Yundi Shi

The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery

Diffusion tensor imaging (DTI) tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography‐derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop.

A diffusion tensor MRI atlas of the postmortem rhesus macaque brain.

The rhesus macaque (Macaca mulatta) is the most widely used nonhuman primate for modeling the structure and function of the brain. Brain atlases, and particularly those based on magnetic resonance imaging (MRI), have become important tools for understanding normal brain structure, and for identifying structural abnormalities resulting from disease states, exposures, and/or aging. Diffusion tensor imaging (DTI)-based MRI brain atlases are widely used in both human and macaque brain imaging studies because of the unique contrasts, quantitative diffusion metrics, and diffusion tractography that they can provide. Previous MRI and DTI atlases of the rhesus brain have been limited by low contrast and/or low spatial resolution imaging. Here we present a microscopic resolution MRI/DTI atlas of the rhesus brain based on 10 postmortem brain specimens. The atlas includes both structural MRI and DTI image data, a detailed three-dimensional segmentation of 241 anatomic structures, diffusion tractography, cortical thickness estimates, and maps of anatomic variability among atlas specimens. This atlas incorporates many useful features from previous work, including anatomic label nomenclature and ontology, data orientation, and stereotaxic reference frame, and further extends prior analyses with the inclusion of high-resolution multi-contrast image data.

Diffusion Tensor Imaging–Based Characterization of Brain Neurodevelopment in Primates

Primate neuroimaging provides a critical opportunity for understanding neurodevelopment. Yet the lack of a normative description has limited the direct comparison with changes in humans. This paper presents for the first time a cross-sectional diffusion tensor imaging (DTI) study characterizing primate brain neurodevelopment between 1 and 6 years of age on 25 healthy undisturbed rhesus monkeys (14 male, 11 female). A comprehensive analysis including region-of-interest, voxel-wise, and fiber tract-based approach demonstrated significant changes of DTI properties over time. Changes in fractional anisotropy (FA), mean diffusivity, axial diffusivity (AD), and radial diffusivity (RD) exhibited a heterogeneous pattern across different regions as well as along fiber tracts. Most of these patterns are similar to those from human studies yet a few followed unique patterns. Overall, we observed substantial increase in FA and AD and a decrease in RD for white matter (WM) along with similar yet smaller changes in gray matter (GM). We further observed an overall posterior-to-anterior trend in DTI property changes over time and strong correlations between WM and GM development. These DTI trends provide crucial insights into underlying age-related biological maturation, including myelination, axonal density changes, fiber tract reorganization, and synaptic pruning processes.

UNC-Utah NA-MIC framework for DTI fiber tract analysis

Diffusion tensor imaging has become an important modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While there exists a number of tractography toolsets, these usually lack tools for preprocessing or to analyze diffusion properties along the fiber tracts. Currently, the field is in critical need of a coherent end-to-end toolset for performing an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. We illustrate the use of our framework on a small sample, cross sectional neuroimaging study of eight healthy 1-year-old children from the Infant Brain Imaging Study (IBIS) Network. In this limited test study, we illustrate the power of our method by quantifying the diffusion properties at 1 year of age on the genu and splenium fiber tracts.

Longitudinal Image Registration With Temporally-Dependent Image Similarity Measure

Longitudinal imaging studies are frequently used to investigate temporal changes in brain morphology and often require spatial correspondence between images achieved through image registration. Beside morphological changes, image intensity may also change over time, for example when studying brain maturation. However, such intensity changes are not accounted for in image similarity measures for standard image registration methods. Hence, 1) local similarity measures, 2) methods estimating intensity transformations between images, and 3) metamorphosis approaches have been developed to either achieve robustness with respect to intensity changes or to simultaneously capture spatial and intensity changes. For these methods, longitudinal intensity changes are not explicitly modeled and images are treated as independent static samples. Here, we propose a model-based image similarity measure for longitudinal image registration that estimates a temporal model of intensity change using all available images simultaneously.

Simple geodesic regression for image time-series

Geodesic regression generalizes linear regression to general Riemannian manifolds. Applied to images, it allows for a compact approximation of an image time-series through an initial image and an initial momentum. Geodesic regression requires the definition of a squared residual (squared distance) between the regression geodesic and the measurement images. In principle, this squared distance should also be defined through a geodesic connecting an image on the regression geodesic to its respective measurement. However, in practice only standard registration distances (such as sum of squared distances) are used, to reduce computation time. This paper describes a simplified geodesic regression method which approximates the registration-based distances with respect to a fixed initial image. This results in dramatically simplified computations. In particular, the method becomes straightforward to implement using readily available large displacement diffeomorphic metric mapping (LDDMM) shooting algorithms and decouples the problem into pairwise image registrations allowing parallel computations. We evaluate the approach using 2D synthetic images and real 3D brain images.

