Automatic classification and neurotransmitter prediction of synapses in electron microscopy

Abstract

This paper presents a deep-learning-based workflow to detect synapses and predict their neurotransmitter type in the primitive chordate Ciona intestinalis (Ciona) electron microscopic (EM) images. Identifying synapses from EM images to build a full map of connections between neurons is a labor-intensive process and requires significant domain expertise. Automation of synapse classification would hasten the generation and analysis of connectomes. Furthermore, inferences concerning neuron type and function from synapse features are in many cases difficult to make. Finding the connection between synapse structure and function is an important step in fully understanding a connectome. Class Activation Maps derived from the convolutional neural network provide insights on important features of synapses based on cell type and function. The main contribution of this work is in the differentiation of synapses by neurotransmitter type through the structural information in their EM images. This enables the prediction of neurotransmitter types for neurons in Ciona, which were previously unknown. The prediction model with code is available on GitHub.

Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster

“…Analogous work detecting excitatory versus inhibitory synapses in the Ciona intestinalis larva has added signs for 49 neurons. 108 In vertebrate EM datasets, human annotators can see differences in the vesicles for excitatory and inhibitory synapses. 16 , 35 , 36 , 37 , 109 , 110 Symmetric synapses (usually inhibitory) have already been disambiguated from asymmetric ones (usually excitatory) automatically and at scale.…”

ReeBundle: A Method for Topological Modeling of White Matter Pathways Using Diffusion MRI

“…In Fig. 12A, we show that the topological distances significantly increases with time for Alzheimer's 2 Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database [42]. As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.…”

“…Current research on modeling brain networks can be categorized into 1) synaptic networks mapped at a microscopic scale where nodes represent neurons and synapses represent edges, and 2) whole brain connectome at a macroscopic scale using diffusion magnetic resonance imaging (dMRI). Synaptic network modeling [1], [2] is desirable and informative but it is almost impossible to access for human brains. It is also challenging for the study of structural connectomics and analyses of neurological diseases due to the underlying anatomic complexity.…”