{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T21:05:07Z","timestamp":1771362307506,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010594","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T00:00:00Z","timestamp":1666224000000}}],"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major International (Regional) Joint Research Project","award":["32020103007"],"award-info":[{"award-number":["32020103007"]}]},{"DOI":"10.13039\/501100010031","name":"Postdoctoral Research Foundation of China","doi-asserted-by":"publisher","award":["2020M682016"],"award-info":[{"award-number":["2020M682016"]}],"id":[{"id":"10.13039\/501100010031","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2021ZD0111602"],"award-info":[{"award-number":["2021ZD0111602"]}]},{"name":"The Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDPB10"],"award-info":[{"award-number":["XDPB10"]}]},{"name":"The Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDB39000000"],"award-info":[{"award-number":["XDB39000000"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Advanced volumetric imaging methods and genetically encoded activity indicators have permitted a comprehensive characterization of whole brain activity at single neuron resolution in <jats:italic>Caenorhabditis elegans<\/jats:italic>. 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 <jats:italic>C. elegans<\/jats:italic>. 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\u20141024 \u00d7 1024 \u00d7 18 in voxels\u2014in 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.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1010594","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T17:58:02Z","timestamp":1665424682000},"page":"e1010594","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":14,"title":["Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis 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