{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T17:55:09Z","timestamp":1790963709140,"version":"4.1.0"},"reference-count":26,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Quant. Biol."],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Zebrafish are a model organism used for the study of vertebrate development, disease, and drug discovery. Two\u2010day old zebrafish exhibit burst swimming behavior that can be elicited by a light touch to the tail. Motor touch responses are frequently video\u2010recorded and later analyzed by hand. Despite this being a simple behavioral assay, methods for robustly analyzing these videos in a reproducible and time\u2010efficient manner rely on manual tracking, which is prone to experimenter bias and error. Here we present LaZeTrack, a machine learning\u2010based program, which employs Ultralytics\u2019 YOLOv8 nano (YOLOv8n) object detection algorithm to automatically analyze touch response videos using 2\u2010day\u2010old zebrafish. This program breaks down video files into their constituent frames which are then passed through a custom\u2010trained YOLOv8n algorithm to detect the presence of a single zebrafish (mean average precision, mAP50\u201395 =\u00a00.74). LaZeTrack then refines the tracking data output by the model and tabulates it into an Excel spreadsheet. It then computes and extracts four relevant swim metrics: swim duration (s), swim distance (mm), mean swim velocity (mm\/s), and maximum swim velocity (mm\/s). LaZeTrack rapidly accelerates\u00a0the analysis process, while also eliminating errors associated with manual tracking. Furthermore, it allows for high\u2010throughput analysis of zebrafish touch response videos and can detect subtle differences in motor metrics arising from temperature differences, demonstrating the utility of this\u00a0tracking algorithm. LaZeTrack is available for download on GitHub (Armstrong\u2010Lab\u201070\/LaZeTrack).<\/jats:p>","DOI":"10.1002\/qub2.70043","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T16:35:54Z","timestamp":1780331754000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LaZeTrack: An open\u2010source object detection algorithm to detect and track 2\u2010day\u2010old zebrafish motor touch responses"],"prefix":"10.1002","volume":"14","author":[{"given":"Adrien","family":"Lacroix","sequence":"first","affiliation":[{"name":"Department of Neurology and Neurosurgery McGill University  Montreal Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gary Alan Barclay","family":"Armstrong","sequence":"additional","affiliation":[{"name":"Department of Neurology and Neurosurgery McGill University  Montreal Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tins.2014.02.011"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/bs.adgen.2015.09.002"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymeth.2018.07.012"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-60192-2_4"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-4695(199812)37:4<622::AID-NEU10>3.0.CO;2-S"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jphysparis.2003.10.009"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.20-11-03964.2000"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1152\/jn.01248.2004"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1172\/jci.insight.97152"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/acn3.51174"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/aja.1002030302"},{"key":"e_1_2_10_13_1","article-title":"Using touch\u2010evoked response and locomotion assays to assess muscle performance and function in zebrafish","volume":"116","author":"Sztal TE","year":"2016","journal-title":"J Vis Exp"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2024.1461264"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-54375-y"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-30303-w"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ADICS58448.2024.10533619"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-7962-2_39"},{"key":"e_1_2_10_19_1","unstructured":"YaseenM.What is YOLOv8: an in\u2010depth exploration of the internal features of the next\u2010generation object 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