Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans

• URL: http://arxiv.org/abs/2109.10474v2
• Date: Thu, 23 Sep 2021 07:16:01 GMT
• Title: Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans
• Authors: Yuxiang Wu, Shang Wu, Xin Wang, Chengtian Lang, Quanshi Zhang, Quan Wen, Tianqi Xu
• Abstract summary: We propose a cascade solution for long-term and rapid recognition of head ganglion neurons in a freely moving textitC. elegans. Under the constraint of a small number of training samples, our bottom-up approach is able to process each volume - $1024 times 1024 times 18$ in voxels - in less than 1 second. Our work represents an important development towards a rapid and fully automated algorithm for decoding whole brain activity underlying natural animal behaviors.
• Score: 18.788855494800238
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Advanced volumetric imaging methods and genetically encoded activity indicators have permitted a comprehensive characterization of whole brain activity at single neuron resolution in \textit{Caenorhabditis elegans}. The constant motion and deformation of the mollusc nervous system, however, impose a great challenge for a consistent identification of densely packed neurons in a behaving animal. Here, we propose a cascade solution for long-term and rapid recognition of head ganglion neurons in a freely moving \textit{C. elegans}. First, potential neuronal regions from a stack of fluorescence images are detected by a deep learning algorithm. Second, 2 dimensional neuronal regions are fused into 3 dimensional neuron entities. Third, by exploiting the neuronal density distribution surrounding a neuron and relative positional information between neurons, a multi-class artificial neural network transforms engineered neuronal feature vectors into digital neuronal identities. Under the constraint of a small number (20-40 volumes) of training samples, our bottom-up approach is able to process each volume - $1024 \times 1024 \times 18$ in voxels - in less than 1 second and achieves an accuracy of $91\%$ in neuronal detection and $74\%$ in neuronal recognition. Our work represents an important development towards a rapid and fully automated algorithm for decoding whole brain activity underlying natural animal behaviors.

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