  Disease in the brain is often associated with subtle, spatially diffuse, or
complex tissue changes that may lie beneath the level of gross visual
inspection, even on magnetic resonance imaging (MRI). Unfortunately, current
computer-assisted approaches that examine pre-specified features, whether
anatomically-defined (i.e. thalamic volume, cortical thickness) or based on
pixelwise comparison (i.e. deformation-based methods), are prone to missing a
vast array of physical changes that are not well-encapsulated by these metrics.
In this paper, we have developed a technique for automated pattern analysis
that can fully determine the relationship between brain structure and
observable phenotype without requiring any a priori features. Our technique,
called transport-based morphometry (TBM), is an image transformation that maps
brain images losslessly to a domain where they become much more separable. The
new approach is validated on structural brain images of healthy older adult
subjects where even linear models for discrimination, regression, and blind
source separation enable TBM to independently discover the characteristic
changes of aging and highlight potential mechanisms by which aerobic fitness
may mediate brain health later in life. TBM is a generative approach that can
provide visualization of physically meaningful shifts in tissue distribution
through inverse transformation. The proposed framework is a powerful technique
that can potentially elucidate genotype-structural-behavioral associations in
myriad diseases.
