The topic of studying the growth of human brain development has become of increasing interest in the neuroimaging community. Cross-sectional studies may allow comparisons between means of different age groups, but they do not provide a growth model that integrates the continuum of time, nor do they present any information about how individuals/population change over time. Longitudinal data analysis method arises as a strong tool to address these questions. In this paper, we use longitudinal analysis methods to study tissue development in early brain growth. A novel approach of multivariate longitudinal analysis is applied to study the associations
between the growth of different brain tissues. In this paper, we present the methodologies to statistically study scalar (univariate) and vector (multivariate) longitudinal data, and demonstrate exploratory results in a neuroimaging study of early brain tissue development. We obtained growth curves as a quadratic function of time for all three tissues. The quadratic terms were
tested to be statistically significant, showing that there was indeed a quadratic growth of tissues in early brain development. Moreover, our result shows that there is a positive correlation between repeated measurements of any single tissue, and among those of different tissues. Our approach is generic in natural and thus can be applied to any longitudinal data with multiple outcomes,
even brain structures. Also, our joint mixed model is flexible enough to allow incomplete and unbalanced data, i.e. subjects do not need to have the same number of measurements, or be measured at the exact time points.
Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely
locate morphological changes between healthy and pathological structures. This manuscript presents a
comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied
in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main
limitations is the necessity of spherical topology.
The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such
as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic
description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM). After alignment,
differences between groups of surfaces are computed using the Hotelling T2 two sample metric. Statistical p-values,
both raw and corrected for multiple comparisons, result in significance maps. Additional visualization
of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group
The correction for multiple comparisons is performed via two separate methods that each have a distinct
view of the problem. The first one aims to control the family-wise error rate (FWER) or false-positives via the
extrema histogram of non-parametric permutations. The second method controls the false discovery rate and
results in a less conservative estimate of the false-negatives.
Prior versions of this shape analysis framework have been applied already to clinical studies on hippocampus
and lateral ventricle shape in adult schizophrenics. The novelty of this submission is the use of the Hotelling T2
two-sample group difference metric for the computation of a template free statistical shape analysis. Template
free group testing allowed this framework to become independent of any template choice, as well as it improved
the sensitivity of our method considerably. In addition to our existing correction methodology for the multiple
comparison problem using non-parametric permutation tests, we have extended the testing framework to include
False Discovery Rate (FDR). FDR provides a significance correction with higher sensitivity while allowing a
expected minimal amount of false-positives compared to our prior testing scheme.