Maternal buffering beyond glucocorticoids: impact of early life stress on corticolimbic circuits that control infant responses to novelty

ABSTRACT Maternal presence has a potent buffering effect on infant fear and stress responses in primates. We previously reported that maternal presence is not effective in buffering the endocrine stress response in infant rhesus monkeys reared by maltreating mothers. We have also reported that maltreating mothers show low maternal responsiveness and permissiveness/secure-base behavior. Although still not understood, it is possible that this maternal buffering effect is mediated, at least partially, through deactivation of amygdala response circuits when mothers are present. Here, we studied rhesus monkey infants that differed in the quality of early maternal care to investigate how this early experience modulated maternal buffering effects on behavioral responses to novelty during the weaning period. We also examined the relationship between these behavioral responses and structural connectivity in one of the underlying regulatory neural circuits: amygdala-prefrontal pathways. Our findings suggest that infant exploration in a novel situation is predicted by maternal responsiveness and structural integrity of amygdala-prefrontal white matter depending on maternal presence (positive relationships when mother is absent). These results provide evidence that maternal buffering of infant behavioral inhibition is dependent on the quality of maternal care and structural connectivity of neural pathways that are sensitive to early life stress.

Group-wise cortical correspondence via sulcal curve-constrained entropy minimization

We present a novel cortical correspondence method employing group-wise registration in a spherical parametrization space for the use in local cortical thickness analysis in human and non-human primate neuroimaging studies. The proposed method is unbiased registration that estimates a continuous smooth deformation field into an unbiased average space via sulcal curve-constrained entropy minimization using spherical harmonic decomposition of the spherical deformation field. We initialize a correspondence by our pair-wise method that establishes a surface correspondence with a prior template. Since this pair-wise correspondence is biased to the choice of a template, we further improve the correspondence by employing unbiased ensemble entropy minimization across all surfaces, which yields a deformation field onto the iteratively updated unbiased average. The specific entropy metric incorporates two terms: the first focused on optimizing the correspondence of automatically extracted sulcal landmarks and the second on that of sulcal depth maps. We also propose an encoding scheme for spherical deformation via spherical harmonics as well as a novel method to choose an optimal spherical polar coordinate system for the most efficient deformation field estimation. The experimental results show evidence that the proposed method improves the correspondence quality in non-human primate and human subjects as compared to the pair-wise method.

The UNC-Wisconsin Rhesus Macaque Neurodevelopment Database: A Structural MRI and DTI Database of Early Postnatal Development

Rhesus macaques are commonly used as a translational animal model in neuroimaging and neurodevelopmental research. In this report, we present longitudinal data from both structural and diffusion MRI images generated on a cohort of 34 typically developing monkeys from 2 weeks to 36 months of age. All images have been manually skull stripped and are being made freely available via an online repository for use by the research community.

UNC-Emory Infant Atlases for Macaque Brain Image Analysis: Postnatal Brain Development through 12 Months.

Computational anatomical atlases have shown to be of immense value in neuroimaging as they provide age appropriate reference spaces alongside ancillary anatomical information for automated analysis such as subcortical structural definitions, cortical parcellations or white fiber tract regions. Standard workflows in neuroimaging necessitate such atlases to be appropriately selected for the subject population of interest. This is especially of importance in early postnatal brain development, where rapid changes in brain shape and appearance render neuroimaging workflows sensitive to the appropriate atlas choice. We present here a set of novel computation atlases for structural MRI and Diffusion Tensor Imaging as crucial resource for the analysis of MRI data from non-human primate rhesus monkey (Macaca mulatta) data in early postnatal brain development. Forty socially-housed infant macaques were scanned longitudinally at ages 2 weeks, 3, 6, and 12 months in order to create cross-sectional structural and DTI atlases via unbiased atlas building at each of these ages. Probabilistic spatial prior definitions for the major tissue classes were trained on each atlas with expert manual segmentations. In this article we present the development and use of these atlases with publicly available tools, as well as the atlases themselves, which are publicly disseminated to the scientific community.