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A comparison of neuroanatomical features of the brain between humans and our evolutionary relatives, nonhuman primates, is key to understanding the human brain system and the neural basis of mental and neurological disorders. Although most comparative MRI studies of human and nonhuman primate brains have been based on brains of primates that had been used as subjects in experiments, it is essential to investigate various species of nonhuman primates in order to elucidate and interpret the diversity of neuroanatomy features among humans and nonhuman primates. To develop a research platform for this purpose, it is necessary to harmonize the scientific contributions of studies with the standards of animal ethics, animal welfare, and the conservation of brain information for long-term continuation of the field. In previous research, we first developed a gated data-repository of anatomical images obtained using 9.4-T ex vivo MRI of postmortem brain samples from 12 nonhuman primate species, and which are stored at the Japan Monkey Centre. In the present study, as a second phase, we released a collection of T2-weighted images and diffusion tensor images obtained in nine species: white-throated capuchin, Bolivian squirrel monkey, stump-tailed macaque, Tibet monkey, Sykes' monkey, Assamese macaque, pig-tailed macaque, crested macaque, and chimpanzee. Our image repository should facilitate scientific discoveries in the field of comparative neuroscience. This repository can also promote animal ethics and animal welfare in experiments with nonhuman primate models by optimizing methods for in vivo and ex vivo MRI scanning of brains and supporting veterinary neuroradiological education. In addition, the repository is expected to contribute to conservation, preserving information about the brains of various primates, including endangered species, in a permanent digital form., Competing Interests: Declaration of Competing Interest The authors have no affiliation with any organization with a direct or indirectfinancial interest in the subject matter discussed in the manuscript., (Copyright © 2023. Published by Elsevier Inc.)

Quantification of tissue magnetic susceptibility using MRI offers a non-invasive measure of important tissue components in the brain, such as iron and myelin, potentially providing valuable information about normal and pathological conditions during aging. Despite many advances made in recent years on imaging techniques of quantitative susceptibility mapping (QSM), accurate and robust automated segmentation tools for QSM images that can help generate universal and sharable susceptibility measures in a biologically meaningful set of structures are still not widely available. In the present study, we developed an automated process to segment brain nuclei and quantify tissue susceptibility in these regions based on a susceptibility multi-atlas library, consisting of 10 atlases with T1-weighted images, gradient echo (GRE) magnitude images and QSM images of brains with different anatomic patterns. For each atlas in this library, 10 regions of interest in iron-rich deep gray matter structures that are better defined by QSM contrast were manually labeled, including caudate, putamen, globus pallidus internal/external, thalamus, pulvinar, subthalamic nucleus, substantia nigra, red nucleus and dentate nucleus in both left and right hemispheres. We then tested different pipelines using different combinations of contrast channels to bring the set of labels from the multi-atlases to each target brain and compared them with the gold standard manual delineation. The results showed that the segmentation accuracy using dual contrasts QSM/T1 pipeline outperformed other dual-contrast or single-contrast pipelines. The dice values of 0.77 ± 0.09 using the QSM/T1 multi-atlas pipeline rivaled with the segmentation reliability obtained from multiple evaluators with dice values of 0.79 ± 0.07 and gave comparable or superior performance in segmenting subcortical nuclei in comparison with standard FSL FIRST or recent multi-atlas package of volBrain. The segmentation performance of the QSM/T1 multi-atlas was further tested on QSM images acquired using different acquisition protocols and platforms and showed good reliability and reproducibility with average dice of 0.79 ± 0.08 to manual labels and 0.89 ± 0.04 in an inter-protocol manner. The extracted quantitative magnetic susceptibility values in the deep gray matter nuclei also correlated well between different protocols with inter-protocol correlation constants all larger than 0.97. Such reliability and performance was ultimately validated in an external dataset acquired at another study site with consistent susceptibility measures obtained using the QSM/T1 multi-atlas approach in comparison to those using manual delineation. In summary, we designed a susceptibility multi-atlas tool for automated and reliable segmentation of QSM images and for quantification of magnetic susceptibilities. It is publicly available through our cloud-based platform (www.mricloud.org). Further improvement on the performance of this multi-atlas tool is expected by increasing the number of atlases in the future., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Diffusion tensor imaging (DTI) is used extensively in neuroscience to noninvasively estimate white matter (WM) microarchitecture. However, the diffusion signal is inherently ambiguous because it infers WM structure from the orientation of water diffusion and cannot identify the biological sources of diffusion changes. To compare inferred WM estimates to directly labeled axonal elements, we performed a novel within-subjects combination of high-resolution ex vivo DTI with two-photon laser microscopy of intact mouse brains rendered optically transparent by Clear Lipid-exchanged, Anatomically Rigid, Imaging/immunostaining compatible, Tissue hYdrogel (CLARITY). We found that myelin basic protein (MBP) immunofluorescence significantly correlated with fractional anisotropy (FA), especially in WM regions with coherent fiber orientations and low fiber dispersion. Our results provide evidence that FA is particularly sensitive to myelination in WM regions with these characteristics. Furthermore, we found that radial diffusivity (RD) was only sensitive to myelination in a subset of WM tracts, suggesting that the association of RD with myelin should be used cautiously. This combined DTI-CLARITY approach illustrates, for the first time, a framework for using brain-wide immunolabeling of WM targets to elucidate the relationship between the diffusion signal and its biological underpinnings. This study also demonstrates the feasibility of a within-subject combination of noninvasive neuroimaging and tissue clearing techniques that has broader implications for neuroscience research., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Technologies for multi-atlas brain segmentation of T1-weighted MRI images have rapidly progressed in recent years, with highly promising results. This approach, however, relies on a large number of atlases with accurate and consistent structural identifications. Here, we introduce our atlas inventories (n=90), which cover ages 4-82years with unique hierarchical structural definitions (286 structures at the finest level). This multi-atlas library resource provides the flexibility to choose appropriate atlases for various studies with different age ranges and structure-definition criteria. In this paper, we describe the details of the atlas resources and demonstrate the improved accuracy achievable with a dynamic age-matching approach, in which atlases that most closely match the subject's age are dynamically selected. The advanced atlas creation strategy, together with atlas pre-selection principles, is expected to support the further development of multi-atlas image segmentation., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Regional heterogeneity in cortical cyto- and myeloarchitecture forms the structural basis of mapping of cortical areas in the human brain. In this study, we investigate the potential of diffusion MRI to probe the microstructure of cortical gray matter and its region-specific heterogeneity across cortical areas in the fixed human brain. High angular resolution diffusion imaging (HARDI) data at an isotropic resolution of 92-μm and 30 diffusion-encoding directions were acquired using a 3D diffusion-weighted gradient-and-spin-echo sequence, from prefrontal (Brodmann area 9), primary motor (area 4), primary somatosensory (area 3b), and primary visual (area 17) cortical specimens (n=3 each) from three human subjects. Further, the diffusion MR findings in these cortical areas were compared with histological silver impregnation of the same specimens, in order to investigate the underlying architectonic features that constitute the microstructural basis of diffusion-driven contrasts in cortical gray matter. Our data reveal distinct and region-specific diffusion MR contrasts across the studied areas, allowing delineation of intracortical bands of tangential fibers in specific layers-layer I, layer VI, and the inner and outer bands of Baillarger. The findings of this work demonstrate unique sensitivity of diffusion MRI to differentiate region-specific cortical microstructure in the human brain, and will be useful for myeloarchitectonic mapping of cortical areas as well as to achieve an understanding of the basis of diffusion NMR contrasts in cortical gray matter., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Voxel-based analysis is widely used for quantitative analysis of brain MRI. While this type of analysis provides the highest granularity level of spatial information (i.e., each voxel), the sheer number of voxels and noisy information from each voxel often lead to low sensitivity for detection of abnormalities. To ameliorate this issue, granularity reduction is commonly performed by applying isotropic spatial filtering. This study proposes a systematic reduction of the spatial information using ontology-based hierarchical structural relationships. The 254 brain structures were first defined in multiple (n=29) geriatric atlases. The multiple atlases were then applied to T1-weighted MR images of each subject's data for automated brain parcellation and five levels of ontological relationships were established, which further reduced the spatial dimension to as few as 11 structures. At each ontology level, the amount of atrophy was evaluated, providing a unique view of low-granularity analysis. This reduction of spatial information allowed us to investigate the anatomical features of each patient, demonstrated in an Alzheimer's disease group., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Atlas-based image analysis (ABA), in which an anatomical "parcellation map" is used for parcel-by-parcel image quantification, is widely used to analyze anatomical and functional changes related to brain development, aging, and various diseases. The parcellation maps are often created based on common MRI templates, which allow users to transform the template to target images, or vice versa, to perform parcel-by-parcel statistics, and report the scientific findings based on common anatomical parcels. The use of a study-specific template, which represents the anatomical features of the study population better than common templates, is preferable for accurate anatomical labeling; however, the creation of a parcellation map for a study-specific template is extremely labor intensive, and the definitions of anatomical boundaries are not necessarily compatible with those of the common template. In this study, we employed a volume-based template estimation (VTE) method to create a neonatal brain template customized to a study population, while keeping the anatomical parcellation identical to that of a common MRI atlas. The VTE was used to morph the standardized parcellation map of the JHU-neonate-SS atlas to capture the anatomical features of a study population. The resultant "study-customized" T1-weighted and diffusion tensor imaging (DTI) template, with three-dimensional anatomical parcellation that defined 122 brain regions, was compared with the JHU-neonate-SS atlas, in terms of the registration accuracy. A pronounced increase in the accuracy of cortical parcellation and superior tensor alignment were observed when the customized template was used. With the customized atlas-based analysis, the fractional anisotropy (FA) detected closely approximated the manual measurements. This tool provides a solution for achieving normalization-based measurements with increased accuracy, while reporting scientific findings in a consistent framework., (Copyright © 2014 Elsevier Inc. All rights reserved.)

