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Microglia are the resident immune cell population in the mammalian central nervous system (CNS). These highly plastic cells exhibit ramified arbors in their resting state, and progressively less-complex arbors when activated. Our goal is to compare the spatial distributions of resting and activated microglia in normal brain tissue against tissue that is perturbed by insertion of a neural recording device. For this, microglia were imaged using multiplex immunostaining and confocal microscopy. The cell arbors were traced automatically, and 127 quantitative measurements based on the L-measure [8] were computed for each cell. A hierarchical extension of Coifman's [1,2] unsupervised harmonic analysis method was used to profile these multivariate data and identify groups of similar cells and the underlying features. This iterative procedure induces an orthogonal basis by constructing a coupled geometry over the row and column spaces of the feature matrix. Smoothing of the dataset, and the row and column clusters is achieved simultaneously when the algorithm converges. Experiments on real image datasets demonstrate the ability of this method to generate qualitative and quantitative groups that are biologically meaningful despite the existence of noise and missing values.

Microglia are Central Nervous System (CNS) cells that are similar to macrophages. They undergo characteristic changes in cell arbor morphology in response to tissue perturbation. Ensembles of microglia exhibit a progression of arbor morphologies. Our goal is to discover these progressions and determine the underlying arbor features from 3-D multi-channel fluorescence confocal microscope images of rat brain tissue multiplex stained with Hoechst to reveal cell nuclei, and immunolabeled for IBA-1 to reveal microglia. The microglia are automatically traced, and a set of 131 arbor features are computed. An agglomerative clustering algorithm based on Pearson's correlation is used to derive coherent modules of features. A k-NNG structural similarity analysis of feature modules enables us to construct a global similarity matrix, from which a global multi-level k-NNG is constructed to derive an interactive progression chart through a modified Fruchterman-Reingold algorithm that clearly reveals a progression from highly ramified microglia to round cells proximal to the injury site of an implanted neural recording device.