RNASE6 is a novel modifier of APOE-ε4 effects on cognition.

Apolipoprotein E4 (APOE-ε4), the strongest common genetic risk factor for Alzheimer's disease (AD), contributes to worse cognition in older adults. However, many APOE-ε4 carriers remain cognitively normal throughout life, suggesting that neuroprotective factors may be present in these individuals. In this study, we leverage whole-blood RNA sequencing (RNAseq) from 324 older adults to identify genetic modifiers of APOE-ε4 effects on cognition. Expression of RNASE6 interacted with APOE-ε4 status (p = 4.35 × 10-8) whereby higher RNASE6 expression was associated with worse memory at baseline among APOE-ε4 carriers. This interaction was replicated using RNAseq data from the prefrontal cortex in an independent dataset (N = 535; p = 0.002), suggesting the peripheral effect of RNASE6 is also present in brain tissue. RNASE6 encodes an antimicrobial peptide involved in innate immune response and has been previously observed in a gene co-expression network module with other AD-related inflammatory genes, including TREM2 and MS4A. Together, these data implicate neuroinflammation in cognitive decline, and suggest that innate immune signaling may be detectable in blood and confer differential susceptibility to AD depending on APOE-ε4.

Evaluation of Sex-Aware PrediXcan Models for Predicting Gene Expression.

Gene-based methods such as PrediXcan use expression quantitative trait loci to build tissue-specific gene expression models when only genetic data is available. There are known sex differences in tissue-specific gene expression and in the genetic architecture of gene expression, but such differences have not been incorporated into predicted gene expression models to date. We built sex-aware PrediXcan models using whole blood transcriptomic data from the Genotype-Tissue Expression (GTEx) project (195 females and 371 males) and evaluated their performance in an independent dataset. Specifically, PrediXcan models were built following the method described in Gamazon et al. 2015, but we included both whole-sample and sex-specific models. Validation was evaluated leveraging lymphoblast RNA sequencing data from the EUR cohort of the 1000 Genomes Project (178 females and 171 males). Correlations (R2) between observed and predicted expression were evaluated in 5,283 autosomal genes to determine performance of models. In sum, we successfully predicted 1,149 genes in males and 623 in females, while 3,511 genes appeared to be not sex-specific. Of the sex-specific genes, 15% (189 genes in males and 73 genes in females) exhibited higher R2 in sex-specific models compared to whole-sample models, although the overall gain in predictive power was generally minimal and well within measurement error. Nevertheless, two female-specific genes and six male-specific genes showed significantly better prediction when using the sex-specific weights versus the whole-sample weights; furthermore, several of these genes play a role in mitochondrial metabolism, which is known to be influenced by sex hormones. Taken together, these results support previous reports of the small contribution of genetic architecture to sex-specific expression. Still, sex-aware PrediXcan models were able to provide robust sex-specific prediction signals. Future studies exploring the contribution of the X chromosome and tissue specificity on sex-specific genetically regulated expression will clarify the utility of this method.

Longitudinal assessment of recovery after spinal cord injury with behavioral measures and diffusion, quantitative magnetization transfer and functional magnetic resonance imaging.

Spinal cord injuries (SCIs) are a leading cause of disability and can severely impact the quality of life. However, to date, the processes of spontaneous repair of damaged spinal cord remain incompletely understood, partly due to a lack of appropriate longitudinal tracking methods. Noninvasive, multiparametric magnetic resonance imaging (MRI) provides potential biomarkers for the comprehensive evaluation of spontaneous repair after SCI. In this study in rats, a clinically relevant contusion injury was introduced at the lumbar level that impairs both hindlimb motor and sensory functions. Quantitative MRI measurements were acquired at baseline and serially post-SCI for up to 2 wk. The progressions of injury and spontaneous recovery in both white and gray matter were tracked longitudinally using pool-size ratio (PSR) measurements derived from quantitative magnetization transfer (qMT) methods, measurements of water diffusion parameters using diffusion tensor imaging (DTI) and intrasegment functional connectivity derived from resting state functional MRI. Changes in these quantitative imaging measurements were correlated with behavioral readouts. We found (a) a progressive decrease in PSR values within 2 wk post-SCI, indicating a progressive demyelination at the center of the injury that was validated with histological staining, (b) PSR correlated closely with fractional anisotropy and transverse relaxation of free water, but did not show significant correlations with behavioral recovery, and (c) preliminary evidence that SCI induced a decrease in functional connectivity between dorsal horns below the injury site at 24 h. Findings from this study not only confirm the value of qMT and DTI methods for assessing the myelination state of injured spinal cord but indicate that they may also have further implications on whether therapies targeted towards remyelination may be appropriate. Additionally, a better understanding of changes after SCI provides valuable information to guide and assess interventions.

