bioRxivNeurosciencePreprint
Representation of Drosophila larval behaviors by muscle activity patterns
Jinrun ZhouiD, Zenan HuangiD, Xinhang LiiD, Zhiying Song, Yixuan SuniD, Junyu Ping, Xiaopeng CheniD, Peng FeiiD, Nenggan ZhengiD, Zhefeng GongiD
Abstract
How muscle actions are coordinated to realize animal movement is a fundamental question in behavioral study. To obtain the overall muscular activity patterns accompanying behaviors at high spatiotemporal resolution is technically difficult. In this work, we used light sheet microscopy to simultaneously image and analyze the activity, length and orientation of Drosophila larval muscles across body segments at single muscle resolution in nearly free behaviors. For typical behavioral modes such as peristalsis, head cast and turning, larval muscles showed behavioral mode specific activity patterns. Unexpectedly, reorientation of larval head involves muscle tone in the apparently motionless posterior segments. With a STGCN(spatial temporal graph convolution neural network)-Generator model, sequence of larval behavioral poses outlined by morphological patterns of muscles could be accurately predicted based on the time series of ventral but not dorsal muscle activities, and vice versa. Laser ablation of ventral but not dorsal muscles interrupted peristaltic wave and undermined head cast in both frequency and amplitude. Our results provide a simplified muscle activity representation of soft body motion that can be used for probing the key components of animal motor control.
- organism: Drosophila
- assay: imaging
- research focus: trajectory inference
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: graph neural network
- Citations
- 1
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