Computational anatomy to assess longitudinal trajectory of brain growth

journal articleThis paper addresses the challenging problem of statistics on images by describing average and variability. We describe computational anatomy tools for building 3-D and spatio-temporal 4-D atlases of volumetric image data. The method is based on the previously published concept of unbiased atlas building, calculating the nonlinear average image of a population of images by simultaneous nonlinear deformable registration. Unlike linear averaging, the resulting center average image is sharp and encodes the average structure and geometry of the whole population. Variability is encoded in the set of deformation maps. As a new extension, longitudinal change is assessed by quantifying local deformation between atlases taken at consecutive time points. Morphological differences between groups are analyzed by the same concept but comparing group-specific atlases. Preliminary tests demonstrate that the atlas building shows excellent robustness and a very good convergence, i.e. atlases start to stabilize after 5 images only and do not show significant changes when including more than 10 volumetric images taken from the same population

Group statistics of DTI fiber bundles using spatial functions of tensor measures

pre-printWe present a framework for hypothesis testing of differences between groups of DTI ber tracts. An anatomical, tract-oriented coordinate system provides a basis for estimating the distribution of diffusion properties. The parametrization of sampled, smooth functions is normalized across a population using DTI atlas building. Functional data analysis, an extension of multivariate statistics to continuous functions is applied to the problem of hypothesis testing and discrimination. B-spline models of fractional anisotropy (FA) and Frobenius norm measures are analyzed jointly. Plots of the discrimination direction provide a clinical interpretation of the group differences. The methodology is tested on a pediatric study of subjects aged one and two years

Noise-induced bias in low-direction diffusion tensor MRI: replication of Monte-Carlo simulation with in-vivo scans

pre-printClinical neuroimaging studies involving diffusion tensor MRI (DTI) require precise estimation of diffusion properties for statistical analysis. Tensor derived measures such as fractional anisotropy (FA) and trace are often analyzed for hypothesis testing. Given the limited scanning time typically available for studies, careful selection of scanning protocols and processing methods is essential for accurate and precise measurements. Analysis of the effect of MR noise on tensor derived measures has shown a negative bias in FA when the principal eigenvector of tensor is aligned with a gradient direction [1][2][3]. This bias can lead to a correlation between FA and tensor orientation in protocols with low numbers of gradient directions. We extend the results of simulation experiments by showing evidence of predicted anisotropy bias in in-vivo data

Population-based fitting of medial shape models with correspondence optimization

pre-printA crucial problem in statistical shape analysis is establishing the correspondence of shape features across a population. While many solutions are easy to express using boundary representations, this has been a considerable challenge for medial representations. This paper uses a new 3-D medial model that allows continuous interpolation of the medial manifold and provides a map back and forth between it and the boundary. A measure defined on the medial surface then allows one to write integrals over the boundary and the object interior in medial coordinates, enabling the expression of important object properties in an object-relative coordinate system.We use these integrals to optimize correspondence during model construction, reducing variability due to the model parameterization that could potentially mask true shape change effects. Discrimination and hypothesis testing of populations of shapes are expected to benefit, potentially resulting in improved significance of shape differences between populations even with a smaller sample size

Theology, News and Notes - Vol. 05, No. 03

Theology News & Notes was a theological journal published by Fuller Theological Seminary from 1954 through 2014.https://digitalcommons.fuller.edu/tnn/1011/thumbnail.jp

Model selection for spatiotemporal modeling of early childhood sub-cortical development

Spatiotemporal shape models capture the dynamics of shape change over time and are an essential tool for monitoring and measuring anatomical growth or degeneration. In this paper we evaluate non-parametric shape regression on the challenging problem of modeling early childhood sub-cortical development starting from birth. Due to the flexibility of the model, it can be challenging to choose parameters which lead to a good model fit yet does not overfit. We systematically test a variety of parameter settings to evaluate model fit as well as the sensitivity of the method to specific parameters, and we explore the impact of missing data on model estimation

Group analysis of DTI fiber tract statistics with application to neurodevelopment

Diffusion tensor imaging (DTI) provides a unique source of information about the underlying tissue structure of brain white matter in vivo including both the geometry of major fiber bundles as well as quantitative information about tissue properties represented by derived tensor measures. This paper presents a method for statistical comparison of fiber bundle diffusion properties between populations of diffusion tensor images. Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics. Diffusion properties, such as fractional anisotropy (FA) and tensor norm, along fiber tracts are modeled as multivariate functions of arc length. Hypothesis testing is performed non-parametrically using permutation testing based on the Hotelling T2 statistic. The linear discriminant embedded in the T2 metric provides an intuitive, localized interpretation of detected differences. The proposed methodology was tested on two clinical studies of neurodevelopment. In a study of one and two year old subjects, a significant increase in FA and a correlated decrease in Frobenius norm was found in several tracts. Significant differences in neonates were found in the splenium tract between controls and subjects with isolated mild ventriculomegaly (MVM) demonstrating the potential of this method for clinical studies

Contaminant biotransport by Pacific salmon to Lake Michigan tributaries

The Great Lakes are ideal systems for evaluating the synergistic components of environmental change, such as exotic species introductions and legacy pollutants. Introduced Pacific Salmon (Oncorhynchus spp.) represent an intersection of these drivers because they are non-native species of economic importance that bioaccumulate contaminants during the open water phase of their life cycle. Furthermore, Pacific salmon can deliver a significant pulse of contaminated tissue to tributaries during spawning and subsequent death. Thus, salmon represent a key pathway by which contaminants accumulated in Lake Michigan are transported inland to tributaries that otherwise lack point source pollution. Our research has revealed that salmon exhibit basin-specific persistent organic pollutant (POP) and mercury (Hg) concentrations reflecting pollutant inputs from both current and historic sources. Overall, Lake Michigan salmon were more contaminated with POPs and Hg than conspecifics from Lakes Huron or Superior. Consequently, Lake Michigan salmon pose a higher risk and magnitude of contaminant biotransport and transfer. Resident stream fish (e.g., brook trout) sampled from salmon spawning reaches had higher pollutant concentrations than fish sampled from upstream reaches lacking salmon, but the extent of fish contamination varied among lake basins and streams. In general, Lake Michigan tributaries were the most impacted, suggesting a direct relationship between the extent of salmon-derived contaminant inputs and resident fish contaminant levels. Within and among lake basins, contaminant biotransport by salmon is context dependent and likely reflects a suite of ecological characteristics such as species identity and trophic position, dynamics of the salmon run, watershed land-use, and instream geomorphology such as sediment size. We suggest that future management of salmon-mediated contaminant biotransport to stream communities in the Great Lakes basin should consider biological, chemical, and physical factors that constitute the environmental context