex vivo mouse brain we find the (Formula presented.) due to WM microstructure to be substantial, changing susceptibility in WM up to 25% root-mean-squared-difference. Conclusion: (Formula presented.) impacts susceptibility estimates and biases susceptibility tensor imaging fitting substantially. Hence, it should not be neglected when imaging structurally anisotropic tissue such as brain WM.

Marčenko-Pastur PCA (MPPCA) denoising is emerging as an effective means for noise suppression in MR imaging (MRI) acquisitions with redundant dimensions. However, MPPCA performance can be severely compromised by spatially correlated noisean issue typically affecting most modern MRI acquisitionsalmost to the point of returning the original images with little or no noise removal. In this study, we explore different threshold criteria for principal component analysis (PCA) component classification that enable efficient and robust denoising of MRI data even when noise exhibits high spatial correlations, especially in cases where data are acquired with Partial Fourier and when only magnitude data are available. We show that efficient denoising can be achieved by incorporating a-priori information about the noise variance into PCA denoising thresholding. Based on this, two denoising strategies developed here are: 1) General PCA (GPCA) denoising that uses a-priori noise variance estimates without assuming specific noise distributions; and 2) Threshold PCA (TPCA) denoising which removes noise components with a threshold computed from a-priori estimated noise variance to determine the upper bound of the Marčenko-Pastur (MP) distribution. These strategies were tested in simulations with known ground truth and applied for denoising diffusion MRI data acquired using pre-clinical (16.4T) and clinical (3T) MRI scanners. In synthetic phantoms, MPPCA denoising failed to denoise spatially correlated data, while GPCA and TPCA better classified components as dominated by signal/noise. In cases where the noise variance was not accurately estimated (as can be the case in many practical scenarios), TPCA still provides excellent denoising performance. Our experiments in pre-clinical diffusion data with highly corrupted by spatial correlated noise revealed that both GPCA and TPCA robustly denoised the data while MPPCA denoising failed. In in vivo diffusion MRI data acquired on a clinical scanner in healthy subjects, MPPCA weakly removed noised, while TPCA was found to have the best performance, likely due to misestimations of the noise variance. Thus, our work shows that these novel denoising approaches can strongly benefit future pre-clinical and clinical MRI applications.

Understanding the dynamics of stability/plasticity balances during adulthood is pivotal for learning, disease, and recovery from injury. However, the brain-wide topography of sensory remapping remains unknown. Here, using a first-of-its-kind setup for delivering patterned visual stimuli in a rodent magnetic resonance imaging (MRI) scanner, coupled with biologically inspired computational models, we noninvasively mapped brain-wide propertiesreceptive fields (RFs) and spatial frequency (SF) tuning curvesthat were insofar only available from invasive electrophysiology or optical imaging. We then tracked the RF dynamics in the chronic visual deprivation model (VDM) of plasticity and found that light exposure progressively promoted a large-scale topographic remapping in adult rats. Upon light exposure, the initially unspecialized visual pathway progressively evidenced sharpened RFs (smaller and more spatially selective) and enhanced SF tuning curves. Our findings reveal that visual experience following VDM reshapes both structure and function of the visual system and shifts the stability/plasticity balance in adults.

Purpose: Estimating magnetic susceptibility using MRI depends on inverting a forward relationship between the susceptibility and measured Larmor frequency. However, an often-overlooked constraint in susceptibility fitting is that the Larmor frequency is only measured inside the sample, and after successful background field removal, susceptibility sources should only reside inside the same sample. Here, we test the impact of accounting for these constraints in susceptibility fitting. Theory and Methods: Two different digital brain phantoms with scalar susceptibility were examined. We used the MEDI phantom, a simple phantom with no background fields, to examine the effect of the imposed constraints for various levels of SNR. Next, we considered the QSM reconstruction challenge 2.0 phantom with and without background fields. We estimated the parameter accuracy of openly-available QSM algorithms by comparing fitting results to the ground truth. Next, we implemented the mentioned constraints and compared to the standard approach. Results: Including the spatial distribution of frequencies and susceptibility sources decreased the RMS-error compared to standard QSM on both brain phantoms when background fields were absent. When background field removal was unsuccessful, as is presumably the case in most in vivo conditions, it is better to allow sources outside the brain. Conclusion: Informing QSM algorithms about the location of susceptibility sources and where Larmor frequency was measured improves susceptibility fitting for realistic SNR levels and efficient background field removal. However, the latter remains the bottleneck of the algorithm. Allowing for external sources regularizes unsuccessful background field removal and is currently the best strategy in vivo.

MP-PCA denoising has become the method of choice for denoising MRI data since it provides an objective threshold to separate the signal components from unwanted thermal noise components. In rodents, thermal noise in the coils is an important source of noise that can reduce the accuracy of activation mapping in fMRI. Further confounding this problem, vendor data often contains zero-filling and other post-processing steps that may violate MP-PCA assumptions. Here, we develop an approach to denoise vendor data and assess activation \u201cspreading\u201d caused by MP-PCA denoising in rodent task-based fMRI data. Data was obtained from N = 3 mice using conventional multislice and ultrafast fMRI acquisitions (1 s and 50 ms temporal resolution, respectively), using a visual stimulation paradigm. MP-PCA denoising produced SNR gains of 64% and 39%, and Fourier Spectral Amplitude (FSA) increases in BOLD maps of 9% and 7% for multislice and ultrafast data, respectively, when using a small [2 2] denoising window. Larger windows provided higher SNR and FSA gains with increased spatial extent of activation that may or may not represent real activation. Simulations showed that MP-PCA denoising can incur activation \u201cspreading\u201d with increased false positive rate and smoother functional maps due to local \u201cbleeding\u201d of principal components, and that the optimal denoising window for improved specificity of functional mapping, based on Dice score calculations, depends on the data's tSNR and functional CNR. This \u201cspreading\u201d effect applies also to another recently proposed low-rank denoising method (NORDIC), although to a lesser degree. Our results bode well for enhancing spatial and/or temporal resolution in future fMRI work, while taking into account the sensitivity/specificity trade-offs of low-rank denoising methods.

Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general.