Spin-lock imaging of intrinsic susceptibility gradients in tumors.

PURPOSE: Previous studies have shown that diffusion of water through intrinsic susceptibility gradients produces a dispersion of the spin-lattice relaxation rate in the rotating frame (R1ρ ) over a low range of spin-locking amplitudes (0 < ω1 < 100 Hz), whereas at higher ω1 and high magnetic fields, a second dispersion arises due to chemical exchange. Here, we separated these different effects and evaluated their contributions in tumors. METHODS: Maps of R1ρ and its changes with locking field were acquired on intracranial 9-L tumor models. The R1ρ changes due to diffusion ( R1ρDiff ) were calculated by subtracting maps of R1ρ at 100 Hz (R1ρ [100 Hz]) from those at 0 Hz (R1ρ [0 Hz]). The R1ρ changes due to exchange ( R1ρEx ) were calculated by subtracting maps of R1ρ at 5620 Hz (R1ρ [5620 Hz]) from those of R1ρ at 100 Hz (R1ρ [100 Hz]). Measurements of vascular dimensions and spacing were performed ex vivo using 3D confocal microscopy. RESULTS: The R1ρ changes at low ω1 in tumors (5.24 ± 1.78 s-1 ) are substantially (p = 3.76 ) greater than those in normal tissues (1.36 ± 0.70 s-1 ), which we suggest are due to greater contributions from diffusion through susceptibility gradients. Tumor vessels were larger and spaced less closely compared with normal brain, which may be 1 factor contributing the susceptibility within 9-L tumors. The contrast between tumor and normal tissues for R1ρDiff is larger than for R1ρEx and for the apparent R2w . CONCLUSION: Images that are sensitive to the variations of spin-lock relaxation rates at low ω1 provide a novel form of contrast that reflects the heterogeneous nature of intrinsic variations within tumors.

Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI.

PURPOSE: Diffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance for characterizing in-vivo white matter. Models relating microarchitecture to observed DW-MRI signals as a function of diffusion sensitization are the lens through which DW-MRI data are interpreted. Numerous modern approaches offer opportunities to assess more complex intra-voxel structures. Nevertheless, there remains a substantial gap between intra-voxel estimated structures and ground truth captured by 3-D histology. METHODS: Herein, we propose a novel data-driven approach to model the non-linear mapping between observed DW-MRI signals and ground truth structures using a sequential deep neural network regression using residual block deep neural network (ResDNN). Training was performed on two 3-D histology datasets of squirrel monkey brains and validated on a third. A second validation was performed using scan-rescan datasets of 12 subjects from Human Connectome Project. The ResDNN was compared with multiple micro-structure reconstruction methods and super resolved-constrained spherical deconvolution (sCSD) in particular as baseline for both the validations. RESULTS: Angular correlation coefficient (ACC) is a correlation/similarity measure and can be interpreted as accuracy when compared with a ground truth. The median ACC of ResDNN is 0.82 and median ACC's of different variants of CSD are 0.75, 0.77, 0.79. The mean, median and std. of ResDNN & sCSD ACC across 12 subjects from HCP are 0.74, 0.88, 0.31 and 0.61, 0.71, 0.31 respectively. CONCLUSION: This work highlights the ability of deep learning to capture linkages between ex-vivo ground truth data with feasible MRI sequences. The data-driven approach is applicable to human in-vivo data and results in intriguingly high reproducibility of orientation structure.

Histologically derived fiber response functions for diffusion MRI vary across white matter fibers-An ex vivo validation study in the squirrel monkey brain.

