Details

Autor(en) / Beteiligte

• Wu, Yuxiang
• Wu, Shang
• Wang, Xin
• Lang, Chengtian
• Zhang, Quanshi
• Wen, Quan
• Xu, Tianqi
• Richards, Blake A

Titel

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

Ist Teil von

• PLoS computational biology, 2022-10, Vol.18 (10), p.e1010594-e1010594

Ort / Verlag

San Francisco: Public Library of Science

Erscheinungsjahr

2022

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Advanced volumetric imaging methods and genetically encoded activity indicators have permitted a comprehensive characterization of whole brain activity at single neuron resolution in Caenorhabditis elegans . The constant motion and deformation of the nematode nervous system, however, impose a great challenge for 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 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. With a small number of training samples, our bottom-up approach is able to process each volume—1024 × 1024 × 18 in voxels—in less than 1 second and achieves an accuracy of 91% in neuronal detection and above 80% in neuronal tracking over a long video recording. Our work represents a step towards rapid and fully automated algorithms for decoding whole brain activity underlying naturalistic behaviors.