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

The magnetic susceptibility of tissue can provide valuable information about its chemical composition and microstructural organization. However, the relation between the magnetic microstructure and the measurable Larmor frequency shift is understood only for a few idealized cases. Here we analyze the microstructure formed by magnetized, NMR-invisible infinite cylinders suspended in an NMR-reporting fluid. Through simulations, we scrutinize various geometries of mesoscopic Lorentz cavities and inclusions, and show that the cavity size should be approximately one order of magnitude larger than the width of the inclusions. We also analytically derive the Larmor frequency shift for a population of cylinders with arbitrary orientation dispersion and show that it is determined by the (Formula presented.) Laplace expansion coefficients (Formula presented.) of the cylinders' orientation distribution function. Our work underscores the need to account for microstructural organization when estimating magnetic tissue properties.

Purpose: To develop a denoising strategy leveraging redundancy in high-dimensional data. Theory and Methods: The SNR fundamentally limits the information accessible by MRI. This limitation has been addressed by a host of denoising techniques, recently including the so-called MPPCA: principal component analysis of the signal followed by automated rank estimation, exploiting the Marchenko-Pastur distribution of noise singular values. Operating on matrices comprised of data patches, this popular approach objectively identifies noise components and, ideally, allows noise to be removed without introducing artifacts such as image blurring, or nonlocal averaging. The MPPCA rank estimation, however, relies on a large number of noise singular values relative to the number of signal components to avoid such ill effects. This condition is unlikely to be met when data patches and therefore matrices are small, for example due to spatially varying noise. Here, we introduce tensor MPPCA (tMPPCA) for the purpose of denoising multidimensional data, such as from multicontrast acquisitions. Rather than combining dimensions in matrices, tMPPCA uses each dimension of the multidimensional data's inherent tensor-structure to better characterize noise, and to recursively estimate signal components. Results: Relative to matrix-based MPPCA, tMPPCA requires no additional assumptions, and comparing the two in a numerical phantom and a multi-TE diffusion MRI data set, tMPPCA dramatically improves denoising performance. This is particularly true for small data patches, suggesting that tMPPCA can be especially beneficial in such cases. Conclusions: The MPPCA denoising technique can be extended to high-dimensional data with improved performance for smaller patch sizes.

Spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals correlate across distant brain areas, shaping functionally relevant intrinsic networks. However, the generative mechanism of fMRI signal correlations, and in particular the link with locally-detected ultra-slow oscillations, are not fully understood. To investigate this link, we record ultrafast ultrahigh field fMRI signals (9.4 Tesla, temporal resolution = 38 milliseconds) from female rats across three anesthesia conditions. Power at frequencies extending up to 0.3 Hz is detected consistently across rat brains and is modulated by anesthesia level. Principal component analysis reveals a repertoire of modes, in which transient oscillations organize with fixed phase relationships across distinct cortical and subcortical structures. Oscillatory modes are found to vary between conditions, resonating at faster frequencies under medetomidine sedation and reducing both in number, frequency, and duration with the addition of isoflurane. Peaking in power within clear anatomical boundaries, these oscillatory modes point to an emergent systemic property. This work provides additional insight into the origin of oscillations detected in fMRI and the organizing principles underpinning spontaneous long-range functional connectivity.

Multiple myeloma (MM), the third most frequent hematological cancer worldwide, is characterized by the proliferation of neoplastic plasma cells in the bone marrow (BM). One of the hallmarks of MM is a permissive BM microenvironment. Increasing evidence suggests that cell-to-cell communication between myeloma and immune cells via tumor cell-derived extracellular vesicles (EV) plays a key role in the pathogenesis of MM. Hence, we aimed to explore BM immune alterations induced by MM-derived EV. For this, we inoculated immunocompetent BALB/cByJ mice with a myeloma cell line, MOPC315.BM, inducing a MM phenotype. Upon tumor establishment, characterization of the BM microenvironment revealed the expression of both activation and suppressive markers by lymphocytes, such as granzyme B and PD-1, respectively. In addition, conditioning of the animals with MOPC315.BM-derived EV, before transplantation of the MOPC315.BM tumor cells, did not anticipate the disease phenotype. However, it induced features of suppression in the BM milieu, such as an increase in PD-1 expression by CD4+ T cells. Overall, our findings reveal the involvement of MOPC315.BM-derived EV protein content as promoters of immune niche remodeling, strengthening the importance of assessing the mechanisms by which MM may impact the immune microenvironment.

Characterizing neural tissue microstructure is a critical goal for future neuroimaging. Diffusion MRI (dMRI) provides contrasts that reflect diffusing spins interactions with myriad microstructural features of biological systems. However, the specificity of dMRI remains limited due to the ambiguity of its signals vis-à-vis the underlying microstructure. To improve specificity, biophysical models of white matter (WM) typically express dMRI signals according to the Standard Model (SM) and have more recently in gray matter (GM) taken spherical compartments into account (the SANDI model) in attempts to represent cell soma. The validity of the assumptions underlying these models, however, remains largely undetermined, especially in GM. To validate these assumptions experimentally, observing their unique, functional properties, such as the b^−1/2 power-law associated with one-dimensional diffusion, has emerged as a fruitful strategy. The absence of this signature in GM, in turn, has been explained by neurite water exchange, non-linear morphology, and/or by obscuring soma signal contributions. Here, we present diffusion simulations in realistic neurons demonstrating that curvature and branching does not destroy the stick power-law behavior in impermeable neurites, but also that their signal is drowned by the soma signal under typical experimental conditions. Nevertheless, by studying the GM dMRI signal's behavior as a function of diffusion weighting as well as time, we identify an attainable experimental regime in which the neurite signal dominates. Furthermore, we find that exchange-driven time dependence produces a signal behavior opposite to that which would be expected from restricted diffusion, thereby providing a functional signature that disambiguates the two effects. We present data from dMRI experiments in ex vivo rat brain at ultrahigh field of 16.4T and observe a time dependence that is consistent with substantial exchange but also with a GM stick power-law. The first finding suggests significant water exchange between neurites and the extracellular space while the second suggests a small sub-population of impermeable neurites. To quantify these observations, we harness the Kärger exchange model and incorporate the corresponding signal time dependence in the SM and SANDI models.

