  Morphometric differences in the anatomy of cortical structures are associated
with neuro-developmental and neuropsychiatric disorders. Such differences can
be quantized and detected by a powerful tool called Labeled Cortical Distance
Map (LCDM). The LCDM method pro-vides distances of labeled gray matter (GM)
voxels from the GM/white matter (WM) surface for specific cortical structures
(or tissues). Here we describe a method to analyze morphometric variability in
the particular tissue using LCDM distances. To extract more of the information
provided by LCDM distances, we perform pooling and censoring of LCDM distances.
In particular, we employ Brown-Forsythe (BF) test of homogeneity of variance
(HOV) on the LCDM distances. HOV analysis of pooled distances provides an
overall analysis of morphometric variability of the LCDMs due to the disease in
question, while the HOV analysis of censored distances suggests the location(s)
of significant variation in these differences (i.e., at which distance from the
GM/WM surface the morphometric variability starts to be significant). We also
check for the influence of assumption violations on the HOV analysis of LCDM
distances. In particular, we demonstrate that BF HOV test is robust to
assumption violations such as the non-normality and within sample dependence of
the residuals from the median for pooled and censored distances and are robust
to data aggregation which occurs in analysis of censored distances. We
illustrate the methodology on a real data example, namely, LCDM distances of GM
voxels in ventral medial prefrontal cortices (VMPFCs) to see the effects of
depression or being of high risk to depression on the morphometry of VMPFCs.
The methodology used here is also valid for morphometric analysis of other
cortical structures.
