Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.
PLoS Comput Biol
; 17(9): e1009439, 2021 09.
Article
in En
| MEDLINE
| ID: mdl-34550974
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.
Full text:
1
Collection:
01-internacional
Health context:
1_ASSA2030
Database:
MEDLINE
Main subject:
Video Recording
/
Behavior, Animal
/
Algorithms
/
Artificial Intelligence
Type of study:
Health_economic_evaluation
/
Risk_factors_studies
Limits:
Animals
Language:
En
Journal:
PLoS Comput Biol
Year:
2021
Document type:
Article