Whole-brain radiotherapy (WBRT) is the treatment backbone for many patients with brain metastasis; however, its efficacy in preventing disease progression and the associated toxicity have questioned the clinical impact of this approach and emphasized the need for alternative treatments. Given the limited therapeutic options available for these patients and the poor understanding of the molecular mechanisms underlying the resistance of metastatic lesions to WBRT, we sought to uncover actionable targets and biomarkers that could help to refine patient selection. Through an unbiased analysis of experimental in vivo models of brain metastasis resistant to WBRT, we identified activation of the S100A9RAGENF-κBJunB pathway in brain metastases as a potential mediator of resistance in this organ. Targeting this pathway genetically or pharmacologically was sufficient to revert the WBRT resistance and increase therapeutic benefits in vivo at lower doses of radiation. In patients with primary melanoma, lung or breast adenocarcinoma developing brain metastasis, endogenous S100A9 levels in brain lesions correlated with clinical response to WBRT and underscored the potential of S100A9 levels in the blood as a noninvasive biomarker. Collectively, we provide a molecular framework to personalize WBRT and improve its efficacy through combination with a radiosensitizer that balances therapeutic benefit and toxicity.

Noninvasively detecting and characterizing modulations in cellular scale micro-architecture remains a desideratum for contemporary neuroimaging. Diffusion MRI (dMRI) has become the mainstay methodology for probing microstructure, and, in ischemia, its contrasts have revolutionized stroke management. Diffusion kurtosis imaging (DKI) has been shown to significantly enhance the sensitivity of stroke detection compared to its diffusion tensor imaging (DTI) counterparts. However, the interpretation of DKI remains ambiguous as its contrast may arise from competing kurtosis sources related to the anisotropy of tissue components, diffusivity variance across components, and microscopic kurtosis (e.g., arising from cross-sectional variance, structural disorder, and restriction). Resolving these sources may be fundamental for developing more specific imaging techniques for stroke management, prognosis, and understanding its pathophysiology. In this study, we apply Correlation Tensor MRI (CTI) a double diffusion encoding (DDE) methodology recently introduced for deciphering kurtosis sources based on the unique information captured in DDE's diffusion correlation tensors to investigate the underpinnings of kurtosis measurements in acute ischemic lesions. Simulations for the different kurtosis sources revealed specific signatures for cross-sectional variance (representing neurite beading), edema, and cell swelling. Ex vivo CTI experiments at 16.4 T were then performed in an experimental photothrombotic stroke model 3 h post-stroke (N = 10), and successfully separated anisotropic, isotropic, and microscopic non-Gaussian diffusion sources in the ischemic lesions. Each of these kurtosis sources provided unique contrasts in the stroked area. Particularly, microscopic kurtosis was shown to be a primary \u201cdriver\u201d of total kurtosis upon ischemia; its large increases, coupled with decreases in anisotropic kurtosis, are consistent with the expected elevation in cross-sectional variance, likely linked to beading effects in small objects such as neurites. In vivo experiments at 9.4 T at the same time point (3 h post ischemia, N = 5) demonstrated the stability and relevance of the findings and showed that fixation is not a dominant confounder in our findings. In future studies, the different CTI contrasts may be useful to address current limitations of stroke imaging, e.g., penumbra characterization, distinguishing lesion progression form tissue recovery, and elucidating pathophysiological correlates.

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

Functional magnetic resonance spectroscopy (fMRS) quantifies metabolic variations upon presentation of a stimulus and can therefore provide complementary information compared to activity inferred from functional magnetic resonance imaging (fMRI). Improving the temporal resolution of fMRS can be beneficial to clinical applications where detailed information on metabolism can assist the characterization of brain function in healthy and sick populations as well as for neuroscience applications where information on the nature of the underlying activity could be potentially gained. Furthermore, fMRS with higher temporal resolution could benefit basic studies on animal models of disease and for investigating brain function in general. However, to date, fMRS has been limited to sustained periods of activation which risk adaptation and other undesirable effects. Here, we performed fMRS experiments in the mouse with high temporal resolution (12 s), and show the feasibility of such an approach for reliably quantifying metabolic variations upon activation. We detected metabolic variations in the superior colliculus of mice subjected to visual stimulation delivered in a block paradigm at 9.4 T. A robust modulation of glutamate is observed on the average time course, on the difference spectra and on the concentration distributions during active and recovery periods. A general linear model is used for the statistical analysis, and for exploring the nature of the modulation. Changes in NAAG, PCr and Cr levels were also detected. A control experiment with no stimulation reveals potential metabolic signal \u201cdrifts\u201d that are not correlated with the functional activity, which should be taken into account when analyzing fMRS data in general. Our findings are promising for future applications of fMRS.

Information about tissue on the microscopic and mesoscopic scales can be accessed by modelling diffusion MRI signals, with the aim of extracting microstructure-specific biomarkers. The standard model (SM) of diffusion, currently the most broadly adopted microstructural model, describes diffusion in white matter (WM) tissues by two Gaussian components, one of which has zero radial diffusivity, to represent diffusion in intra- and extra-axonal water, respectively. Here, we reappraise these SM assumptions by collecting comprehensive double diffusion encoded (DDE) MRI data with both linear and planar encodings, which was recently shown to substantially enhance the ability to estimate SM parameters. We find however, that the SM is unable to account for data recorded in fixed rat spinal cord at an ultrahigh field of 16.4 T, suggesting that its underlying assumptions are violated in our experimental data. We offer three model extensions to mitigate this problem: first, we generalize the SM to accommodate finite radii (axons) by releasing the constraint of zero radial diffusivity in the intra-axonal compartment. Second, we include intracompartmental kurtosis to account for non-Gaussian behaviour. Third, we introduce an additional (third) compartment. The ability of these models to account for our experimental data are compared based on parameter feasibility and Bayesian information criterion. Our analysis identifies the three-compartment description as the optimal model. The third compartment exhibits slow diffusion with a minor but non-negligible signal fraction (∼12%). We demonstrate how failure to take the presence of such a compartment into account severely misguides inferences about WM microstructure. Our findings bear significance for microstructural modelling at large and can impact the interpretation of biomarkers extracted from the standard model of diffusion.

Detecting neuroplasticity in global brain circuits in vivo is key for understanding myriad processes such as memory, learning, and recovery from injury. Functional Magnetic Resonance Imaging (fMRI) is instrumental for such in vivo mappings, yet it typically relies on mapping changes in spatial extent of activation or via signal amplitude modulations, whose interpretation can be highly ambiguous. Importantly, a central aspect of neuroplasticity involves modulation of neural activity timing properties. We thus hypothesized that this temporal dimension could serve as a new marker for neuroplasticity. To detect fMRI signals more associated with the underlying neural dynamics, we developed an ultrafast fMRI (ufMRI) approach facilitating high spatiotemporal sensitivity and resolution in distributed neural pathways. When neuroplasticity was induced in the mouse visual pathway via dark rearing, ufMRI indeed mapped temporal modulations in the entire visual pathway. Our findings therefore suggest a new dimension for exploring neuroplasticity in vivo.