High-resolution diffusion MRI (dMRI) is useful for resolving complex microstructures in the mouse brain, but technically challenging for in vivo studies due to the long scan time. In this study, selective excitation and a three-dimensional fast imaging sequence were used to achieve in vivo high-resolution dMRI of the mouse brain at 11.7Tesla. By reducing the field of view using spatially selective radio frequency pulses, we were able to focus on targeted brain structures and acquire high angular resolution diffusion imaging (HARDI) data at an isotropic resolution of 0.1mm and 30 diffusion encoding directions in approximately 1h. We investigated the complex tissue microstructures of the mouse hippocampus, cerebellum, and several cortical areas using this localized dMRI approach, and compared the results with histological sections stained with several axonal and dendritic markers. In the mouse visual cortex, the results showed predominately radially arranged structures in an outer layer and tangentially arranged structures in an inner layer, similar to observations from postmortem human brain specimens., (Copyright © 2014 Elsevier Inc. All rights reserved.)

It has been shown that the anatomy of major white matter tracts can be delineated using diffusion tensor imaging (DTI) data. Tract reconstruction, however, often suffers from a large number of false-negative results when a simple line propagation algorithm is used. This limits the application of this technique to only the core of prominent white matter tracts. By employing probabilistic path-generation algorithms, connectivity between a larger number of anatomical regions can be studied, but an increase in the number of false-positive results is inevitable. One of the causes of the inaccuracy is the complex axonal anatomy within a voxel; however, high-angular resolution (HAR) methods have been proposed to ameliorate this limitation. However, HAR data are relatively rare due to the long scan times required and the low signal-to-noise ratio. In this study, we tested a probabilistic path-finding method in which two anatomical regions with known connectivity were pre-defined and a path that maximized agreement with the DTI data was searched. To increase the accuracy of the trajectories, knowledge-based anatomical constraints were applied. The reconstruction protocols were tested using DTI data from 19 normal subjects to examine test-retest reproducibility and cross-subject variability. Fifty-two tracts were found to be reliably reconstructed using this approach, which can be viewed on our website., (© 2013 Elsevier Inc. All rights reserved.)

