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Automated Algorithms for Multiscale Morphometry of Neuronal Dendrites

christina{at}camelot.mssm.edu, Department of Biomathematical Sciences and Computational Neurobiology and Imaging Center, Mount Sinai School of Medicine, New York, NY 10029, U.S.A.

Patrick R. Hof

hof{at}neuro.mssm.edu, Fishberg Research Center for Neurobiology, Kastor Neurobiology of Aging Laboratories, Computational Neurobiology and Imaging Center, and Advanced Imaging Program, Mount Sinai School of Medicine, New York, NY 10029, U.S.A.

Susan L. Wearne

susan{at}camelot.mssm.edu, Department of Biomathematical Sciences, Fishberg Research Center for Neurobiology, and Computational Neurobiology and Imaging Center, Mount Sinai School of Medicine, New York, NY 10029, U.S.A.

W. Brent Lindquist

lindquis{at}ams.sunysb.edu, Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, U.S.A.

We describe the synthesis of automated neuron branching morphology and spine detection algorithms to provide multiscale three-dimensional morphological analysis of neurons. The resulting software is applied to the analysis of a high-resolution (0.098 µm x 0.098 µm x 0.081 µm) image of an entire pyramidal neuron from layer III of the superior temporal cortex in rhesus macaque monkey. The approach provides a highly automated, complete morphological analysis of the entire neuron; each dendritic branch segment is characterized by several parameters, including branch order, length, and radius as a function of distance along the branch, as well as by the locations, lengths, shape classification (e.g., mushroom, stubby, thin), and density distribution of spines on the branch. Results for this automated analysis are compared to published results obtained by other computer-assisted manual means.

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