In the original article, there was an error in Eq. 1. The correct Eq. 1 should read: (Formula presented.). The author apologizes for this error and states that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Diffusion tensor imaging (DTI) is a well-established technique for mapping brain microstructure and white matter tracts in vivo. High resolution DTI, however, is usually associated with low intrinsic sensitivity and therefore long acquisition times. By increasing sensitivity, high magnetic fields can alleviate these demands, yet high fields are also typically associated with significant susceptibility-induced image distortions. This study explores the potential arising from employing new pulse sequences and emerging hardware at ultrahigh fields, to overcome these limitations. To this end, a 15.2 T MRI instrument equipped with a cryocooled surface transceiver coil was employed, and DTI experiments were compared between SPatiotemporal ENcoding (SPEN), a technique that tolerates well susceptibility-induced image distortions, and double-sampled Spin-Echo Echo-Planar Imaging (SE-EPI) methods. Following optimization, SE-EPI afforded whole brain DTI maps at 135 μm isotropic resolution that possessed higher signal-to-noise ratios (SNRs) than SPEN counterparts. SPEN, however, was a better alternative to SE-EPI when focusing on challenging regions of the mouse brain including the olfactory bulb and the cerebellum. In these instances, the higher robustness of fully refocused SPEN acquisitions coupled to its built-in zooming abilities, provided in vivo DTI maps with 75 μm nominal isotropic spatial resolution. These DTI maps, and in particular the mean diffusion direction (MDD) details, exhibited variations that matched very well the anatomical features known from histological brain Atlases. Using these capabilities, the development of the olfactory bulb (OB) in live mice was followed from week 1 post-partum, until adulthood. The diffusivity of this organ showed a systematic decrease in its overall isotropic value and increase in its fractional anisotropy with age; this maturation was observed for all regions used in the OB's segmentation but was most evident for the lobules' centers, in particular for the granular cell layer. The complexity of the OB neuronal connections also increased during maturation, as evidenced by the growth in directionalities arising in the mean diffusivity direction maps.

Diffusion kurtosis imaging (DKI) is an imaging modality that yields novel disease biomarkers and in combination with nervous tissue modeling, provides access to microstructural parameters. Recently, DKI and subsequent estimation of microstructural model parameters has been used for assessment of tissue changes in neurodegenerative diseases and associated animal models. In this study, mouse spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using DKI in combination with biophysical modeling to study the relationship between microstructural metrics and degree of animal dysfunction. Thirteen spinal cords were extracted from animals with varied grades of disability and scanned in a high-field MRI scanner along with five control specimen. Diffusion weighted data were acquired together with high resolution T₂* images. Diffusion data were fit to estimate diffusion and kurtosis tensors and white matter modeling parameters, which were all used for subsequent statistical analysis using a linear mixed effects model. T₂* images were used to delineate focal demyelination/inflammation. Our results reveal a strong relationship between disability and measured microstructural parameters in normal appearing white matter and gray matter. Relationships between disability and mean of the kurtosis tensor, radial kurtosis, radial diffusivity were similar to what has been found in other hypomyelinating MS models, and in patients. However, the changes in biophysical modeling parameters and in particular in extra-axonal axial diffusivity were clearly different from previous studies employing other animal models of MS. In conclusion, our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes correlating with clinical disability.

Axon caliber plays a crucial role in determining conduction velocity and, consequently, in the timing and synchronization of neural activation. Noninvasive measurement of axon radii could have significant impact on the understanding of healthy and diseased neural processes. Until now, accurate axon radius mapping has eluded in vivo neuroimaging, mainly due to a lack of sensitivity of the MRI signal to micron-sized axons. Here, we show how - when confounding factors such as extra-axonal water and axonal orientation dispersion are eliminated - heavily diffusion-weighted MRI signals become sensitive to axon radii. However, diffusion MRI is only capable of estimating a single metric, the effective radius, representing the entire axon radius distribution within a voxel that emphasizes the larger axons. Our findings, both in rodents and humans, enable noninvasive mapping of critical information on axon radii, as well as resolve the long-standing debate on whether axon radii can be quantified.

Purpose: Multi-exponential relaxometry is a powerful tool for characterizing tissue, but generally requires high image signal-to-noise ratio (SNR). This work evaluates the use of principal-component-analysis (PCA) denoising to mitigate these SNR demands and improve the precision of relaxometry measures. Methods: PCA denoising was evaluated using both simulated and experimental MRI data. Bi-exponential transverse relaxation signals were simulated for a wide range of acquisition and sample parameters, and experimental data were acquired from three excised and fixed mouse brains. In both cases, standard relaxometry analysis was performed on both original and denoised image data, and resulting estimated signal parameters were compared. Results: Denoising reduced the root-mean-square-error of parameters estimated from multi-exponential relaxometry by factors of ≈3×, for typical acquisition and sample parameters. Denoised images and subsequent parameter maps showed little or no signs of spatial artifact or loss of resolution. Conclusion : Experimental studies and simulations demonstrate that PCA denoising of MRI relaxometry data is an effective method of improving parameter precision without sacrificing image resolution. This simple yet important processing step thus paves the way for broader applicability of multi-exponential MRI relaxometry.

Designing novel diffusion-weighted pulse sequences to probe tissue microstructure beyond the conventional Stejskal-Tanner family is currently of broad interest. One such technique, multidimensional diffusion MRI, has been recently proposed to afford model-free decomposition of diffusion signal kurtosis into terms originating from either ensemble variance of isotropic diffusivity or microscopic diffusion anisotropy. This ability rests on the assumption that diffusion can be described as a sum of multiple Gaussian compartments, but this is often not strictly fulfilled. The effects of nongaussian diffusion on single shot isotropic diffusion sequences were first considered in detail by de Swiet and Mitra in 1996. They showed theoretically that anisotropic compartments lead to anisotropic time dependence of the diffusion tensors, which causes the measured isotropic diffusivity to depend on gradient frame orientation. Here we show how such deviations from the multiple Gaussian compartments assumption conflates orientation dispersion with ensemble variance in isotropic diffusivity. Second, we consider additional contributions to the apparent variance in isotropic diffusivity arising due to intracompartmental kurtosis. These will likewise depend on gradient frame orientation. We illustrate the potential importance of these confounds with analytical expressions, numerical simulations in simple model geometries, and microimaging experiments in fixed spinal cord using isotropic diffusion encoding waveforms with 7.5 ms duration and 3000 mT/m maximum amplitude.

