Progress in Functional Neuroanatomy: Precise Automatic Geometric Reconstruction of Neuronal Morphology From Confocal Image Stacks

Abstract

Dendritic architecture provides the structural substrate for myriads of input and output synapses in the brain and for the integration of presynaptic inputs. Understanding mechanisms of evolution and development of neuronal shape and its respective function is thus a formidable problem in neuroscience. A fundamental prerequisite for finding answers is a precise quantitative analysis of neuronal structure in situ and in vivo. Therefore we have developed a tool set for automatic geometric reconstruction of neuronal architecture from stacks of confocal images. It provides exact midlines, diameters, surfaces, volumes, and branch point locations and allows analysis of labeled molecule distribution along neuronal surfaces as well as direct export into modeling software. We show the high accuracy of geometric reconstruction and the analysis of putative input synapse distribution throughout entire dendritic trees from in situ light microscopy preparations as a possible application. The binary version of the reconstruction module is downloadable at no cost.