Understanding the relationship between the diffusion-weighted MRI signal and the arrangement of white matter fibers is fundamental for accurate voxel-wise reconstruction of the fiber orientation distribution (FOD) and subsequent fiber tractography. Spherical deconvolution reconstruction techniques model the diffusion signal as the convolution of the FOD with a response function that represents the signal profile of a single fiber orientation. Thus, given the signal and a fiber response function, the FOD can be estimated in every imaging voxel by deconvolution. However, the selection of the appropriate response function remains relatively under-studied, and requires further validation. In this work, using 3D histologically defined FODs and the corresponding diffusion signal from three ex vivo squirrel monkey brains, we derive the ground truth response functions. We find that the histologically derived response functions differ from those conventionally used. Next, we find that response functions statistically vary across brain regions, which suggests that the practice of using the same kernel throughout the brain is not optimal. We show that different kernels lead to different FOD reconstructions, which in turn can lead to different tractography results depending on algorithmic parameters, with large variations in the accuracy of resulting reconstructions. Together, these results suggest there is room for improvement in estimating and understanding the relationship between the diffusion signal and the underlying FOD.

Lipopolysaccharide Induced Opening of the Blood Brain Barrier on Aging 5XFAD Mouse Model.

The development of neurotherapeutics for many neurodegenerative diseases has largely been hindered by limited pharmacologic penetration across the blood-brain barrier (BBB). Previous attempts to target and clear amyloid-ß (Aß) plaques, a key mediator of neurodegenerative changes in Alzheimer's disease (AD), have had limited clinical success due to low bioavailability in the brain because of the BBB. Here we test the effects of inducing an inflammatory response to disrupt the BBB in the 5XFAD transgenic mouse model of AD. Lipopolysaccharide (LPS), a bacterial endotoxin recognized by the innate immune system, was injected at varying doses. 24 hours later, mice were injected with either thioflavin S, a fluorescent Aß-binding small molecule or 30 nm superparamagnetic iron oxide (SPIO) nanoparticles, both of which are unable to penetrate the BBB under normal physiologic conditions. Our results showed that when pretreated with 3.0 mg/kg LPS, thioflavin S can be found in the brain bound to Aß plaques in aged 5XFAD transgenic mice. Following the same LPS pretreatment, SPIO nanoparticles could also be found in the brain. However, when done on wild type or young 5XFAD mice, limited SPIO was detected. Our results suggest that the BBB in aged 5XFAD mouse model is susceptible to increased permeability mediated by LPS, allowing for improved delivery of the small molecule thioflavin S to target Aß plaques and SPIO nanoparticles, which are significantly larger than antibodies used in clinical trials for immunotherapy of AD. Although this approach demonstrated efficacy for improved delivery to the brain, LPS treatment resulted in significant weight loss even at low doses, resulting from the induced inflammatory response. These findings suggest inducing inflammation can improve delivery of small and large materials to the brain for improved therapeutic or diagnostic efficacy. However, this approach must be balanced with the risks of systemic inflammation.

Anatomical accuracy of standard-practice tractography algorithms in the motor system - A histological validation in the squirrel monkey brain.

For two decades diffusion fiber tractography has been used to probe both the spatial extent of white matter pathways and the region to region connectivity of the brain. In both cases, anatomical accuracy of tractography is critical for sound scientific conclusions. Here we assess and validate the algorithms and tractography implementations that have been most widely used - often because of ease of use, algorithm simplicity, or availability offered in open source software. Comparing forty tractography results to a ground truth defined by histological tracers in the primary motor cortex on the same squirrel monkey brains, we assess tract fidelity on the scale of voxels as well as over larger spatial domains or regional connectivity. No algorithms are successful in all metrics, and, in fact, some implementations fail to reconstruct large portions of pathways or identify major points of connectivity. The accuracy is most dependent on reconstruction method and tracking algorithm, as well as the seed region and how this region is utilized. We also note a tremendous variability in the results, even though the same MR images act as inputs to all algorithms. In addition, anatomical accuracy is significantly decreased at increased distances from the seed. An analysis of the spatial errors in tractography reveals that many techniques have trouble properly leaving the gray matter, and many only reveal connectivity to adjacent regions of interest. These results show that the most commonly implemented algorithms have several shortcomings and limitations, and choices in implementations lead to very different results. This study should provide guidance for algorithm choices based on study requirements for sensitivity, specificity, or the need to identify particular connections, and should serve as a heuristic for future developments in tractography.

A Web-Based Atlas Combining MRI and Histology of the Squirrel Monkey Brain.

