Purpose: The UNC-Utah NA-MIC DTI framework represents a coherent, open source, atlas fiber tract based DTI
analysis framework that addresses the lack of a standardized fiber tract based DTI analysis workflow in the field. Most
steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for
non-technical researchers/investigators. Data: We illustrate the use of our framework on a 54 directional DWI
neuroimaging study contrasting 15 Smokers and 14 Controls. Method(s): At the heart of the framework is a set of tools anchored around the multi-purpose image analysis platform 3D-Slicer. Several workflow steps are handled via external modules called from Slicer in order to provide an integrated approach. Our workflow starts with conversion from DICOM, followed by thorough automatic and interactive quality control (QC), which is a must for a good DTI study. Our framework is centered around a DTI atlas that is either provided as a template or computed directly as an unbiased average atlas from the study data via deformable atlas building. Fiber tracts are defined via interactive tractography and clustering on that atlas. DTI fiber profiles are extracted automatically using the atlas mapping information. These tract parameter profiles are then analyzed using our statistics toolbox (FADTTS). The statistical results are then mapped back on to the fiber bundles and visualized with 3D Slicer. Results: This framework provides a coherent set of tools for DTI quality control and analysis. Conclusions: This framework will provide the field with a uniform process for DTI quality control and analysis.
Diffusion tensor imaging (DTI) could provide convenient and crucial insights into the underlying age-related biological
maturation of human brains, including myelination, axonal density changes, fiber tract reorganization, and synaptic
pruning processes. Fractional anisotropy (FA) derived from DTI has been commonly used to characterize cellular
morphological changes associated with the development of human brain, due to its sensitivity to microstructural
changes. In this paper, we aim to discern the longitudinal neurodevelopmental patterns in typically maturing human
brains using 200 healthy subjects from 5 to 22 years of age, based on the FA in cortical gray matter (GM). Specifically,
FA image is first aligned with the corresponding T1 image, which has been parcellated into different cortical ROIs, and
then the average FA in each ROI is computed. Linear mixed model is used to analyze the FA developmental pattern in
each cortical ROI. The developmental trajectory of FA in each ROI across ages is delineated, and the best-fitting models
of age-related changes in FA were linear for all ROIs. FA generally increases with the age from 5 to 22 years of age. In
addition, males and females follow the similar pattern, with the FA of females being generally lower than that of males
in most ROIs. This provides us some insights into the microstructural changes in the longitudinal cerebral cortex development.
Diffusion tensor image (DTI) is a powerful tool for quantitatively assessing the integrity of anatomical connectivity
in white matter in clinical populations. The prevalent methods for group-level analysis of DTI are statistical
analyses of invariant measures (e.g., fractional anisotropy) and principal directions across groups. The invariant
measures and principal directions, however, do not capture all information in full diffusion tensor, which can
decrease the statistical power of DTI in detecting subtle changes of white matters. Thus, it is very desirable to
develop new statistical methods for analyzing full diffusion tensors.
In this paper, we develop a set of toolbox, called RADTI, for the analysis of the full diffusion tensors as
responses and establish their association with a set of covariates. The key idea is to use the recent development
of log-Euclidean metric and then transform diffusion tensors in a nonlinear space into their matrix logarithms
in a Euclidean space. Our regression model is a semiparametric model, which avoids any specific parametric
assumptions. We develop an estimation procedure and a test procedure based on score statistics and a resampling
method to simultaneously assess the statistical significance of linear hypotheses across a large region of interest.
Monte Carlo simulations are used to examine the finite sample performance of the test procedure for controlling
the family-wise error rate. We apply our methods to the detection of statistical significance of diagnostic and
age effects on the integrity of white matter in a diffusion tensor study of human immunodeficiency virus.
A novel hierarchical unbiased group-wise registration is developed to robustly transform each individual image towards
a common space for atlas based analysis. This hierarchical group-wise registration approach consists of two main
components, (1) data clustering to group similar images together and (2) unbiased group-wise registration to generate a
mean image for each cluster. The mean images generated in the lower hierarchical level are regarded as the input
images for the higher hierarchy. In the higher hierarchical level, these input images will be further clustered and then
registered by using the same two components mentioned. This hierarchical bottom-up clustering and within-cluster
group-wise registration is repeated until a final mean image for the whole population is formed. This final mean image
represents the common space for all the subjects to be warped to in order for the atlas based analysis. Each individual
image at the bottom of the constructed hierarchy is transformed towards the root node through concatenating all the
intermediate displacement fields. In order to evaluate the performance of the proposed hierarchical registration in atlas
based statistical analysis, comparisons were made with the conventional group-wise registration in detecting simulated
brain atrophy as well as fractional anisotropy differences between neonates and 1-year-olds. In both cases, the proposed
approach demonstrated improved sensitivity (higher t-scores) than the conventional unbiased registration approach.
Longitudinal imaging studies are essential to understanding the neural development of neuropsychiatric disorders,
substance use disorders, and normal brain. Using appropriate image processing and statistical tools to analyze
the imaging, behavioral, and clinical data is critical for optimally exploring and interpreting the findings from
those imaging studies. However, the existing imaging processing and statistical methods for analyzing imaging
longitudinal measures are primarily developed for cross-sectional neuroimaging studies. The simple use of these
cross-sectional tools to longitudinal imaging studies will significantly decrease the statistical power of longitudinal
studies in detecting subtle changes of imaging measures and the causal role of time-dependent covariate in disease
process.
The main objective of this paper is to develop longitudinal statistics toolbox, called LSTGEE, for the analysis
of neuroimaging data from longitudinal studies. We develop generalized estimating equations for jointly modeling
imaging measures with behavioral and clinical variables from longitudinal studies. We develop a test procedure
based on a score test statistic and a resampling method to test linear hypotheses of unknown parameters,
such as associations between brain structure and function and covariates of interest, such as IQ, age, gene,
diagnostic groups, and severity of disease. We demonstrate the application of our statistical methods to the
detection of the changes of the fractional anisotropy across time in a longitudinal neonate study. Particularly,
our results demonstrate that the use of longitudinal statistics can dramatically increase the statistical power in
detecting the changes of neuroimaging measures. The proposed approach can be applied to longitudinal data
with multiple outcomes and accommodate incomplete and unbalanced data, i.e., subjects with different number
of measurements.
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