Analysis of brain structures using high-resolution MRI is a technique
commonly used to assess morphologic change due to illness, aging or treatment.
However, the timing, the association with treatment, and an intuitive
explanation of morphologic changes are not known. Imaging studies using
structural MRI so far have most often focused on volumetric assessment of
anatomical structures or on voxel-based analysis after image registration. With
increasing evidence for structural changes in small subregions and parts of
structures, analysis of shape might provide important additional information
since morphologic assessment via shape analysis provides a rich set of features.
Phenotyping via non-invasive imaging and statistical shape analysis
applied to clinical studies might reveal improved insight to understand
pathological processes and drug effects. An important emphasis of our research
is to explain shape changes in natural language terms (local growth, atrophy,
bending) to explain type, magnitude and locality of changes and thus to
facilitate association of pathomorphological change with findings on cellular
levels.
This lecture will discuss surface-based and medially-based shape
representation of brain structures (hippocampus, ventricles, caudate) applied to
clinical schizophrenia and autism studies. In particular, we will discuss
statistical issues related to the common problem of high-dimensional samples and
low sample size (HDLSS), and will compare surface and medial shape
representations applied to the same data sets. We will also demonstrate shape
change over time and over duration of illness by combining shape parametrization
with patient variables into a statistical framework. Several of our exploratory
studies demonstrate that shape discriminates patients and controls with greater
power than volume.
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