The squirrel monkey (Saimiri sciureus) is a commonly-used surrogate for humans in biomedical research. In the neuroimaging community, MRI and histological atlases serve as valuable resources for anatomical, physiological, and functional studies of the brain; however, no digital MRI/histology atlas is currently available for the squirrel monkey. This paper describes the construction of a web-based multi-modal atlas of the squirrel monkey brain. The MRI-derived information includes anatomical MRI contrast (i.e., T2-weighted and proton-density-weighted) and diffusion MRI metrics (i.e., fractional anisotropy and mean diffusivity) from data acquired both in vivo and ex vivo on a 9.4 Tesla scanner. The histological images include Nissl and myelin stains, co-registered to the corresponding MRI, allowing identification of cyto- and myelo-architecture. In addition, a bidirectional neuronal tracer, biotinylated dextran amine (BDA) was injected into the primary motor cortex, enabling highly specific identification of regions connected to the injection location. The atlas integrates the results of common image analysis methods including diffusion tensor imaging glyphs, labels of 57 white-matter tracts identified using DTI-tractography, and 18 cortical regions of interest identified from Nissl-revealed cyto-architecture. All data are presented in a common space, and all image types are accessible through a web-based atlas viewer, which allows visualization and interaction of user-selectable contrasts and varying resolutions. By providing an easy to use reference system of anatomical information, our web-accessible multi-contrast atlas forms a rich and convenient resource for comparisons of brain findings across subjects or modalities. The atlas is called the Combined Histology-MRI Integrated Atlas of the Squirrel Monkey (CHIASM). All images are accessible through our web-based viewer ( https://chiasm.vuse.vanderbilt.edu /), and data are available for download at ( https://www.nitrc.org/projects/smatlas/ ).

Harmonizing 1.5T/3T Diffusion Weighted MRI through Development of Deep Learning Stabilized Microarchitecture Estimators.

Diffusion weighted magnetic resonance imaging (DW-MRI) is interpreted as a quantitative method that is sensitive to tissue microarchitecture at a millimeter scale. However, the sensitization is dependent on acquisition sequences (e.g., diffusion time, gradient strength, etc.) and susceptible to imaging artifacts. Hence, comparison of quantitative DW-MRI biomarkers across field strengths (including different scanners, hardware performance, and sequence design considerations) is a challenging area of research. We propose a novel method to estimate microstructure using DW-MRI that is robust to scanner difference between 1.5T and 3T imaging. We propose to use a null space deep network (NSDN) architecture to model DW-MRI signal as fiber orientation distributions (FOD) to represent tissue microstructure. The NSDN approach is consistent with histologically observed microstructure (on previously acquired ex vivo squirrel monkey dataset) and scan-rescan data. The contribution of this work is that we incorporate identical dual networks (IDN) to minimize the influence of scanner effects via scan-rescan data. Briefly, our estimator is trained on two datasets. First, a histology dataset was acquired on three squirrel monkeys with corresponding DW-MRI and confocal histology (512 independent voxels). Second, 37 control subjects from the Baltimore Longitudinal Study of Aging (67-95 y/o) were identified who had been scanned at 1.5T and 3T scanners (b-value of 700 s/mm2, voxel resolution at 2.2mm, 30-32 gradient volumes) with an average interval of 4 years (standard deviation 1.3 years). After image registration, we used paired white matter (WM) voxels for 17 subjects and 440 histology voxels for training and 20 subjects and 72 histology voxels for testing. We compare the proposed estimator with super-resolved constrained spherical deconvolution (CSD) and a previously presented regression deep neural network (DNN). NSDN outperformed CSD and DNN in angular correlation coefficient (ACC) 0.81 versus 0.28 and 0.46, mean squared error (MSE) 0.001 versus 0.003 and 0.03, and general fractional anisotropy (GFA) 0.05 versus 0.05 and 0.09. Further validation and evaluation with contemporaneous imaging are necessary, but the NSDN is promising avenue for building understanding of microarchitecture in a consistent and device-independent manner.

Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE.

