Tracked keypoints of walking flies used in: Miniature linear and split-belt treadmills reveal mechanisms of adaptive motor control in walking Drosophila
Creators: name: Pratt, Brandon
name: Lee, Su-Yee
name: Chou, Grant
name: Tuthill, John
Publisher: name: Zenodo
Year: 2024
Subjects: Drosophila melanogaster
Walking
Kinematics
Behavioral neuroscience
sensorimotor control
locomotion
motor adaptation
treadmill

description: To navigate complex environments, walking animals must detect and overcome unexpected perturbations. One technical challenge when investigating adaptive locomotion is measuring behavioral responses to precise perturbations during naturalistic walking; another is that experimentally silencing neurons in sensorimotor circuits often reduces spontaneous locomotion. To overcome these obstacles, we introduce miniature treadmill systems for coercing locomotion and tracking 3D kinematics of walking Drosophila. By systematically comparing walking in three experimental setups, we show that flies compelled to walk on the linear treadmill have similar stepping kinematics to freely walking flies, while kinematics of tethered walking flies are subtly different. Genetically silencing mechanosensory neurons alters step kinematics of flies walking on the linear treadmill across all speeds, while inter-leg coordination remains intact. We also found that flies can maintain a forward heading on a split-belt treadmill by adapting the step distance of their middle legs. Overall, these insights demonstrate the utility of miniature treadmills for studying insect locomotion.
description: Funding provided by: National Science FoundationCrossref Funder Registry ID: https://ror.org/021nxhr62Award Number: 2018261272
Funding provided by: National Institutes of HealthCrossref Funder Registry ID: https://ror.org/01cwqze88Award Number: NS 99578-3
Funding provided by: National Institutes of HealthCrossref Funder Registry ID: https://ror.org/01cwqze88Award Number: R01NS102333
Funding provided by: National Institutes of HealthCrossref Funder Registry ID: https://ror.org/01cwqze88Award Number: U19NS104655
Funding provided by: Searle Scholar Award*Crossref Funder Registry ID: Award Number: 
Funding provided by: Klingenstein-Simons Fellowship*Crossref Funder Registry ID: Award Number: 
Funding provided by: Pew Biomedical Scholar Award*Crossref Funder Registry ID: Award Number: 
Funding provided by: McKnight Scholar Award*Crossref Funder Registry ID: Award Number: 
Funding provided by: Sloan Research Fellowship*Crossref Funder Registry ID: Award Number: 
Funding provided by: New York Stem Cell FoundationCrossref Funder Registry ID: https://ror.org/03n2a3p06Award Number: 
Funding provided by: UW Innovation Award*Crossref Funder Registry ID: Award Number:
description: The datasets within this repository were collected by recording flies with high-speed cameras walking on a mini treadmill (linear or split-belt treadmill), freely in a featureless arena, or on a ball suspended in air while tethered. Flies on the linear treadmill and split-belt treadmills were recorded with 5 high-speed cameras at 180 and 200 fps, respectively. Flies walking in the arena were recorded at 150 fps with a top-down camera. Lastly, tethered flies walking on the ball were recorded with 6 high-speed cameras at 300 fps. We then used modern pose estimation tools (DeepLabCut and Anipose for treadmill and tethered walking flies, and SLEAP for freely walking flies) to extract 2D or 3D positions for labeled key points (i.e. points on the body and leg tips). We then computed walking kinematics from these positions using Python. Please refer to the corresponding paper for more details on data collection and processing.