MRI-based human brain atlases, which serve as a common coordinate system for image analysis, play an increasingly important role in our understanding of brain anatomy, image registration, and segmentation. Study-specific brain atlases are often obtained from one of the subjects in a study or by averaging the images of all participants after linear or non-linear registration. The latter approach has the advantage of providing an unbiased anatomical representation of the study population. But, the image contrast is influenced by both inherent MR contrasts and residual anatomical variability after the registration; in addition, the topology of the brain structures cannot reliably be preserved. In this study, we demonstrated a population-based template-creation approach, which is based on Bayesian template estimation on a diffeomorphic random orbit model. This approach attempts to define a population-representative template without the cross-subject intensity averaging; thus, the topology of the brain structures is preserved. It has been tested for segmented brain structures, such as the hippocampus, but its validity on whole-brain MR images has not been examined. This paper validates and evaluates this atlas generation approach, i.e., Volume-based Template Estimation (VTE). Using datasets from normal subjects and Alzheimer's patients, quantitative measurements of sub-cortical structural volumes, metric distance, displacement vector, and Jacobian were examined to validate the group-averaged shape features of the VTE. In addition to the volume-based quantitative analysis, the preserved brain topology of the VTE allows surface-based analysis within the same atlas framework. This property was demonstrated by analyzing the registration accuracy of the pre- and post-central gyri. The proposed method achieved registration accuracy within 1mm for these population-preserved cortical structures in an elderly population., (Published by Elsevier Inc.)

Diffusion tensor imaging (DTI) of the laboratory mouse brain provides important macroscopic information for anatomical characterization of mouse models in basic research. Currently, in vivo DTI of the mouse brain is often limited by the available resolution. In this study, we demonstrate in vivo high-resolution DTI of the mouse brain using a cryogenic probe and a modified diffusion-weighted gradient and spin echo (GRASE) imaging sequence at 11.7 T. Three-dimensional (3D) DTI of the entire mouse brain at 0.125 mm isotropic resolution could be obtained in approximately 2 h. The high spatial resolution, which was previously only available with ex vivo imaging, enabled non-invasive examination of small structures in the adult and neonatal mouse brains. Based on data acquired from eight adult mice, a group-averaged DTI atlas of the in vivo adult mouse brain with 60 structure segmentations was developed. Comparisons between in vivo and ex vivo mouse brain DTI data showed significant differences in brain morphology and tissue contrasts, which indicate the importance of the in vivo DTI-based mouse brain atlas., (Copyright © 2013 Elsevier Inc. All rights reserved.)

The purpose of this paper is to extend the single-subject Eve atlas from Johns Hopkins University, which currently contains diffusion tensor and T1-weighted anatomical maps, by including contrast based on quantitative susceptibility mapping. The new atlas combines a "deep gray matter parcellation map" (DGMPM) derived from a single-subject quantitative susceptibility map with the previously established "white matter parcellation map" (WMPM) from the same subject's T1-weighted and diffusion tensor imaging data into an MNI coordinate map named the "Everything Parcellation Map in Eve Space," also known as the "EvePM." It allows automated segmentation of gray matter and white matter structures. Quantitative susceptibility maps from five healthy male volunteers (30 to 33 years of age) were coregistered to the Eve Atlas with AIR and Large Deformation Diffeomorphic Metric Mapping (LDDMM), and the transformation matrices were applied to the EvePM to produce automated parcellation in subject space. Parcellation accuracy was measured with a kappa analysis for the left and right structures of six deep gray matter regions. For multi-orientation QSM images, the Kappa statistic was 0.85 between automated and manual segmentation, with the inter-rater reproducibility Kappa being 0.89 for the human raters, suggesting "almost perfect" agreement between all segmentation methods. Segmentation seemed slightly more difficult for human raters on single-orientation QSM images, with the Kappa statistic being 0.88 between automated and manual segmentation, and 0.85 and 0.86 between human raters. Overall, this atlas provides a time-efficient tool for automated coregistration and segmentation of quantitative susceptibility data to analyze many regions of interest. These data were used to establish a baseline for normal magnetic susceptibility measurements for over 60 brain structures of 30- to 33-year-old males. Correlating the average susceptibility with age-based iron concentrations in gray matter structures measured by Hallgren and Sourander (1958) allowed interpolation of the average iron concentration of several deep gray matter regions delineated in the EvePM., (Copyright © 2013 Elsevier Inc. All rights reserved.)

A three-dimensional stereotaxic atlas of the human brainstem based on high resolution ex vivo diffusion tensor imaging (DTI) is introduced. The atlas consists of high resolution (125-255 μm isotropic) three-dimensional DT images of the formalin-fixed brainstem acquired at 11.7 T. The DTI data revealed microscopic neuroanatomical details, allowing three-dimensional visualization and reconstruction of fiber pathways including the decussation of the pyramidal tract fibers, and interdigitating fascicles of the corticospinal and transverse pontine fibers. Additionally, strong gray-white matter contrasts in the apparent diffusion coefficient (ADC) maps enabled precise delineation of gray matter nuclei in the brainstem, including the cranial nerve and the inferior olivary nuclei. Comparison with myelin-stained histology shows that at the level of resolution achieved in this study, the structural details resolved with DTI contrasts in the brainstem were comparable to anatomical delineation obtained with histological sectioning. Major neural structures delineated from DTI contrasts in the brainstem are segmented and three-dimensionally reconstructed. Further, the ex vivo DTI data are nonlinearly mapped to a widely-used in vivo human brain atlas, to construct a high-resolution atlas of the brainstem in the Montreal Neurological Institute (MNI) stereotaxic coordinate space. The results demonstrate the feasibility of developing a 3D DTI based atlas for detailed characterization of brainstem neuroanatomy with high resolution and contrasts, which will be a useful resource for research and clinical applications., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Resting state functional connectivity MRI (rsfc-MRI) reveals a wealth of information about the functional organization of the brain, but poses unique challenges for quantitative image analysis, mostly related to the large number of voxels with low signal-to-noise ratios. In this study, we tested the idea of using a prior spatial parcellation of the entire brain into various structural units, to perform an analysis on a structure-by-structure, rather than voxel-by-voxel, basis. This analysis, based upon atlas parcels, potentially offers enhanced SNR and reproducibility, and can be used as a common anatomical framework for cross-modality and cross-subject quantitative analysis. We used Large Deformation Diffeomorphic Metric Mapping (LDDMM) and a deformable brain atlas to parcel each brain into 185 regions. To investigate the precision of the cross-subject analysis, we computed inter-parcel correlations in 20 participants, each of whom was scanned twice, as well as the consistency of the connectivity patterns inter- and intra-subject, and the intersession reproducibility. We report significant inter-parcel correlations consistent with previous findings, and high test-retest reliability, an important consideration when the goal is to compare clinical populations. As an example of the cross-modality analysis, correlation with anatomical connectivity is also examined., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Mouse models of Huntington's disease (HD) that recapitulate some of the phenotypic features of human HD, play a crucial role in investigating disease mechanisms and testing potential therapeutic approaches. Longitudinal studies of these models can yield valuable insights into the temporal course of disease progression and the effect of drug treatments on the progressive phenotypes. Atrophy of the brain, particularly the striatum, is a characteristic phenotype of human HD, is known to begin long before the onset of motor symptoms, and correlates strongly with clinical features. Elucidating the spatial and temporal patterns of atrophy in HD mouse models is important to characterize the phenotypes of these models, as well as evaluate the effects of neuroprotective treatments at specific time frames during disease progression. In this study, three dimensional in vivo magnetic resonance imaging (MRI) and automated longitudinal deformation-based morphological analysis was used to elucidate the spatial and temporal patterns of brain atrophy in the R6/2 and N171-82Q mouse models of HD. Using an established MRI-based brain atlas and mixed-effects modeling of deformation-based metrics, we report the rates of progression and region-specificity of brain atrophy in the two models. Further, the longitudinal analysis approach was used to evaluate the effects of sertraline and coenzyme Q(10) (CoQ(10)) treatments on progressive atrophy in the N171-82Q model. Sertraline treatment resulted in significant slowing of atrophy, especially in the striatum and frontal cortex regions, while no significant effects of CoQ(10) treatment were observed. Progressive cortical and striatal atrophy in the N171-82Q mice showed significant positive correlations with measured functional deficits. The findings of this report can be used for future testing and comparison of potential therapeutics in mouse models of HD., (Copyright © 2012 Elsevier Inc. All rights reserved.)