Intra-voxel models of the diffusion signal are essential for interpreting organization of the tissue environment at micrometer level with data at millimeter resolution. Recent advances in data driven methods have enabled direct comparison and optimization of methods for in-vivo data with externally validated histological sections with both 2-D and 3-D histology. Yet, all existing methods make limiting assumptions of either (1) model-based linkages between b-values or (2) limited associations with single shell data. We generalize prior deep learning models that used single shell spherical harmonic transforms to integrate the recently developed simple harmonic oscillator reconstruction (SHORE) basis. To enable learning on the SHORE manifold, we present an alternative formulation of the fiber orientation distribution (FOD) object using the SHORE basis while representing the observed diffusion weighted data in the SHORE basis. To ensure consistency of hyper-parameter optimization for SHORE, we present our Deep SHORE approach to learn on a data-optimized manifold. Deep SHORE is evaluated with eight-fold cross-validation of a preclinical MRI-histology data with four b-values. Generalizability of in-vivo human data is evaluated on two separate 3T MRI scanners. Specificity in terms of angular correlation (ACC) with the preclinical data improved on single shell: 0.78 relative to 0.73 and 0.73, multi-shell: 0.80 relative to 0.74 (p < 0.001). In the in-vivo human data, Deep SHORE was more consistent across scanners with 0.63 relative to other multi-shell methods 0.39, 0.52 and 0.57 in terms of ACC. In conclusion, Deep SHORE is a promising method to enable data driven learning with DW-MRI under conditions with varying b-values, number of diffusion shells, and gradient directions per shell.

Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning.

Diffusion-weighted magnetic resonance imaging (DW-MRI) allows for non-invasive imaging of the local fiber architecture of the human brain at a millimetric scale. Multiple classical approaches have been proposed to detect both single (e.g., tensors) and multiple (e.g., constrained spherical deconvolution, CSD) fiber population orientations per voxel. However, existing techniques generally exhibit low reproducibility across MRI scanners. Herein, we propose a data-driven technique using a neural network design which exploits two categories of data. First, training data were acquired on three squirrel monkey brains using ex-vivo DW-MRI and histology of the brain. Second, repeated scans of human subjects were acquired on two different scanners to augment the learning of the network proposed. To use these data, we propose a new network architecture, the null space deep network (NSDN), to simultaneously learn on traditional observed/truth pairs (e.g., MRI-histology voxels) along with repeated observations without a known truth (e.g., scan-rescan MRI). The NSDN was tested on twenty percent of the histology voxels that were kept completely blind to the network. NSDN significantly improved absolute performance relative to histology by 3.87% over CSD and 1.42% over a recently proposed deep neural network approach. Moreover, it improved reproducibility on the paired data by 21.19% over CSD and 10.09% over a recently proposed deep approach. Finally, NSDN improved generalizability of the model to a third in vivo human scanner (which was not used in training) by 16.08% over CSD and 10.41% over a recently proposed deep learning approach. This work suggests that data-driven approaches for local fiber reconstruction are more reproducible, informative and precise and offers a novel, practical method for determining these models.

Histological validation of diffusion MRI fiber orientation distributions and dispersion.

Diffusion magnetic resonance imaging (dMRI) is widely used to probe tissue microstructure, and is currently the only non-invasive way to measure the brain's fiber architecture. While a large number of approaches to recover the intra-voxel fiber structure have been utilized in the scientific community, a direct, 3D, quantitative validation of these methods against relevant histological fiber geometries is lacking. In this study, we investigate how well different high angular resolution diffusion imaging (HARDI) models and reconstruction methods predict the ground-truth histologically defined fiber orientation distribution (FOD), as well as investigate their behavior over a range of physical and experimental conditions. The dMRI methods tested include constrained spherical deconvolution (CSD), Q-ball imaging (QBI), diffusion orientation transform (DOT), persistent angular structure (PAS), and neurite orientation dispersion and density imaging (NODDI) methods. Evaluation criteria focus on overall agreement in FOD shape, correct assessment of the number of fiber populations, and angular accuracy in orientation. In addition, we make comparisons of the histological orientation dispersion with the fiber spread determined from the dMRI methods. As a general result, no HARDI method outperformed others in all quality criteria, with many showing tradeoffs in reconstruction accuracy. All reconstruction techniques describe the overall continuous angular structure of the histological FOD quite well, with good to moderate correlation (median angular correlation coefficient > 0.70) in both single- and multiple-fiber voxels. However, no method is consistently successful at extracting discrete measures of the number and orientations of FOD peaks. The major inaccuracies of all techniques tend to be in extracting local maxima of the FOD, resulting in either false positive or false negative peaks. Median angular errors are ∼10° for the primary fiber direction and ∼20° for the secondary fiber, if present. For most methods, these results did not vary strongly over a wide range of acquisition parameters (number of diffusion weighting directions and b value). Regardless of acquisition parameters, all methods show improved successes at resolving multiple fiber compartments in a voxel when fiber populations cross at near-orthogonal angles, with no method adequately capturing low to moderate angle (<60°) crossing fibers. Finally, most methods are limited in their ability to capture orientation dispersion, resulting in low to moderate, yet statistically significant, correlation with histologically-derived dispersion with both HARDI and NODDI methodologies. Together, these results provide quantitative measures of the reliability and limitations of dMRI reconstruction methods and can be used to identify relative advantages of competing approaches as well as potential strategies for improving accuracy.

