EPBscore: a Novel Method for Computer-Assisted Analysis of Axonal Structure and Dynamics
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Live brain imaging at cellular and synaptic resolution has dramatically improved our understanding of its organization and plasticity. A major issue in this field is the lack of automated tools to reliably analyze synaptic structures without heavily depending on the user’s subjectivity. As time-lapse imaging experiments can produce vast amounts of data, the tediousness of extracting key structural features of neurites such as the number, location and size of synaptic contacts through image analysis is a major bottleneck to perform large-scale studies. Unbiased quantitative tracking of large populations of synaptic sites is still challenging (Helmstaedter et al. 2011), despite the importance of understanding the principles of synaptic organization, connectivity and plasticity. This task is especially complex for axons and their boutons (Brown et al. 2011). Canonical cortical axons have a relatively simple structure, consisting of thin axonal shafts with uniform diameters (~100–1000 nm,...
KeywordsAxons Axonal boutons Automated analysis Segmentation Synaptic plasticity Learning and memory Aging Presynaptic Connectivity In vivo 2-photon imaging
We thank Karel Svoboda for support and for critical input on the initial development of EPBscore. Anthony Holtmaat, Linda Wilbrecht for help with an initial set of experiments. Lucien West, Jonathan Bowen, Dawn Thompson, Graham Little for help with the analysis. Cher Bachar for help with Matlab implementation. SS is supported by grants from NSFC (91332122 and 31171047), and a seed grant from the Center for Brain-inspired Computation Research. This work was funded by the Medical Research Council, UK.
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