@article {663460,
	title = {Extracting neural signals from semi-immobilized animals with deformable non-negative matrix factorization},
	journal = {bioRxiv},
	year = {Submitted},
	month = {08 July 2020},
	abstract = {Extracting calcium traces from populations of neurons is a critical step in the study of the large-scale neural dynamics that govern behavior. Accurate activity extraction requires the correction of motion and movement-induced deformations as well as demixing of signals that may overlap spatially due to limitations in optical resolution. Traditionally, non-negative matrix factorization (NMF) methods have been successful in demixing and denoising cellular calcium activity in relatively motionless or pre-registered videos. However, standard NMF methods fail in animals undergoing significant non-rigid motion; similarly, standard image registration methods based on template matching can fail when large changes in activity lead to mismatches with the image template. To address these issues simultaneously, we introduce a deformable non-negative matrix factorization (dNMF) framework that jointly optimizes registration with signal demixing. On simulated data and real semi-immobilized\&nbsp;C. elegans\&nbsp;microscopy videos, dNMF outperforms traditional demixing methods that account for motion and demixing separately. Finally, following the extraction of neural traces from multiple imaging experiments, we develop a quantile regression time-series normalization technique to account for varying neural signal intensity baselines across different animals or different imaging setups. Open source code implementing this pipeline is available at\&nbsp;https://github.com/amin-nejat/dNMF.},
	author = {Amin Nejatbakhsh and Erdem Varol and Eviatar Yemini and Vivek Venkatachalam and Lin, Albert and Aravinthan D. T. Samuel and Liam Paninski}
}
