We applied a modified probabilistic fiber-tracking method for the extraction of fiber pathways to quantify decreased white matter integrity as a surrogate of structural loss in connectivity due to cranial radiation therapy (CRT) as treatment for pediatric medulloblastoma. Thirty subjects were examined (n=8 average-risk, n=22 high-risk) and the groups did not differ significantly in age at examination. The pathway analysis created a structural connectome focused on sub-networks within the central executive network (CEN) for comparison between baseline and post-CRT scans and for comparison between standard and high dose CRT. A paired-wise comparison of the connectivity between baseline and post-CRT scans showed the irradiation did have a significant detrimental impact on white matter integrity (decreased fractional anisotropy (FA) and decreased axial diffusivity (AX)) in most of the CEN sub-networks. Group comparisons of the change in the connectivity revealed that patients receiving high dose CRT experienced significant AX decreases in all sub-networks while the patients receiving standard dose CRT had relatively stable AX measures across time. This study on pediatric patients with medulloblastoma demonstrated the utility of this method to identify specific sub-networks within the developing brain affected by CRT.
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.
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
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