Confirmation of a gyral bias in diffusion MRI fiber tractography.

Diffusion MRI fiber tractography has been increasingly used to map the structural connectivity of the human brain. However, this technique is not without limitations; for example, there is a growing concern over anatomically correlated bias in tractography findings. In this study, we demonstrate that there is a bias for fiber tracking algorithms to terminate preferentially on gyral crowns, rather than the banks of sulci. We investigate this issue by comparing diffusion MRI (dMRI) tractography with equivalent measures made on myelin-stained histological sections. We begin by investigating the orientation and trajectories of axons near the white matter/gray matter boundary, and the density of axons entering the cortex at different locations along gyral blades. These results are compared with dMRI orientations and tract densities at the same locations, where we find a significant gyral bias in many gyral blades across the brain. This effect is shown for a range of tracking algorithms, both deterministic and probabilistic, and multiple diffusion models, including the diffusion tensor and a high angular resolution diffusion imaging technique. Additionally, the gyral bias occurs for a range of diffusion weightings, and even for very high-resolution datasets. The bias could significantly affect connectivity results using the current generation of tracking algorithms.

Can increased spatial resolution solve the crossing fiber problem for diffusion MRI?

It is now widely recognized that voxels with crossing fibers or complex geometrical configurations present a challenge for diffusion MRI (dMRI) reconstruction and fiber tracking, as well as microstructural modeling of brain tissues. This "crossing fiber" problem has been estimated to affect anywhere from 30% to as many as 90% of white matter voxels, and it is often assumed that increasing spatial resolution will decrease the prevalence of voxels containing multiple fiber populations. The aim of this study is to estimate the extent of the crossing fiber problem as we progressively increase the spatial resolution, with the goal of determining whether it is possible to mitigate this problem with higher resolution spatial sampling. This is accomplished using ex vivo MRI data of the macaque brain, followed by histological analysis of the same specimen to validate these measurements, as well as to extend this analysis to resolutions not yet achievable in practice with MRI. In both dMRI and histology, we find unexpected results: the prevalence of crossing fibers increases as we increase spatial resolution. The problem of crossing fibers appears to be a fundamental limitation of dMRI associated with the complexity of brain tissue, rather than a technical problem that can be overcome with advances such as higher fields and stronger gradients.

Comparison of 3D orientation distribution functions measured with confocal microscopy and diffusion MRI.

The ability of diffusion MRI (dMRI) fiber tractography to non-invasively map three-dimensional (3D) anatomical networks in the human brain has made it a valuable tool in both clinical and research settings. However, there are many assumptions inherent to any tractography algorithm that can limit the accuracy of the reconstructed fiber tracts. Among them is the assumption that the diffusion-weighted images accurately reflect the underlying fiber orientation distribution (FOD) in the MRI voxel. Consequently, validating dMRI's ability to assess the underlying fiber orientation in each voxel is critical for its use as a biomedical tool. Here, using post-mortem histology and confocal microscopy, we present a method to perform histological validation of orientation functions in 3D, which has previously been limited to two-dimensional analysis of tissue sections. We demonstrate the ability to extract the 3D FOD from confocal z-stacks, and quantify the agreement between the MRI estimates of orientation information obtained using constrained spherical deconvolution (CSD) and the true geometry of the fibers. We find an orientation error of approximately 6° in voxels containing nearly parallel fibers, and 10-11° in crossing fiber regions, and note that CSD was unable to resolve fibers crossing at angles below 60° in our dataset. This is the first time that the 3D white matter orientation distribution is calculated from histology and compared to dMRI. Thus, this technique serves as a gold standard for dMRI validation studies - providing the ability to determine the extent to which the dMRI signal is consistent with the histological FOD, and to establish how well different dMRI models can predict the ground truth FOD.

Integrating histology and MRI in the first digital brain of common squirrel monkey, Saimiri sciureus.

