Modern computational methods in bioimaging have become vital in enhancing our understanding of classical and molecular neuroanatomy of the brain. Recent advances in cell labeling and high-resolution imaging have made it possible to computationally reconstruct 3D digital atlases from the single-neuron to whole-brain level [1-3]. Central to these results are high-throughput technologies such as microarrays and in situ hybridization (ISH), and more recently digital sequencing, as well as robust data organization and mining tools to attack molecular questions on a geno-mic scale. The emerging neuroinformatics subspecialty of computational anatomy [4] involves the automatic construction of anatomic surfaces, their statistical comparison, and detailed analysis. In a general sense, computational neuroanatomy is the application of computational techniques such as analysis, visualization, modeling, and simulation to the investigation of neural structure, as well as the study of variability in this information with other functional, genetic or structural information.
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