The inter-site and intra-site variability of system performance of MRI scanners (due to site-dependent and time-variant variations) can have significant adverse effects on the integration of multi-center DTI data. Measurement errors in accuracy and precision of each acquisition determine both the inter-site and intra-site variability. In this study, multiple scans of an identical isotropic diffusion phantom and of the brain of a traveling human volunteer were acquired at MRI scanners from the same vendor and with similar configurations at three sites. We assessed the feasibility of multi-center DTI studies by direct quantification of accuracy and precision of each dataset. Accuracy was quantified via comparison to carefully constructed gold standard datasets while precision (the within-scan variability) was estimated by wild bootstrap analysis. The results from both the phantom and human data suggest that the inter-site variation in system performance, although relatively small among scanners of the same vendor, significantly affects DTI measurement accuracy and precision and therefore the effectiveness for the integration of multi-center DTI measurements. Our results also highlight the value of a DTI-specific phantom in identifying and quantifying measurement errors due to site-dependent variations in the system performance, and its usefulness for quality assurance/quality control in multi-center DTI studies. In addition, we observed that the within-scan variability of each data acquisition, as assessed by wild bootstrap analysis, is of the same magnitude as the inter-site and intra-site variability. We propose that by weighing datasets based on their variability, as evaluated by wild bootstrap analysis, one can improve the quality of the dataset. This approach will provide a more effective integration of datasets from multi-center DTI studies., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Huntington's disease (HD) displays progressive striatal atrophy that occurs long before the onset of clinical motor symptoms. As there is no treatment for the disease once overt symptoms appear, it has been suggested that neuroprotective therapy given during this presymptomatic period might slow progression of the disease. This requires biomarkers that can reliably detect early changes and are sensitive to treatment response. In mouse models of HD, structural MRI measures have been shown to detect disease onset. To determine whether such measures could also be suitable biomarkers for following responses to treatment, we used T2-weighted MR imaging combined with automated morphological analyses and characterized changes in regional brain volumes longitudinally in the N171-82Q HD mouse model in a preclinical trial. We report here that N171-82Q HD mice exhibit adult-onset and progressive brain atrophy in the striatum and neocortex as well as in whole brain; the progressive atrophy in striatum and neocortex is positively correlated with motor deficits. Most notably, MRI also detected neuroprotective effects of sertraline treatment, a neuroprotective agent confirmed in our previous studies. Our present studies provide the first evidence that longitudinal structural MRI measures can detect the therapeutic effect in HD mice, suggesting that such measures in brain could be valuable biomarkers in HD clinical trials., (Copyright © 2011 Elsevier Inc. All rights reserved.)

MRI is a sensitive method for detecting subtle anatomic abnormalities in the neonatal brain. To optimize the usefulness for neonatal and pediatric care, systematic research, based on quantitative image analysis and functional correlation, is required. Normalization-based image analysis is one of the most effective methods for image quantification and statistical comparison. However, the application of this methodology to neonatal brain MRI scans is rare. Some of the difficulties are the rapid changes in T1 and T2 contrasts and the lack of contrast between brain structures, which prohibits accurate cross-subject image registration. Diffusion tensor imaging (DTI), which provides rich and quantitative anatomical contrast in neonate brains, is an ideal technology for normalization-based neonatal brain analysis. In this paper, we report the development of neonatal brain atlases with detailed anatomic information derived from DTI and co-registered anatomical MRI. Combined with a diffeomorphic transformation, we were able to normalize neonatal brain images to the atlas space and three-dimensionally parcellate images into 122 regions. The accuracy of the normalization was comparable to the reliability of human raters. This method was then applied to babies of 37-53 post-conceptional weeks to characterize developmental changes of the white matter, which indicated a posterior-to-anterior and a central-to-peripheral direction of maturation. We expect that future applications of this atlas will include investigations of the effect of prenatal events and the effects of preterm birth or low birth weights, as well as clinical applications, such as determining imaging biomarkers for various neurological disorders., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Modern MRI image processing methods have yielded quantitative, morphometric, functional, and structural assessments of the human brain. These analyses typically exploit carefully optimized protocols for specific imaging targets. Algorithm investigators have several excellent public data resources to use to test, develop, and optimize their methods. Recently, there has been an increasing focus on combining MRI protocols in multi-parametric studies. Notably, these have included innovative approaches for fusing connectivity inferences with functional and/or anatomical characterizations. Yet, validation of the reproducibility of these interesting and novel methods has been severely hampered by the limited availability of appropriate multi-parametric data. We present an imaging protocol optimized to include state-of-the-art assessment of brain function, structure, micro-architecture, and quantitative parameters within a clinically feasible 60-min protocol on a 3-T MRI scanner. We present scan-rescan reproducibility of these imaging contrasts based on 21 healthy volunteers (11 M/10 F, 22-61 years old). The cortical gray matter, cortical white matter, ventricular cerebrospinal fluid, thalamus, putamen, caudate, cerebellar gray matter, cerebellar white matter, and brainstem were identified with mean volume-wise reproducibility of 3.5%. We tabulate the mean intensity, variability, and reproducibility of each contrast in a region of interest approach, which is essential for prospective study planning and retrospective power analysis considerations. Anatomy was highly consistent on structural acquisition (~1-5% variability), while variation on diffusion and several other quantitative scans was higher (~<10%). Some sequences are particularly variable in specific structures (ASL exhibited variation of 28% in the cerebral white matter) or in thin structures (quantitative T2 varied by up to 73% in the caudate) due, in large part, to variability in automated ROI placement. The richness of the joint distribution of intensities across imaging methods can be best assessed within the context of a particular analysis approach as opposed to a summary table. As such, all imaging data and analysis routines have been made publicly and freely available. This effort provides the neuroimaging community with a resource for optimization of algorithms that exploit the diversity of modern MRI modalities. Additionally, it establishes a baseline for continuing development and optimization of multi-parametric imaging protocols., (Copyright © 2010 Elsevier Inc. All rights reserved.)