Biophysical modelling of diffusion MRI is necessary to provide specific microstructural tissue properties. However, estimating model parameters from data with limited diffusion gradient strength, such as clinical scanners, has proven unreliable due to a shallow optimization landscape. On the other hand, estimation of diffusion kurtosis (DKI) parameters is more robust, and its parameters may be connected to microstructural parameters, given an appropriate biophysical model. However, it was previously shown that this procedure still does not provide sufficient information to uniquely determine all model parameters. In particular, a parameter degeneracy related to the relative magnitude of intra-axonal and extra-axonal diffusivities remains. Here we develop a model of diffusion in white matter including axonal dispersion and demonstrate stable estimation of all model parameters from DKI in fixed pig spinal cord. By employing the recently developed fast axisymmetric DKI, we use stimulated echo acquisition mode to collect data over a two orders of magnitude diffusion time range with very narrow diffusion gradient pulses, enabling finely resolved measurements of diffusion time dependence of both net diffusion and kurtosis metrics, as well as model intra- and extra-axonal diffusivities, and axonal dispersion. Our results demonstrate substantial time dependence of all parameters except volume fractions, and the additional time dimension provides support for intra-axonal diffusivity to be larger than extra-axonal diffusivity in spinal cord white matter, although not unambiguously. We compare our findings for the time-dependent compartmental diffusivities to predictions from effective medium theory with reasonable agreement.

Many developmental processes, such as plasticity and aging, or pathological processes such as neurological diseases are characterized by modulations of specific cellular types and their microstructures. Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) is a powerful technique for probing microstructure, yet its information arises from the ubiquitous, non-specific water signal. By contrast, diffusion-weighted Magnetic Resonance Spectroscopy (DW-MRS) allows specific characterizations of tissues such as brain and muscle in vivo by quantifying the diffusion properties of MR-observable metabolites. Many brain metabolites are predominantly intracellular, and some of them are preferentially localized in specific brain cell populations, e.g., neurons and glia. Given the microstructural sensitivity of diffusion-encoding filters, investigation of metabolite diffusion properties using DW-MRS can thus provide exclusive cell and compartment-specific information. Furthermore, since many models and assumptions are used for quantification of water diffusion, metabolite diffusion may serve to generate a-priori information for model selection in DW-MRI. However, DW-MRS measurements are extremely challenging, from the acquisition to the accurate and correct analysis and quantification stages. In this review, we survey the state-of-the-art methods that have been developed for the robust acquisition, quantification and analysis of DW-MRS data and discuss the potential relevance of DW-MRS for elucidating brain microstructure in vivo. The review highlights that when accurate data on the diffusion of multiple metabolites is combined with accurate computational and geometrical modeling, DW-MRS can provide unique cell-specific information on the intracellular structure of brain tissue, in health and disease, which could serve as incentives for further application in vivo in human research and clinical MRI.

Nuclear magnetic resonance is a powerful tool for probing the structures of chemical and biological systems. Combined with field gradients it leads to NMR imaging (MRI), a widespread tool in non-invasive examinations. Sensitivity usually limits MRI's spatial resolution to tens of micrometers, but other sources of information like those delivered by constrained diffusion processes, enable one extract morphological information down to micron and sub-micron scales. We report here on a new method that also exploits diffusion-isotropic or anisotropic-to sense morphological parameters in the nm-mm range, based on distributions of susceptibility-induced magnetic field gradients. A theoretical framework is developed to define this source of information, leading to the proposition of internal gradient-distribution tensors. Gradient-based spin-echo sequences are designed to measure these new observables. These methods can be used to map orientations even when dealing with unconstrained diffusion, as is here demonstrated with studies of structured systems, including tissues.

Measuring cellular microstructures non-invasively and achieving specificity towards a celltype population within an interrogated in vivo tissue, remains an outstanding challenge in brain research. Magnetic Resonance Spectroscopy (MRS) provides an opportunity to achieve cellular specificity via the spectral resolution of metabolites such as N-Acetylaspartate (NAA) and myo-Inositol (mI), which are considered neuronal and astrocytic markers, respectively. Yet the information typically obtained with MRS describes metabolic concentrations, diffusion coefficients or relaxation rates rather than microstructures. Understanding how these metabolites are compartmentalized is a challenging but important goal, which so far has been mainly addressed using diffusion models. Here, we present direct in vivo evidence for the confinement of NAA and mI within sub-cellular components, namely, the randomly oriented process of neurons and astrocytes, respectively. Our approach applied Relaxation Enhanced MRS at ultrahigh (21.1 T) field, and used its high H-1 sensitivity to measure restricted diffusion correlations for NAA and mI using a Double Diffusion Encoding (DDE) filter. While very low macroscopic anisotropy was revealed by spatially localized Diffusion Tensor Spectroscopy, DDE displayed characteristic amplitude modulations reporting on confinements in otherwise randomly oriented anisotropic microstructures for both metabolites. This implies that for the chosen set of parameters, the DDE measurements had a biased sensitivity towards NAA and mI sited in the more confined environments of neurites and astrocytic branches, than in the cell somata. These measurements thus provide intrinsic diffusivities and compartment diameters, and revealed subcellular neuronal and astrocytic morphologies in normal in vivo rat brains. The relevance of these measurements towards human applications- which could in turn help understand CNS plasticity as well as diagnose brain diseases- is discussed.

White Matter (WM) microstructures, such as axonal density and average diameter, are crucial to the normal function of the Central Nervous System (CNS) as they are closely related with axonal conduction velocities. Conversely, disruptions of these microstructural features may result in severe neurological deficits, suggesting that their noninvasive mapping could be an important step towards diagnosing and following pathophysiology. Whereas diffusion based MRI methods have been proposed to map these features, they typically entail the application of powerful gradients, which are rarely available in the clinic, or extremely long acquisition schemes to extract information from parameter-intensive models. In this study, we suggest that simple and time-efficient multi-gradient-echo (MGE) MRI can be used to extract the axon density from susceptibility-driven non-monotonic decay in the time-dependent signal. We show, both theoretically and with simulations, that a non-monotonic signal decay will occur for multi-compartmental microstructures such as axons and extra-axonal spaces, which were here used as a simple model for the microstructure and that, for axons parallel to the main magnetic field, the axonal density can be extracted. We then experimentally demonstrate in ex-vivo rat spinal cords that its different tracts characterized by different microstructures can be clearly contrasted using the MGE-derived maps. When the quantitative results are compared against ground-truth histology, they reflect the axonal fraction (though with a bias, as evident from Bland-Altman analysis). As well, the extra-axonal fraction can be estimated. The results suggest that our model is oversimplified, yet at the same time evidencing a potential and usefulness of the approach to map underlying microstructures using a simple and time-efficient MRI sequence. We further show that a simple general-linear-model can predict the average axonal diameters from the four model parameters, and map these average axonal diameters in the spinal cords. While clearly further modelling and theoretical developments are necessary, we conclude that salient WM microstructural features can be extracted from simple, SNR-efficient multi-gradient echo MRI, and that this paves the way towards easier estimation of WM microstructure in vivo.

