{"doi":"10.1016/j.crmeth.2025.101050","title":"A circadian behavioral analysis suite for real-time classification of daily rhythms in complex behaviors","abstract":"Long-term analysis of animal behavior has been limited by reliance on real-time sensors or manual scoring. Existing machine learning tools can automate analysis but often fail under variable conditions or ignore temporal dynamics. We developed a scalable pipeline for continuous, real-time acquisition and classification of behavior across multiple animals and conditions. At its core is a self-supervised vision model paired with a lightweight classifier that enables robust performance with minimal manual labeling. Our system achieves expert-level performance and can operate indefinitely across diverse recording environments. As a proof-of-concept, we recorded 97 mice over 2 weeks to test whether sex hormones influence circadian behaviors. We discovered sex- and estrogen-dependent rhythms in behaviors such as digging and nesting. We introduce the Circadian Behavioral Analysis Suite (CBAS), a modular toolkit that supports high-throughput, long-timescale behavioral phenotyping, allowing for the temporal analysis of behaviors that were previously difficult or impossible to observe.","journal":"Cell Reports Methods","year":2025,"id":541228,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9421,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1429973,"name":"Gavin E. Ratcliff","orcid":null,"position":1,"is_corresponding":false},{"id":1429974,"name":"Arthur Mayo","orcid":null,"position":2,"is_corresponding":false},{"id":1332817,"name":"Blanca Perez","orcid":null,"position":3,"is_corresponding":false},{"id":1332818,"name":"Larissa Rays Wahba","orcid":null,"position":4,"is_corresponding":false},{"id":1131085,"name":"K. L. Nikhil","orcid":"0000-0003-1814-2069","position":5,"is_corresponding":false},{"id":1332820,"name":"William C. Lenzen","orcid":null,"position":6,"is_corresponding":false},{"id":1429975,"name":"Yangyuan Li","orcid":null,"position":7,"is_corresponding":false},{"id":542599,"name":"Jordan Mar","orcid":"0000-0003-4597-2404","position":8,"is_corresponding":false},{"id":562047,"name":"Isabella Farhy-Tselnicker","orcid":"0000-0002-3733-7120","position":9,"is_corresponding":false},{"id":940423,"name":"Wanhe Li","orcid":"0000-0001-5682-5173","position":10,"is_corresponding":false},{"id":628607,"name":"Jeff R. Jones","orcid":"0000-0002-8386-7798","position":11,"is_corresponding":false},{"id":1332816,"name":"L. Perry","orcid":null,"position":0,"is_corresponding":true}],"reference_count":80,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:52:47.161928Z","pmid":"40393389","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}