We have developed a new method to provide a comprehensive quantitative analysis of brain anatomy in cerebral palsy patients, which makes use of two techniques: diffusion tensor imaging and automated 3D whole brain segmentation based on our brain atlas and a nonlinear normalization technique (large-deformation diffeomorphic metric mapping). This method was applied to 13 patients and normal controls. The reliability of the automated segmentation revealed close agreement with the manual segmentation. We illustrate some potential applications for individual characterization and group comparison. This technique also provides a framework for determining the impact of various neuroanatomic features on brain functions., (Copyright © 2010 Elsevier Inc. All rights reserved.)

The advent of mammalian gene engineering and genetically modified mouse models has led to renewed interest in developing resources for referencing and quantitative analysis of mouse brain anatomy. In this study, we used diffusion tensor imaging (DTI) for quantitative characterization of anatomical phenotypes in the developing mouse brain. As an anatomical reference for neuroscience research using mouse models, this paper presents DTI based atlases of ex vivo C57BL/6 mouse brains at several developmental stages. The atlas complements existing histology and MRI-based atlases by providing users access to three-dimensional, high-resolution images of the developing mouse brain, with distinct tissue contrasts and segmentations of major gray matter and white matter structures. The usefulness of the atlas and database was demonstrated by quantitative measurements of the development of major gray matter and white matter structures. Population average images of the mouse brain at several postnatal stages were created using large deformation diffeomorphic metric mapping and their anatomical variations were quantitatively characterized. The atlas and database enhance our ability to examine the neuroanatomy in normal or genetically engineered mouse strains and mouse models of neurological diseases., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Tractography based on diffusion tensor imaging (DTI) is widely used to quantitatively analyze the status of the white matter anatomy in a tract-specific manner in many types of diseases. This approach, however, involves subjective judgment in the tract-editing process to extract only the tracts of interest. This process, usually performed by manual delineation of regions of interest, is also time-consuming, and certain tracts, especially the short cortico-cortical association fibers, are difficult to reconstruct. In this paper, we propose an automated approach for reconstruction of a large number of white matter tracts. In this approach, existing anatomical knowledge about tract trajectories (called the Template ROI Set or TRS) were stored in our DTI-based brain atlas with 130 three-dimensional anatomical segmentations, which were warped non-linearly to individual DTI data. We examined the degree of matching with manual results for selected fibers. We established 30 TRSs to reconstruct 30 prominent and previously well-described fibers. In addition, TRSs were developed to delineate 29 short association fibers that were found in all normal subjects examined in this paper (N=20). Probabilistic maps of the 59 tract trajectories were created from the normal subjects and were incorporated into our image analysis tool for automated tract-specific quantification., (Copyright 2010 Elsevier Inc. All rights reserved.)

Quantification of normal brain maturation is a crucial step in understanding developmental abnormalities in brain anatomy and function. The aim of this study was to develop atlas-based tools for time-dependent quantitative image analysis, and to characterize the anatomical changes that occur from 2years of age to adulthood. We used large deformation diffeomorphic metric mapping to register diffusion tensor images of normal participants into the common coordinates and used a pre-segmented atlas to segment the entire brain into 176 structures. Both voxel- and atlas-based analyses reported a structure that showed distinctive changes in terms of its volume and diffusivity measures. In the white matter, fractional anisotropy (FA) linearly increased with age in logarithmic scale, while diffusivity indices, such as apparent diffusion coefficient (ADC), and axial and radial diffusivity, decreased at a different rate in several regions. The average, variability, and the time course of each measured parameter are incorporated into the atlas, which can be used for automated detection of developmental abnormalities. As a demonstration of future application studies, the brainstem anatomy of cerebral palsy patients was evaluated and the altered anatomy was delineated., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

Diffusion-tensor-imaging fiber tractography enables interrogation of brain white matter tracts that subserve different functions. However, tract reconstruction can be labor and time intensive and can yield variable results that may reduce the power to link imaging abnormalities with disability. Automated segmentation of these tracts would help make tract-specific imaging clinically useful, but implementation of such segmentation is problematic in the presence of diseases that alter brain structure. In this work, we investigated an automated tract-probability-mapping scheme and applied it to multiple sclerosis, comparing the results to those derived from conventional tractography. We found that the automated method has consistently lower scan-rescan variability (typically 0.7-1.5% vs. up to 3% for conventional tractography) and avoids problems related to tractography failures within and around lesions. In the corpus callosum, optic radiation, and corticospinal tract, tract-specific MRI indices calculated by the two methods were moderately to strongly correlated, though systematic, tract-specific differences were present. In these tracts, the two methods also yielded similar correlation coefficients relating tract-specific MRI indices to clinical disability scores. In the optic tract, the automated method failed. With judicious application, therefore, the automated method may be useful for studies that investigate the relationship between imaging findings and clinical outcomes in disease., (Published by Elsevier Inc.)

