ArticlePLoS computational biology2021
Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Does advancement in marker-less pose-estimation mean more quality research? A systematic review.Frontiers in behavioral neuroscience · 2025Pooled it
- Article
- Animal behavioral analysis and neural encoding with transformer-based self-supervised pretraining.ArXiv · 2026Article
- Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data.ArXiv · 2025Article
- Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data.Proceedings of machine learning research · 2025Article
- Shared-AE: Automatic Identification of Shared Subspaces in High-dimensional Neural and Behavioral Activity.... International Conference on Learning Representations · 2025Article
- Article
- Biomarker discovery using machine learning in the psychosis spectrum.Biomarkers in neuropsychiatry · 2024Article
- Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models.Cell reports · 2024Article
- Behavioral Coding of Captive African Elephants (Animals : an open access journal from MDPI · 2024Article
- Vocal Call Locator Benchmark (VCL) for localizing rodent vocalizations from multi-channel audio.bioRxiv : the preprint server for biology · 2024Article
- Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience.Nature neuroscience · 2024Article
- Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools.bioRxiv : the preprint server for biology · 2024Article
- A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms.Neurons, behavior, data analysis, and theory · 2024Article
- Facemap: a framework for modeling neural activity based on orofacial tracking.Nature neuroscience · 2024Article
- Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics.bioRxiv : the preprint server for biology · 2023Article
- Article
- Manipulation-specific cortical activity as mice handle food.Current biology : CB · 2022Article
- Identifying behavioral structure from deep variational embeddings of animal motion.Communications biology · 2022Article
- Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis.Neuron · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
Abstract
Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.