deeplabcut  (Noldus Information Technology)

Journal: bioRxiv

Article Title: VTA dopamine neurons drive spatiotemporally heterogeneous striatal dopamine signals during learning

doi: 10.1101/2023.07.01.547331

Figure Lengend Snippet: A) Behavior data was extracted from video recordings using the DeepLabCut pipeline. B) Video frames containing experimenter labeled rat body parts (ears, nose, tail base, etc) and static features of the environment (cue, chamber corners), were used to train a deep learning network to estimate frame by frame x,y coordinates for the entire video data set. This data was used to calculate movement and position information across optogenetic Pavlovian conditioning. C) Head direction was interpolated by calculating the angle between the vector projecting from the cranial implant to the nose with the vector projecting from the cranial implant to the cue. D) Example trials showing linear cue-evoked movement. Each dot represents the position of the rat’s nose on an individual video frame, on 5 different trials early in training. E) Example trials showing rotational movement during 5 trials late in training. F) Polar plots showing distribution of head direction angles for an example rat across training.

Article Snippet: Markerless tracking of animal body parts was conducted using version 2.2.1.1 of the DeepLabCut (DLC) Toolbox 37,41 and analysis of movement features based on these tracked coordinates was conducted in Matlab R2020b (Mathworks).

Techniques: Labeling, Plasmid Preparation