{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automatically-tracking-neurons-in-a-moving","title":"Automatically tracking neurons in a moving and deforming brain","arxiv_id":"1610.04579","date":"2016-10-14","proceeding":null,"authors":["Jeffrey P. Nguyen","Ashley N. Linder","George S. Plummer","Joshua W. Shaevitz","Andrew M. Leifer"],"abstract":"Advances in optical neuroimaging techniques now allow neural activity to be\nrecorded with cellular resolution in awake and behaving animals. Brain motion\nin these recordings pose a unique challenge. The location of individual neurons\nmust be tracked in 3D over time to accurately extract single neuron activity\ntraces. Recordings from small invertebrates like C. elegans are especially\nchallenging because they undergo very large brain motion and deformation during\nanimal movement. Here we present an automated computer vision pipeline to\nreliably track populations of neurons with single neuron resolution in the\nbrain of a freely moving C. elegans undergoing large motion and deformation. 3D\nvolumetric fluorescent images of the animal's brain are straightened, aligned\nand registered, and the locations of neurons in the images are found via\nsegmentation. Each neuron is then assigned an identity using a new\ntime-independent machine-learning approach we call Neuron Registration Vector\nEncoding. In this approach, non-rigid point-set registration is used to match\neach segmented neuron in each volume with a set of reference volumes taken from\nthroughout the recording. The way each neuron matches with the references\ndefines a feature vector which is clustered to assign an identity to each\nneuron in each volume. Finally, thin-plate spline interpolation is used to\ncorrect errors in segmentation and check consistency of assigned identities.\nThe Neuron Registration Vector Encoding approach proposed here is uniquely well\nsuited for tracking neurons in brains undergoing large deformations. When\napplied to whole-brain calcium imaging recordings in freely moving C. elegans,\nthis analysis pipeline located 150 neurons for the duration of an 8 minute\nrecording and consistently found more neurons more quickly than manual or\nsemi-automated approaches.","url_abs":"http://arxiv.org/abs/1610.04579v1","url_pdf":"http://arxiv.org/pdf/1610.04579v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"automatically-tracking-neurons-in-a-moving","repo_url":"https://github.com/leiferlab/NeRVEclustering","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}