Evidence map›Paper›PMID 39568468›Full record

ArticlePatterns (New York, N.Y.)2024

Segmentation tracking and clustering system enables accurate multi-animal tracking of social behaviors.

Cheng Tang, Yang Zhou, Shuaizhu Zhao, Mingshu Xie, Ruizhe Zhang, Xiaoyan Long, Lingqiang Zhu, Youming Lu, Guangzhi Ma, Hao Li

Abstract read
In one paragraph

Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Cheng TangInnovation Center of Brain Medical Sciences, the Ministry of Education, China, Huazhong University of Science and Technology, Wuhan 430022, China.
Yang ZhouInnovation Center of Brain Medical Sciences, the Ministry of Education, China, Huazhong University of Science and Technology, Wuhan 430022, China.
Shuaizhu ZhaoInnovation Center of Brain Medical Sciences, the Ministry of Education, China, Huazhong University of Science and Technology, Wuhan 430022, China.
Mingshu XieInnovation Center of Brain Medical Sciences, the Ministry of Education, China, Huazhong University of Science and Technology, Wuhan 430022, China.
Ruizhe ZhangWuhan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Xiaoyan LongWuhan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Lingqiang ZhuInnovation Center of Brain Medical Sciences, the Ministry of Education, China, Huazhong University of Science and Technology, Wuhan 430022, China.
Youming LuInnovation Center of Brain Medical Sciences, the Ministry of Education, China, Huazhong University of Science and Technology, Wuhan 430022, China.
Guangzhi MaSchool of Computer Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Hao LiInnovation Center of Brain Medical Sciences, the Ministry of Education, China, Huazhong University of Science and Technology, Wuhan 430022, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate analysis of social behaviors in animals is hindered by methodological challenges. Here, we develop a segmentation tracking and clustering system (STCS) to address two major challenges in computational neuroethology: reliable multi-animal tracking and pose estimation under complex interaction conditions and providing interpretable insights into social differences guided by genotype information. We established a comprehensive, long-term, multi-animal-tracking dataset across various experimental settings. Benchmarking STCS against state-of-the-art tracking algorithms, we demonstrated its superior efficacy in analyzing behavioral experiments and establishing a robust tracking baseline. By analyzing the behavior of mice with autism spectrum disorder (ASD) using a novel weakly supervised clustering method under both solitary and social conditions, STCS reveals potential links between social stress and motor impairments. Benefiting from its modular and web-based design, STCS allows researchers to easily integrate the latest computer vision methods, enabling comprehensive behavior analysis services over the Internet, even from a single laptop.

Indexed as

autism spectrum disordersbehavioral clusteringdeep learningmulti-animal trackingsocial behavior

Identifiers

PMID39568468
PMCPMC11573910

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.