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Disease in the brain is often associated with subtle, spatially diffuse, orcomplex tissue changes that may lie beneath the level of gross visualinspection, even on magnetic resonance imaging (MRI). Unfortunately, currentcomputer-assisted approaches that examine pre-specified features, whetheranatomically-defined (i.e. thalamic volume, cortical thickness) or based onpixelwise comparison (i.e. deformation-based methods), are prone to missing avast array of physical changes that are not well-encapsulated by these metrics.In this paper, we have developed a technique for automated pattern analysisthat can fully determine the relationship between brain structure andobservable phenotype without requiring any a priori features. Our technique,called transport-based morphometry (TBM), is an image transformation that mapsbrain images losslessly to a domain where they become much more separable. Thenew approach is validated on structural brain images of healthy older adultsubjects where even linear models for discrimination, regression, and blindsource separation enable TBM to independently discover the characteristicchanges of aging and highlight potential mechanisms by which aerobic fitnessmay mediate brain health later in life. TBM is a generative approach that canprovide visualization of physically meaningful shifts in tissue distributionthrough inverse transformation. The proposed framework is a powerful techniquethat can potentially elucidate genotype-structural-behavioral associations inmyriad diseases.

Discovery and visualization of structural biomarkers from MRI using transport-based morphometry