Purpose: A relaxation-enhanced (RE) approach to acquire in vivo localized spectra with flat baselines and good sensitivity has been recently proposed. As RE MR spectroscopy (MRS) targets a subset of a priori known resonances, new possibilities arise to acquire spectroscopic imaging data in faster, more efficient manners. This is hereby illustrated by Spectroscopically Encoded Chemical Shift Imaging (SECSI).Methods: SECSI delivers spectral/spatial correlations by collecting gradient echo trains whose timings are defined by the shifts of the resonances to be disentangled. Condition number considerations allow one to unravel these image contributions for various sites by a simple matrix inversion. The efficiency of the ensuing method is high enough to enable a sampling of additional spatial axes by means of their phase encoding in spin-echo trains.Results: The one-dimensional (1D) spectral / 2D spatial SECSI acquisitions were implemented on phantom, ex vivo, and in vivo models. In all cases, quality site-resolved images were obtained. The experimentally observed enhancements were consistent with theoretical signal-to-noise ratio derivations.Conclusion: While still bound by MRSI's sensitivity limitations, a novel spectroscopic imaging protocol exploiting a priori information, selective excitations and multiple echo encodings, was proposed and demonstrated. The method is promising when dealing with high T-2/ T2* ratios, sparse data, or hyperpolarization studies. Magn Reson Med 77:511-519, 2017. (c) 2016 International Society for Magnetic Resonance in Medicine

Purpose: This study seeks to evaluate in vivo T-2 relaxation times of selectively excited stroke-relevant metabolites via H-1 relaxation-enhanced magnetic resonance spectroscopy (RE-MRS) at 21.1T (900 MHz).Methods: A quadrature surface coil was designed and optimized for investigations of rodents at 21.1T. With voxel localization, a RE-MRS pulse sequence incorporating the excitation of selected metabolites was modified to include a variable echo delay for T-2 measurements. A middle cerebral artery occlusion (MCAO) animal model for stroke was examined with spectra taken 24h post occlusion. Fourteen echo times were acquired, with each measurement completed in less than 2min.Results: The RE-MRS approach produced high-quality spectra of the selectively excited metabolites in the stroked and contralateral regions. T-2 measurements reveal differential results between these regions, with significance achieved for lactic acid.Conclusion: Using the RE-MRS technique at ultra-high magnetic field and an optimized quadrature surface coil design, full metabolic T-2 quantifications in a localized voxel is now possible in less than 27min. Magn Reson Med 77:520-528, 2017. (c) 2016 International Society for Magnetic Resonance in Medicine

Objects making up complex porous systems in Nature usually span a range of sizes. These size distributions play fundamental roles in defining the physicochemical, biophysical and physiological properties of a wide variety of systems - ranging from advanced catalytic materials to Central Nervous System diseases. Accurate and noninvasive measurements of size distributions in opaque, three-dimensional objects, have thus remained long-standing and important challenges. Herein we describe how a recently introduced diffusionbased magnetic resonance methodology, Non-Uniform-Oscillating-Gradient-Spin-Echo (NOGSE), can determine such distributions noninvasively. The method relies on its ability to probe confining lengths with a (length)6 parametric sensitivity, in a constant-time, constant- number-of-gradients fashion; combined, these attributes provide sufficient sensitivity for characterizing the underlying distributions in μm-scaled cellular systems. Theoretical derivations and simulations are presented to verify NOGSE's ability to faithfully reconstruct size distributions through suitable modeling of their distribution parameters. Experiments in yeast cell suspensions - where the ground truth can be determined from ancillary microscopy - corroborate these trends experimentally. Finally, by appending to the NOGSE protocol an imaging acquisition, novel MRI maps of cellular size distributions were collected from a mouse brain. The ensuing micro-architectural contrasts successfully delineated distinctive hallmark anatomical sub-structures, in both white matter and gray matter tissues, in a non-invasive manner. Such findings highlight NOGSE's potential for characterizing aberrations in cellular size distributions upon disease, or during normal processes such as development.

Dynamical decoupling, a generalization of the original NMR spin-echo sequence, is becoming increasingly relevant as a tool for reducing decoherence in quantum systems. Such sequences apply non-equidistant refocusing pulses for optimizing the coupling between systems, and environmental fluctuations characterized by a given noise spectrum. One such sequence, dubbed Selective Dynamical Recoupling (SDR) [P. E. S. Smith, G. Bensky, G. A. Álvarez, G. Kurizki, and L. Frydman, Proc. Natl. Acad. Sci. 109, 5958 (2012)], allows one to coherently reintroduce diffusion decoherence effects driven by fluctuations arising from restricted molecular diffusion [G. A. Álvarez, N. Shemesh, and L. Frydman, Phys. Rev. Lett. 111, 080404 (2013)]. The fully-refocused, constant-time, and constant-number-of-pulses nature of SDR also allows one to filter out "intrinsic" T₁ and T₂ weightings, as well as pulse errors acting as additional sources of decoherence. This article explores such features when the fluctuations are now driven by unrestricted molecular diffusion. In particular, we show that diffusion-driven SDR can be exploited to investigate the decoherence arising from the frequency fluctuations imposed by internal gradients. As a result, SDR presents a unique way of probing and characterizing these internal magnetic fields, given an a priori known free diffusion coefficient. This has important implications in studies of structured systems, including porous media and live tissues, where the internal gradients may serve as fingerprints for the system's composition or structure. The principles of this method, along with full analytical solutions for the unrestricted diffusion-driven modulation of the SDR signal, are presented. The potential of this approach is demonstrated with the generation of a novel source of MRI contrast, based on the background gradients active in an ex vivo mouse brain. Additional features and limitations of this new method are discussed.