Mouse models of human diseases play crucial roles in understanding disease mechanisms and developing therapeutic measures. Huntington's disease (HD) is characterized by striatal atrophy that begins long before the onset of motor symptoms. In symptomatic HD, striatal volumes decline predictably with disease course. Thus, imaging based volumetric measures have been proposed as outcomes for presymptomatic as well as symptomatic clinical trials of HD. Magnetic resonance imaging of the mouse brain structures is becoming widely available and has been proposed as one of the biomarkers of disease progression and drug efficacy testing. However, three-dimensional and quantitative morphological analyses of the brains are not straightforward. In this paper, we describe a tool for automated segmentation and voxel-based morphological analyses of the mouse brains. This tool was applied to a well-established mouse model of Huntington's disease, the R6/2 transgenic mouse strain. Comparison between the automated and manual segmentation results showed excellent agreement in most brain regions. The automated method was able to sensitively detect atrophy as early as 4 weeks of age and accurately follow disease progression. Comparison between ex vivo and in vivo MRI suggests that the ex vivo end-point measurement of brain morphology is also a valid approach except for the morphology of the ventricles. This is the first report of longitudinal characterization of brain atrophy in a mouse model of Huntington's disease by using automatic morphological analysis., (Copyright (c) 2009 Elsevier Inc. All rights reserved.)

Diffusion tensor imaging (DTI) can reveal detailed white matter anatomy and has the potential to detect abnormalities in specific white matter structures. Such detection and quantification are, however, not straightforward. The voxel-based analysis after image normalization is one of the most widely used methods for quantitative image analyses. To apply this approach to DTI, it is important to examine if structures in the white matter are well registered among subjects, which would be highly dependent on employed algorithms for normalization. In this paper, we evaluate the accuracy of normalization of DTI data using a highly elastic transformation algorithm, called large deformation diffeomorphic metric mapping. After simulation-based validation of the algorithm, DTI data from normal subjects were used to measure the registration accuracy. To examine the impact of morphological abnormalities on the accuracy, the algorithm was also tested using data from Alzheimer's disease (AD) patients with severe brain atrophy. The accuracy level was measured by using manual landmark-based white matter matching and surface-based brain and ventricle matching as gold standard. To improve the accuracy level, cascading and multi-contrast approaches were developed. The accuracy level for the white matter was 1.88+/-0.55 and 2.19+/-0.84 mm for the measured locations in the controls and patients, respectively.

The purpose of this paper is to establish single-participant white matter atlases based on diffusion tensor imaging. As one of the applications of the atlas, automated brain segmentation was performed and the accuracy was measured using Large Deformation Diffeomorphic Metric Mapping (LDDMM). High-quality diffusion tensor imaging (DTI) data from a single-participant were B0-distortion-corrected and transformed to the ICBM-152 atlas or to Talairach coordinates. The deep white matter structures, which have been previously well documented and clearly identified by DTI, were manually segmented. The superficial white matter areas beneath the cortex were defined, based on a population-averaged white matter probability map. The white matter was parcellated into 176 regions based on the anatomical labeling in the ICBM-DTI-81 atlas. The automated parcellation was achieved by warping this parcellation map to normal controls and to Alzheimer's disease patients with severe anatomical atrophy. The parcellation accuracy was measured by a kappa analysis between the automated and manual parcellation at 11 anatomical regions. The kappa values were 0.70 for both normal controls and patients while the inter-rater reproducibility was 0.81 (controls) and 0.82 (patients), suggesting "almost perfect" agreement. A power analysis suggested that the proposed method is suitable for detecting FA and size abnormalities of the white matter in clinical studies.

Although DTI can provide detailed information about white matter anatomy, it is not yet straightforward enough to quantify the anatomical information it visualizes. In this study, we developed and tested a new tool to perform brain normalization and voxel-based analysis of DTI data. For the normalization part, manually placed landmarks ensured that the visualized white matter tracts were well-registered among the populations. A standard landmark set in ICBM-152 space and an interface to remap them to subject data were integrated in the procedure. After landmark placement, highly elastic non-linear Large Deformation Diffeomorphic Metric Mapping (LDDMM) was driven by the landmarks to normalize the brainstem anatomy of normal subjects. The approach was then applied to delineate brainstem tract abnormalities in patients with left chronic middle cerebral artery (MCA) stroke. The voxel-based comparison between control and patient groups identified abnormalities in the ipsilesional corticospinal tract and contralesional cerebellar peduncles. We believe that this tool is useful for regional brain normalization of patients with severe anatomical alterations, such as stroke, brain tumor, and lobectomy, for whom standard automated normalization tools may not work properly.

Structural delineation and assignment are the fundamental steps in understanding the anatomy of the human brain. The white matter has been structurally defined in the past only at its core regions (deep white matter). However, the most peripheral white matter areas, which are interleaved between the cortex and the deep white matter, have lacked clear anatomical definitions and parcellations. We used axonal fiber alignment information from diffusion tensor imaging (DTI) to delineate the peripheral white matter, and investigated its relationship with the cortex and the deep white matter. Using DTI data from 81 healthy subjects, we identified nine common, blade-like anatomical regions, which were further parcellated into 21 subregions based on the cortical anatomy. Four short association fiber tracts connecting adjacent gyri (U-fibers) were also identified reproducibly among the healthy population. We anticipate that this atlas will be useful resource for atlas-based white matter anatomical studies.

Reconstruction of white matter tracts based on diffusion tensor imaging (DTI) is currently widely used in clinical research. This reconstruction allows us to identify coordinates of specific white matter tracts and to investigate their anatomy. Fiber reconstruction, however, relies on manual identification of anatomical landmarks of a tract of interest, which is based on subjective judgment and thus a potential source of experimental variability. Here, an automated tract reconstruction approach is introduced. A set of reference regions of interest (rROIs) known to select a tract of interest was marked in our DTI brain atlas. The atlas was then linearly transformed to each subject, and the rROI set was transferred to the subject for tract reconstruction. Agreement between the automated and manual approaches was measured for 11 tracts in 10 healthy volunteers and found to be excellent (kappa>0.8) and remained high up to 4-5 mm of the linear transformation errors. As a first example, the automated approach was applied to brain tumor patients and strategies to cope with severe anatomical abnormalities are discussed.

