Abstract
BackgroundIn the nervous system, the neurons communicate through synapses. The size, morphology, and connectivity of these synapses are significant in determining the functional properties of the neural network. Therefore, they have always been a major focus of neuroscience research. Two-photon laser scanning microscopy allows the visualization of synaptic structures in vivo, leading to many important findings. However, the identification and quantification of structural imaging data currently rely heavily on manual annotation, a method that is both time-consuming and prone to bias.ResultsWe present an automated approach for the identification of synaptic structures in two-photon images. Axon boutons and dendritic spines are structurally distinct. They can be detected automatically using this image processing method. Then, synapses can be identified by integrating information from adjacent axon boutons and dendritic spines. In this study, we first detected the axonal boutons and dendritic spines respectively, and then identified synapses based on these results. Experimental results were validated manually, and the effectiveness of our proposed method was demonstrated.ConclusionsThis approach will helpful for neuroscientists to automatically analyze and quantify the formation, elimination and destabilization of the axonal boutons, dendritic spines and synapses.
Highlights
Synapses were first discovered in the 1890s, when Sir Sherrington, through his pioneering work on motor reflexes, wrote that synapse is the way of neuronal communication in the nervous system [1]
We focus on the detection of axonal boutons, dendritic spines and synapses from the in vivo two-photon image stacks
Experimental results In order to demonstrate the effectiveness of the proposed algorithm, we show two axon images corresponding to layer 1 and layer 20, with the axonal boutons indicated by red circles marked by experienced neurobiologists in Fig. 13a and b
Summary
Synapses were first discovered in the 1890s, when Sir Sherrington, through his pioneering work on motor reflexes, wrote that synapse is the way of neuronal communication in the nervous system [1]. In the mammalian central nervous system, the vast majority of the synapses are chemical. Especially excitatory synapses, typically consist of presynaptic axon boutons and postsynaptic dendritic spines. The structural plasticity of boutons and spines underlies functional synaptic plasticity, widely accepted as the neural basis of learning and memory. The advent of boutons and spines can be imaged in. The size, morphology, and connectivity of these synapses are significant in determining the functional properties of the neural network. They have always been a major focus of neuroscience research. The identification and quantification of structural imaging data currently rely heavily on manual annotation, a method that is both time-consuming and prone to bias
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