The spatial distribution of synaptic inputs on the dendritic tree of a neuron can have significant influence on neuronal function. Consequently, accurate anatomical reconstructions of neuron morphology and synaptic localization are critical when modeling and predicting physiological responses of individual neurons. Historically, generation of three-dimensional (3D) neuronal reconstructions together with comprehensive mapping of synaptic inputs has been an extensive task requiring manual identification of putative synaptic contacts directly from tissue samples or digital images. Recent developments in neuronal tracing software applications have improved the speed and accuracy of 3D reconstructions, but localization of synaptic sites through the use of pre- and/or post-synaptic markers has remained largely a manual process. To address this problem, we have developed an algorithm, based on 3D distance measurements between putative pre-synaptic terminals and the post-synaptic dendrite, to automate synaptic contact detection on dendrites of individually labeled neurons from 3D immunofluorescence image sets. In this study, the algorithm is implemented with custom routines in Matlab, and its effectiveness is evaluated through analysis of primary sensory afferent terminals on motor neurons. Optimization of algorithm parameters enabled automated identification of synaptic contacts that matched those identified by manual inspection with low incidence of error. Substantial time savings and the elimination of variability in contact detection introduced by different users are significant advantages of this method. (C) 2011 Elsevier B.V. All rights reserved.
Available at: http://works.bepress.com/david_ladle/13/