Hierarchical Compression Reveals Sub-Second to Day-Long Structure in Larval Zebrafish Behavior
Animal behavior is dynamic, evolving over multiple timescales from milliseconds to days and even across a lifetime. To understand the mechanisms governing these dynamics, it is necessary to capture multi-timescale structure from behavioral data. Here, we develop computational tools and study the beh...
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| Vydané v: | eNeuro Ročník 7; číslo 4; s. ENEURO.0408-19.2020 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
Society for Neuroscience
01.07.2020
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| ISSN: | 2373-2822, 2373-2822 |
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| Abstract | Animal behavior is dynamic, evolving over multiple timescales from milliseconds to days and even across a lifetime. To understand the mechanisms governing these dynamics, it is necessary to capture multi-timescale structure from behavioral data. Here, we develop computational tools and study the behavior of hundreds of larval zebrafish tracked continuously across multiple 24-h day/night cycles. We extracted millions of movements and pauses, termed bouts, and used unsupervised learning to reduce each larva’s behavior to an alternating sequence of active and inactive bout types, termed modules. Through hierarchical compression, we identified recurrent behavioral patterns, termed motifs. Module and motif usage varied across the day/night cycle, revealing structure at sub-second to day-long timescales. We further demonstrate that module and motif analysis can uncover novel pharmacological and genetic mutant phenotypes. Overall, our work reveals the organization of larval zebrafish behavior at multiple timescales and provides tools to identify structure from large-scale behavioral datasets. |
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| AbstractList | Animal behavior is dynamic, evolving over multiple timescales from milliseconds to days and even across a lifetime. To understand the mechanisms governing these dynamics, it is necessary to capture multi-timescale structure from behavioral data. Here, we develop computational tools and study the behavior of hundreds of larval zebrafish tracked continuously across multiple 24-h day/night cycles. We extracted millions of movements and pauses, termed bouts, and used unsupervised learning to reduce each larva’s behavior to an alternating sequence of active and inactive bout types, termed modules. Through hierarchical compression, we identified recurrent behavioral patterns, termed motifs. Module and motif usage varied across the day/night cycle, revealing structure at sub-second to day-long timescales. We further demonstrate that module and motif analysis can uncover novel pharmacological and genetic mutant phenotypes. Overall, our work reveals the organization of larval zebrafish behavior at multiple timescales and provides tools to identify structure from large-scale behavioral datasets. Animal behavior is dynamic, evolving over multiple timescales from milliseconds to days and even across a lifetime. To understand the mechanisms governing these dynamics, it is necessary to capture multi-timescale structure from behavioral data. Here, we develop computational tools and study the behavior of hundreds of larval zebrafish tracked continuously across multiple 24-h day/night cycles. We extracted millions of movements and pauses, termed bouts, and used unsupervised learning to reduce each larva's behavior to an alternating sequence of active and inactive bout types, termed modules. Through hierarchical compression, we identified recurrent behavioral patterns, termed motifs. Module and motif usage varied across the day/night cycle, revealing structure at sub-second to day-long timescales. We further demonstrate that module and motif analysis can uncover novel pharmacological and genetic mutant phenotypes. Overall, our work reveals the organization of larval zebrafish behavior at multiple timescales and provides tools to identify structure from large-scale behavioral datasets.Animal behavior is dynamic, evolving over multiple timescales from milliseconds to days and even across a lifetime. To understand the mechanisms governing these dynamics, it is necessary to capture multi-timescale structure from behavioral data. Here, we develop computational tools and study the behavior of hundreds of larval zebrafish tracked continuously across multiple 24-h day/night cycles. We extracted millions of movements and pauses, termed bouts, and used unsupervised learning to reduce each larva's behavior to an alternating sequence of active and inactive bout types, termed modules. Through hierarchical compression, we identified recurrent behavioral patterns, termed motifs. Module and motif usage varied across the day/night cycle, revealing structure at sub-second to day-long timescales. We further demonstrate that module and motif analysis can uncover novel pharmacological and genetic mutant phenotypes. Overall, our work reveals the organization of larval zebrafish behavior at multiple timescales and provides tools to identify structure from large-scale behavioral datasets. |
| Author | Ghosh, Marcus Rihel, Jason |
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| CitedBy_id | crossref_primary_10_1016_j_cub_2024_02_022 crossref_primary_10_7554_eLife_82249 crossref_primary_10_1016_j_compbiomed_2025_110959 crossref_primary_10_7554_eLife_59683 crossref_primary_10_1016_j_crmeth_2022_100381 crossref_primary_10_1159_000543081 crossref_primary_10_1016_j_cophys_2020_03_006 crossref_primary_10_1242_dmm_044594 |
| Cites_doi | 10.1176/appi.books.9780890425596 10.1016/j.cell.2017.10.041 10.1016/S0003-3472(76)80003-6 10.1371/journal.pone.0000443 10.1126/sciadv.aau9253 10.1523/JNEUROSCI.1284-12.2012 10.1038/nature12111 10.1016/B978-0-12-384892-5.00011-6 10.1016/j.neuron.2016.12.041 10.1016/j.cub.2017.12.002 10.1038/s41567-018-0093-0 10.1038/nmeth.4429 10.7554/eLife.12741 10.1126/science.1183090 10.1016/j.neuron.2015.12.039 10.1016/j.neuron.2015.11.031 10.1016/j.neuroscience.2004.11.031 10.7554/eLife.26652 10.1016/j.conb.2017.03.009 10.1073/pnas.1607601113 10.1016/J.CELL.2019.01.048 10.1109/TPAMI.2005.113 10.1016/j.cell.2019.05.050 10.1109/5.892710 10.1073/pnas.1308282110 10.1016/S0092-8674(00)81965-0 10.1016/j.cell.2017.06.032 10.1371/journal.pbio.0050277 10.1088/1478-3975/14/1/015002 10.1242/jeb.003939 10.1126/science.1132939 10.1126/science.aaf3621 10.1126/science.1250298 10.1016/j.conb.2018.02.004 10.1038/nmeth.3581 10.1098/rsif.2014.0672 10.1016/S0006-8993(01)02444-1 10.1073/pnas.1206894109 10.1523/JNEUROSCI.4332-06.2006 10.1007/978-3-642-27728-3_135-1 10.1109/TPAMI.2005.159 10.2144/00291bm09 10.1098/rsif.2016.0466 10.1111/j.1439-0310.1963.tb01161.x 10.1073/pnas.0914718107 10.1016/j.cels.2017.10.001 10.1016/j.conb.2017.02.009 10.1038/nchembio.307 10.1186/s12915-018-0494-7 |
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| Copyright | Copyright © 2020 Ghosh and Rihel. Copyright © 2020 Ghosh and Rihel 2020 Ghosh and Rihel |
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| Keywords | sleep behavioral dynamics zebrafish |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 This work was supported by a Medical Research Council Doctoral Training grant (M.G.), a UCL Excellence fellowship (J.R.), a Wellcome Trust Investigator Award (J.R.), and a European Research Council starting grant (J.R.). The authors declare no competing financial interests. Author contributions: M.G. and J.R. designed research; M.G. performed research; J.R. contributed unpublished reagents/analytic tools; M.G. and J.R. analyzed data; M.G. and J.R. wrote the paper. |
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