Interruptions in cerebral blood flow may lead to devastating neural outcomes. Magnetic resonance has a central role in diagnosing and monitoring these insufficiencies, as well as in understanding their underlying metabolic consequences. Magnetic resonance spectroscopy (MRS) in particular can probe ischemia via the signatures of endogenous metabolites including lactic acid (Lac), N-acetylaspartate, creatine (Cre), and cholines. Typically, MRS reports on these metabolites' concentrations. This study focuses on establishing the potential occurrence of in vivo longitudinal relaxation enhancement (LRE) effects - a phenomenon involving a reduction of the apparent T 1 with selective bandwidth excitations -in a rat stroke model at 21.1 T. Statistically significant reductions in Cre's apparent T 1 s were observed at all the examined post-ischemia time points for both ipsi- and contralateral hemispheres, thereby establishing the existence of LREs for this metabolite in vivo. Ischemia-dependent LRE trends were also noted for Lac in the ipsilateral hemisphere only 24 hours after ischemia. Metabolic T 1 s were also found to vary significantly as a function of post-stroke recovery time, with the most remarkable and rapid changes observed for Lac T 1 s. The potential of such measurements to understand stroke at a molecular level and assist in its diagnosis, is discussed.

1 H magnetic resonance spectroscopy (MRS) yields site-specific signatures that directly report metabolic concentrations, biochemistry and kinetics-provided spectral sensitivity and quality are sufficient. Here, an enabling relaxation-enhanced (RE) MRS approach is demonstrated that by combining highly selective spectral excitations with operation at very high magnetic fields, delivers spectra exhibiting signal-to-noise ratios >50:1 in under 6s for ∼5 × 5 × 5 (mm) 3 voxels, with flat baselines and no interference from water. With this spectral quality, MRS was used to interrogate a number of metabolic properties in stroked rat models. Metabolic confinements imposed by randomly oriented micro-architectures were detected and found to change upon ischaemia; intensities of downfield resonances were found to be selectively altered in stroked hemispheres; and longitudinal relaxation time of lactic acid was found to increase by over 50% its control value as early as 3-h post ischaemia, paralleling the onset of cytotoxic oedema. These results demonstrate potential of 1 H MRS at ultrahigh fields.

In recent years, diffusion MRI has become an extremely important tool for studying the morphology of living brain tissue, as it provides unique insights into both its macrostructure and microstructure. Recent applications of diffusion MRI aimed to characterize the structural connectome using tractography to infer connectivity between brain regions. In parallel to the development of tractography, additional diffusion MRI based frameworks (CHARMED, AxCaliber, ActiveAx) were developed enabling the extraction of a multitude of micro-structural parameters (axon diameter distribution, mean axonal diameter and axonal density). This unique insight into both tissue microstructure and connectivity has enormous potential value in understanding the structure and organization of the brain as well as providing unique insights to abnormalities that underpin disease states.The CONNECT (Consortium Of Neuroimagers for the Non-invasive Exploration of brain Connectivity and Tracts) project aimed to combine tractography and micro-structural measures of the living human brain in order to obtain a better estimate of the connectome, while also striving to extend validation of these measurements. This paper summarizes the project and describes the perspective of using micro-structural measures to study the connectome.

Nuclear magnetic resonance spectroscopy is governed by longitudinal (T ₁) relaxation. For protein and nucleic acid experiments in solutions, it is well established that apparent T₁ values can be enhanced by selective excitation of targeted resonances. The present study explores such longitudinal relaxation enhancement (LRE) effects for molecules residing in biological tissues. The longitudinal relaxation recovery of tissue resonances positioned both down- and upfield of the water peak were measured by spectrally selective excitation/refocusing pulses, and compared with conventional water-suppressed, broadband-excited counterparts at 9.4T. Marked LRE effects with up to threefold reductions in apparent T₁ values were observed as expected for resonances in the 6-9ppm region; remarkably, statistically significant LRE effects were also found for several non-exchanging metabolite resonances in the 1-4ppm region, encompassing 30-50 % decreases in apparent T₁ values. These LRE effects suggest a novel means of increasing the sensitivity of tissue-oriented experiments, and open new vistas to investigate the nature of interactions among metabolites, water and macromolecules at a molecular level. Relax your mind: Longitudinal relaxation enhancement (LRE) is a phenomenon known in biomolecular NMR spectroscopy, which so far has not been observed for metabolites in tissues. In brain tissues, selective excitation shortens the apparent T₁ of exchanging metabolic resonances by 30-300 %. The ensuing high-fidelity spectra are promising for studying the nature of metabolic interactions within tissues.

During recent years, dynamical decoupling (DD) has gained relevance as a tool for manipulating and interrogating quantum systems. This is particularly relevant for spins involved in nuclear magnetic resonance (NMR), where DD sequences can be used to prolong quantum coherences, or to selectively couple or decouple the effects imposed by random environmental fluctuations. In this Letter, we show that these concepts can be exploited to selectively recouple diffusion processes in restricted spaces. The ensuing method provides a novel tool to measure restriction lengths in confined systems such as capillaries, pores or cells. The principles of this method for selectively recoupling diffusion-driven decoherence, its standing within the context of diffusion NMR, extensions to the characterization of other kinds of quantum fluctuations, and corroborating experiments, are presented.

Diffusion-weighted (DW) MRI is a powerful modality for studying microstructure in normal and pathological tissues. The accuracy derived from DW MRI depends on the acquisition of quality images, and on a precise assessment of the b-values involved. Conventional DW MRI tends to be of limited use in regions suffering from large magnetic field or chemical shift heterogeneities, which severely distort the MR images. In this study we propose novel sequences based on SPatio-temporal ENcoding (SPEN), which overcome such shortcomings owing to SPEN's inherent robustness to offsets. SPEN, however, relies on the simultaneous application of gradients and radiofrequency-swept pulses, which may impart different diffusion weightings along the spatial axes. These will be further complicated in DW measurements by the diffusion-sensitizing gradients, and will in general lead to complex, spatially-dependent b-values. This study presents a formalism for analyzing these diffusion-weighted SPEN (dSPEN) data, which takes into account the concomitant effects of adiabatic pulses, of the imaging as well as diffusion gradients, and of the cross-terms between them. These analytical b-values derivations are subject to experimental validations in phantom systems and ex vivo spinal cords. Excellent agreement is found between the theoretical predictions and these dSPEN experiments. The ensuing methodology is then demonstrated by in vivo mapping of diffusion in human breast - organs where conventional k-space DW acquisition methods are challenged by both field and chemical shift heterogeneities. These studies demonstrate the increased robustness of dSPEN vis-à-vis comparable DW echo planar imaging, and demonstrate the value of this new methodology for medium- or high-field diffusion measurements in heterogeneous systems.