Brain registration to a stereotaxic atlas is an effective way to report anatomic locations of interest and to perform anatomic quantification. However, existing stereotaxic atlases lack comprehensive coordinate information about white matter structures. In this paper, white matter-specific atlases in stereotaxic coordinates are introduced. As a reference template, the widely used ICBM-152 was used. The atlas contains fiber orientation maps and hand-segmented white matter parcellation maps based on diffusion tensor imaging (DTI). Registration accuracy by linear and non-linear transformation was measured, and automated template-based white matter parcellation was tested. The results showed a high correlation between the manual ROI-based and the automated approaches for normal adult populations. The atlases are freely available and believed to be a useful resource as a target template and for automated parcellation methods.

Diffusion tensor imaging (DTI) is an exciting new MRI modality that can reveal detailed anatomy of the white matter. DTI also allows us to approximate the 3D trajectories of major white matter bundles. By combining the identified tract coordinates with various types of MR parameter maps, such as T2 and diffusion properties, we can perform tract-specific analysis of these parameters. Unfortunately, 3D tract reconstruction is marred by noise, partial volume effects, and complicated axonal structures. Furthermore, changes in diffusion anisotropy under pathological conditions could alter the results of 3D tract reconstruction. In this study, we created a white matter parcellation atlas based on probabilistic maps of 11 major white matter tracts derived from the DTI data from 28 normal subjects. Using these probabilistic maps, automated tract-specific quantification of fractional anisotropy and mean diffusivity were performed. Excellent correlation was found between the automated and the individual tractography-based results. This tool allows efficient initial screening of the status of multiple white matter tracts.

Background/purpose: Muscle weakness is an important feature of multiple sclerosis and is responsible for much of the disability associated with that condition. Here, we describe the quantitative magnetic resonance imaging (MRI) attributes of the major intracerebral motor pathway--the corticospinal tract--in multiple sclerosis. To do so, we develop an intuitive method for creating and displaying spatially normalized tract-specific imaging data., Methods: In 75 individuals with multiple sclerosis and 29 healthy controls, the corticospinal tracts were reconstructed from diffusion tensor imaging at 3 T. Multiple MRI indices--T2 relaxation time; fractional anisotropy; mean, longitudinal, and transverse diffusivity; and magnetization transfer ratio--were examined within the reconstructed tracts. Spatially normalized tract profiles were created to compare, across subjects, the variation in MRI index as a function of tract position., Results: Each index's tract profile had a characteristic shape. Individual subjects had markedly abnormal tract profiles, particularly at lesion sites. On average, tract profiles were different between patients and controls, particularly in the subcortical white matter and corona radiata, for all indices examined except for fractional anisotropy. Magnetization transfer ratio was further decreased in subjects with secondary progressive disease. Tract asymmetry was increased in multiple sclerosis compared to controls., Conclusion: Multiparametric MRI allows rapid detection, localization, and characterization of tract-specific abnormalities in multiple sclerosis. Tract profiles bridge the gap between whole-brain imaging of neurological disease and the interrogation of individual, functionally relevant subsystems.

While the majority of axonal organization is established by birth in mammalian brains, axonal wiring and pruning processes, as well as myelination, are known to extend to the postnatal periods, where environmental stimuli often play a major role. Normal axonal and myelin development of individual white matter tracts of human in this period is poorly understood and may have a major role in cognitive development of human. In this study, we applied diffusion tensor imaging and normalization-based population analyses to 44 preteen children and 30 adult images. We observed highly significant changes of fiber orientations at regions that correspond to the superior longitudinal fasciculus during the first 5 years. The result is attributed to slow axonal and/or myelin maturation of this tract, which is believed to be involved in language functions.

Diffusion tensor imaging (DTI) is used to study tissue composition and architecture in vivo. To increase the signal to noise ratio (SNR) of DTI contrasts, studies typically use more than the minimum of 6 diffusion weighting (DW) directions or acquire repeated observations of the same set of DW directions. Simulation-based studies have sought to optimize DTI acquisitions and suggest that increasing the directional resolution of a DTI dataset (i.e., the number of distinct directions) is preferable to repeating observations, in an equal scan time comparison. However, it is not always clear how to translate these recommendations into practice when considering physiological noise and scanner stability. Furthermore, the effect of different DW schemes on in vivo DTI findings is not fully understood. This study characterizes how the makeup of a DW scheme, in terms of the number of directions, impacts the precision and accuracy of in vivo fractional anisotropy (FA), mean diffusivity (MD), and principal eigenvector (PEV) findings. Orientation dependence of DTI reliability is demonstrated in vivo and a principled theoretical framework is provided to support and interpret findings with simulation results. As long as sampling orientations are well balanced, differences in DTI contrasts due to different DW schemes are shown to be small relative to intra-session variability. These differences are accentuated at low SNR, while minimized at high SNR. This result suggests that typical clinical studies, which use similar protocols but different well-balanced DW schemes, are readily comparable within the experimental precision.

Tractography based on diffusion tensor imaging (DTI) allows visualization of white matter tracts. In this study, protocols to reconstruct eleven major white matter tracts are described. The protocols were refined by several iterations of intra- and inter-rater measurements and identification of sources of variability. Reproducibility of the established protocols was then tested by raters who did not have previous experience in tractography. The protocols were applied to a DTI database of adult normal subjects to study size, fractional anisotropy (FA), and T2 of individual white matter tracts. Distinctive features in FA and T2 were found for the corticospinal tract and callosal fibers. Hemispheric asymmetry was observed for the size of white matter tracts projecting to the temporal lobe. This protocol provides guidelines for reproducible DTI-based tract-specific quantification.