This effort is a continuation of development of a digital brain atlas of the common squirrel monkey, Saimiri sciureus, a New World monkey with functional and microstructural organization of central nervous system similar to that of humans. Here, we present the integration of histology with multi-modal magnetic resonance imaging (MRI) atlas constructed from the brain of an adult female squirrel monkey. The central concept of this work is to use block face photography to establish an intermediate common space in coordinate system which preserves the high resolution in-plane resolution of histology while enabling 3-D correspondence with MRI. In vivo MRI acquisitions include high resolution T2 structural imaging (300 µm isotropic) and low resolution diffusion tensor imaging (600 um isotropic). Ex vivo MRI acquisitions include high resolution T2 structural imaging and high resolution diffusion tensor imaging (both 300 µm isotropic). Cortical regions were manually annotated on the co-registered volumes based on published histological sections in-plane. We describe mapping of histology and MRI based data of the common squirrel monkey and construction of a viewing tool that enable online viewing of these datasets. The previously descried atlas MRI is used for its deformation to provide accurate conformation to the MRI, thus adding information at the histological level to the MRI volume. This paper presents the mapping of single 2D image slice in block face as a proof of concept and this can be extended to map the atlas space in 3D coordinate system as part of the future work and can be loaded to an XNAT system for further use.

The radial diffusivity and magnetization transfer pool size ratio are sensitive markers for demyelination in a rat model of type III multiple sclerosis (MS) lesions.

Determining biophysical sensitivity and specificity of quantitative magnetic resonance imaging is essential to develop effective imaging metrics of neurodegeneration. Among these metrics, apparent pool size ratio (PSR) from quantitative magnetization transfer (qMT) imaging and radial diffusivity (RD) from diffusion tensor imaging (DTI) are both known to relate to histological measure of myelin density and integrity. However their relative sensitivities towards quantitative myelin detection are unknown. In this study, we correlated high-resolution quantitative magnetic resonance imaging measures of subvoxel tissue structures with corresponding quantitative myelin histology in a lipopolysaccharide (LPS) mediated animal model of MS. Specifically, we acquired quantitative magnetization transfer (qMT) and diffusion tensor imaging (DTI) metrics (on the same tissue sample) in an animal model system of type III oligodendrogliopathy which lacked prominent lymphocytic infiltration, a system that had not been previously examined with quantitative MRI. We find that the qMT measured apparent pool size ratio (PSR) showed the strongest correlation with a histological measure of myelin content. DTI measured RD showed the next strongest correlation, and other DTI and relaxation parameters (such as the longitudinal relaxation rate (R1f) or fractional anisotropy (FA)) showed considerably weaker correlations with myelin content.

A new method for detecting exchanging amide protons using chemical exchange rotation transfer.

In this study, we introduce a new method for amide proton transfer imaging based on chemical exchange rotation transfer. It avoids several artifacts that plague conventional chemical exchange saturation transfer approaches by creating label and reference scans based on varying the irradiation pulse rotation angle (π and 2π radians) instead of the frequency offset (3.5 and -3.5 ppm). Specifically, conventional analysis is sensitive to confounding contributions from magnetic field (B(0)) inhomogeneities and, more problematically, inherently asymmetric macromolecular resonances. In addition, the lipid resonance at -3.5 ppm complicates the interpretation of the reference scan and decreases the resulting contrast. Finally, partial overlap of the amide signal by nearby amines and hydroxyls obscure the results. By avoiding these issues, our new method is a promising approach for imaging endogenous protein and peptide content and mapping pH.

Multi-angle ratiometric approach to measure chemical exchange in amide proton transfer imaging.

Amide proton transfer imaging, a specific form of chemical exchange saturation transfer imaging, has previously been applied to studies of acute ischemic acidosis, stroke, and cancer. However, interpreting the resulting contrast is complicated by its dependence on the exchange rate between amides and water, the amide concentration, amide and water relaxation, and macromolecular magnetization transfer. Hence, conventional chemical exchange saturation transfer contrast is not specific to changes such as reductions in pH due to tissue acidosis. In this article, a multi-angle ratiometric approach based on several pulsed-chemical exchange saturation transfer scans at different irradiation flip angles is proposed to specifically reflect exchange rates only. This separation of exchange effects in pulsed-chemical exchange saturation transfer experiments is based on isolating rotation vs. saturation contributions, and such methods form a new subclass of chemical exchange rotation transfer (CERT) experiments. Simulations and measurements of creatine/agar phantoms indicate that a newly proposed imaging metric isolates the effects of exchange rate changes, independent of other sample parameters.