Noninvasive measurements of microstructure in materials, cells, and in biological tissues, constitute a unique capability of gradient-assisted NMR. Diffusion-diffraction MR approaches pioneered by Callaghan demonstrated this ability; Oscillating-Gradient Spin-Echo (OGSE) methodologies tackle the demanding gradient amplitudes required for observing diffraction patterns by utilizing constant-frequency oscillating gradient pairs that probe the diffusion spectrum, D(ω). Here we present a new class of diffusion MR experiments, termed Non-uniform Oscillating-Gradient Spin-Echo (NOGSE), which dynamically probe multiple frequencies of the diffusion spectral density at once, thus affording direct microstructural information on the compartment's dimension. The NOGSE methodology applies N constant-amplitude gradient oscillations; N - 1 of these oscillations are spaced by a characteristic time x, followed by a single gradient oscillation characterized by a time y, such that the diffusion dynamics is probed while keeping (N - 1)x + y ≡ T_NOGSE constant. These constant-time, fixed-gradient-amplitude, multi-frequency attributes render NOGSE particularly useful for probing small compartment dimensions with relatively weak gradients - alleviating difficulties associated with probing D(ω) frequency-by-frequency or with varying relaxation weightings, as in other diffusion-monitoring experiments. Analytical descriptions of the NOGSE signal are given, and the sequence's ability to extract small compartment sizes with a sensitivity towards length to the sixth power, is demonstrated using a microstructural phantom. Excellent agreement between theory and experiments was evidenced even upon applying weak gradient amplitudes. An MR imaging version of NOGSE was also implemented in ex vivo pig spinal cords and mouse brains, affording maps based on compartment sizes. The effects of size distributions on NOGSE are also briefly analyzed.

Purpose: To assess, by MR spectroscopy (MRS) and diffusion weighted imaging (DWI), the ability of electrical stimulation of the sphenopalatine ganglion (SPG) to augment stroke recovery in transient middle cerebral artery occluded (t-MCAO) rats, when treatment is started 18 ± 2 h post-occlusion. Materials and Methods: ¹H-MRS imaging (¹H-MRSI) and DWI were used to evaluate ischemic brain tissue after SPG stimulation in rats subjected to 2 h of t-MCAO. Rats were examined by ¹H-MRSI, DWI, and behavioral tests at 16 ± 2 h, 8 days, and 28 days post-MCAO. Results: N-Acetyl-aspartate (NAA) levels of the stimulated and control rats were the same 16 ± 2 h post-MCAO (0.52 ± 0.03, 0.54 ± 0.03). At 28 days post-occlusion, NAA levels were significantly higher in the treated group (0.60 ± 0.04) compared with those of the untreated animals (0.50 ± 0.04; P < 0.05). This effect was more pronounced for regions with low NAA values (0.16 ± 0.03) that changed to 0.32 ± 0.03 (P = 0.04) for the treated group and to 0.10 ± 0.03 (P = 0.20) for the controls. DWI data showed better ischemic tissue condition for the treated rats, but the measured parameters showed only a trend of improvement. The MR results were corroborated by behavioral examinations. Conclusion: Our findings suggest that SPG stimulation may ameliorate MR tissue characteristics following t-MCAO even if treatment is started 18 h post-occlusion.

Stem cell-based therapy is a promising treatment for neurodegenerative diseases. In our laboratory, a novel protocol has been developed to induce bone marrow-derived mesenchymal stem cells (MSC) into neurotrophic factors-secreting cells (NTF-SC), thus combining stem cell-based therapy with the NTF-based neuroprotection. These cells produce and secrete factors such as brain-derived neurotrophic factor (BDNF) and glial cell-derived neurotrophic factor. Conditioned medium of the NTF-SC that was applied to a neuroblastoma cell line (SH-SY5Y) 1 h before exposure to the neurotoxin 6-hydroxydopamine (6-OHDA) demonstrated marked protection. An efficacy study was conducted on the 6-OHDA-induced lesion, a rat model of Parkinson's disease. The cells, either MSC or NTF-SC, were transplanted on the day of 6-OHDA administration and amphetamine-induced rotations were measured as a primary behavior index. We demonstrated that when transplanted posterior to the 6-OHDA lesion, the NTF-SC ameliorated amphetamine-induced rotations by 45. HPLC analysis demonstrated that 6-OHDA induced dopamine depletion to a level of 21 compared to the untreated striatum. NTF-SC inhibited dopamine depletion to a level of 72 of the contralateral striatum. Moreover, an MRI study conducted with iron-labeled cells, followed by histological verification, revealed that the engrafted cells migrated toward the lesion. In a histological assessment, we found that the cells induced regeneration in the damaged striatal dopaminergic nerve terminal network. We therefore conclude that the induced MSC have a therapeutic potential for neurodegenerative processes and diseases, both by the NTFs secretion and by the migratory trait toward the diseased tissue.

Theoretical and experimental studies of restricted diffusion have been conducted for decades using single pulsed field gradient (s-PFG) diffusion experiments. In homogenous samples, the diffusion-diffraction phenomenon arising from a single population of diffusing species has been observed experimentally and predicted theoretically. In this study, we introduce a composite bi-compartmental model which superposes restricted diffusion in microcapillaries with free diffusion in an unconfined compartment, leading to fast and slow diffusing components in the NMR signal decay. Although simplified (no exchange), the superposed diffusion modes in this model may exhibit features seen in more complex porous materials and biological tissues. We find that at low q-values the freely diffusing component masks the restricted diffusion component, and that prolongation of the diffusion time shifts the transition from free to restricted profiles to lower q-values. The effect of increasing the volume fraction of freely diffusing water was also studied; we find that the transition in the signal decay from the free mode to the restricted mode occurs at higher q-values when the volume fraction of the freely diffusing water is increased. These findings were then applied to a phantom consisting of crossing fibers, which demonstrated the same qualitative trends in the signal decay. The angular d-PGSE experiment, which has been recently shown to be able to measure small compartmental dimensions even at low q-values, revealed that microscopic anisotropy is lost at low q-values where the fast diffusing component is prominent. Our findings may be of importance in studying realistic systems which exhibit compartmentation.