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A fundamental problem in brain imaging is the identification of volumes whose features distinguish two populations. One popular solution, Voxel-Based Analyses (VBA), glues together contiguous voxels with significant intra-voxel population differences. VBA's output regions may not be spatially consistent: each voxel may show a unique population effect. We introduce Agglomerative Region-Based Analysis (ARBA), which mitigates this issue to increase sensitivity. ARBA is an Agglomerative Clustering procedure, like Ward's method, which segments image sets in a common space to greedily maximize a likelihood function. The resulting regions are pared down to a set of disjoint regions that show statistically significant population differences via Permutation Testing. ARBA is shown to increase sensitivity over VBA in a detection task on multivariate Diffusion MRI brain images.
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Agglomerative Region-Based Analysis
A fundamental problem in brain imaging is the identification of volumes whose features distinguish two populations. One popular solution, Voxel-Based Analyses (VBA), glues together contiguous voxels with significant intra-voxel population differences. VBA's output regions may not be spatially consistent: each voxel may show a unique population effect. We introduce Agglomerative Region-Based Analysis (ARBA), which mitigates this issue to increase sensitivity. ARBA is an Agglomerative Clustering procedure, like Ward's method, which segments image sets in a common space to greedily maximize a likelihood function. The resulting regions are pared down to a set of disjoint regions that show statistically significant population differences via Permutation Testing. ARBA is shown to increase sensitivity over VBA in a detection task on multivariate Diffusion MRI brain images.