Unlabelled: We evaluated intra-rater, inter-rater, and between-scan reproducibility, hemispheric differences, and the effect of age on apparent diffusion coefficient (ADC) and fractional anisotropy (FA) in healthy children (age range 5.5-19.1 years) examined with a clinical diffusion tensor imaging (DTI) protocol at 1.5 T, using a region of interest (ROI) methodology. Measures of reliability and precision were assessed in six ROIs using two different ROI shapes (polygonal and ellipsoidal)., Results: Highly reproducible values of ADC and FA were obtained with the polygonal method on intra-rater (coefficients of variation

Brain anatomy is characterized by dramatic growth from the end of the second trimester through the neonatal stage. The characterization of normal axonal growth of the white matter tracts has not been well-documented to date and could provide important clues to understanding the extensive inhomogeneity of white matter injuries in cerebral palsy (CP) patients. However, anatomical studies of human brain development during this period are surprisingly scarce and histology-based atlases have become available only recently. Diffusion tensor magnetic resonance imaging (DTMRI) can reveal detailed anatomy of white matter. We acquired diffusion tensor images (DTI) of postmortem fetal brain samples and in vivo neonates and children. Neural structures were annotated in two-dimensional (2D) slices, segmented, measured, and reconstructed three-dimensionally (3D). The growth status of various white matter tracts was evaluated on cross-sections at 19-20 gestational weeks, and compared with 0-month-old neonates and 5- to 6-year-old children. Limbic, commissural, association, and projection white matter tracts and gray matter structures were illustrated in 3D and quantitatively characterized to assess their dynamic changes. The overall pattern of the time courses for the development of different white matter is that limbic fibers develop first and association fibers last and commissural and projection fibers are forming from anterior to posterior part of the brain. The resultant DTMRI-based 3D human brain data will be a valuable resource for human brain developmental study and will provide reference standards for diagnostic radiology of premature newborns.

Recent advances in diffusion tensor imaging (DTI) have made it possible to reveal white matter anatomy and to detect neurological abnormalities in children. However, the clinical use of this technique is hampered by the lack of a normal standard of reference. The goal of this study was to initiate the establishment of a database of DTI images in children, which can be used as a normal standard of reference for diagnosis of pediatric neurological abnormalities. Seven pediatric volunteers and 23 pediatric patients (age range: 0-54 months) referred for clinical MR examinations, but whose brains were shown to be normal, underwent anatomical and DTI acquisitions on a 1.5 T MR scanner. The white matter maturation, as observed on DTI color maps, was described and illustrated. Changes in diffusion fractional anisotropy (FA), average apparent diffusion constant (ADC(ave)), and T2-weighted (T2W) signal intensity were quantified in 12 locations to characterize the anatomical variability of the maturation process. Almost all prominent white matter tracts could be identified from birth, although their anisotropy was often low. The evolution of FA, shape, and size of the white matter tracts comprised generally three phases: rapid changes during the first 12 months; slow modifications during the second year; and relative stability after 24 months. The time courses of FA, ADC(ave), and T2W signal intensity confirmed our visual observations that maturation of the white matter and the normality of its architecture can be assessed with DTI in young children. The database is available online and is expected to foster the use of this promising technique in the diagnosis of pediatric pathologies.

While mouse brain development has been extensively studied using histology, quantitative characterization of morphological changes is still a challenging task. This paper presents how developing brain structures can be quantitatively characterized with magnetic resonance diffusion tensor microimaging coupled with techniques of computational anatomy. High resolution diffusion tensor images of ex vivo postnatal mouse brains provide excellent contrasts to reveal the evolutions of mouse forebrain structures. Using anatomical landmarks defined on diffusion tensor images, tissue level growth patterns of mouse brains were quantified. The results demonstrate the use of these techniques to three-dimensionally and quantitatively characterize brain growth.

Morphology of the corpus callosum (CC) at the mid-sagittal level has been a target of extensive studies. However, the lack of internal structures and its polymorphism make it a challenging task to quantitatively analyze shape differences among subjects. In this paper, diffusion tensor Imaging (DTI) and tract tracing technique were applied to incorporate cortical connectivity information to the morphological study. The CC was parcellated into six major subdivisions based on trajectories to different cortical areas. This subdivision was performed for eight normal subjects and one stroke patient. The parcellated CCs of the normal subjects were normalized for morphological analysis. When comparing the stroke patient to the normal population, we detected significant atrophy in the motor and sensory areas of the patient CC, in line with the clinical deficits. This approach provides a new tool to investigate callosal morphology and functional relationships.

Diffusion tensor imaging (DTI) was applied to 12 patients with focal cortical dysplasia (FCD) in frontal or occipital cortex. Fiber tractography was obtained from seeding points in superior longitudinal fasciculus or posterior corona radiata. Mean fractional anisotropy of fiber bundles around the affected cortex was decreased in comparison to the contralateral hemisphere with statistical significance (paired t test, P = 0.0274). On visual analysis, tractography depicted decreased volume of fiber bundles connected to the dysplastic cortex invariably even in those with a normal T2 signal intensity of underlying white matter adjacent to FCD. DTI has high potential to be applied to localize the FCD and to provide a better understanding of the pathological changes in the white matter.

Investigation of three-dimensional (3D) morphometry of developing brains has been hindered by a lack of imaging modalities that can monitor the 3D evolution of various anatomical structures without sectioning and staining processes. In this study, we combined magnetic resonance microimaging and diffusion tensor imaging techniques to accomplish such visualization. The application of this approach to developing mouse embryos revealed that it could clearly delineate early critical structures such as neuroepithelium, cortical plate, and various axonal structures, and follow their developmental evolution. The technique was applied to the study of the Netrin-1 mutant, allowing verification of its anatomical phenotype.

Recent studies suggested that functional activation using apparent diffusion coefficient (ADC) contrast can be used to detect synchronized functional MRI (fMRI) signal changes during brain activation. Such changes may reflect better spatial localization to the smaller vessels, which are closely coupled to the true neuronal activation. Since it is generally believed that there are neural pathways among neuronally relevant areas, methods that would allow clear delineation of such pathways could help validate the neuronal relevance of the activated functional areas. The development of diffusion tensor imaging (DTI) has shown promise in detailed nerve fiber tracking. In this report, DTI was adopted to track the fiber connections among the discrete areas determined using the ADC contrast, in an effort to confirm the neuronal origin of these activated areas. As a comparison, activated areas using blood oxygenation level-dependent (BOLD) contrast were also obtained. Our results showed that the areas determined by the ADC contrast consistently allowed better fiber tracking within, while the BOLD-activated areas were more spatially diffused due to the smearing effect of brain vasculature, rendering the task of fiber tracking more difficult. This observation provides converging evidence that the activated areas using ADC contrast are more closely coupled to the neuronal activity than those using BOLD contrast.