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About the Authors:
Jeffrey P. Nguyen
Affiliation: Department of Physics, Princeton University, Princeton, New Jersey, United States of America
Ashley N. Linder
Affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
George S. Plummer
Current address: Tufts University School of Medicine, Boston, Massachusetts, United States of America
Affiliation: Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
Joshua W. Shaevitz
Affiliations Department of Physics, Princeton University, Princeton, New Jersey, United States of America, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
Andrew M. Leifer
* E-mail: [email protected]
Affiliations Department of Physics, Princeton University, Princeton, New Jersey, United States of America, Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
ORCID http://orcid.org/0000-0002-5362-5093Abstract
Advances in optical neuroimaging techniques now allow neural activity to be recorded with cellular resolution in awake and behaving animals. Brain motion in these recordings pose a unique challenge. The location of individual neurons must be tracked in 3D over time to accurately extract single neuron activity traces. Recordings from small invertebrates like C. elegans are especially challenging because they undergo very large brain motion and deformation during animal movement. Here we present an automated computer vision pipeline to reliably track populations of neurons with single neuron resolution in the brain of a freely moving C. elegans undergoing large motion and deformation. 3D volumetric fluorescent images of the animal’s brain are straightened, aligned and registered, and the locations of neurons in the images are found via segmentation. Each neuron is then assigned an identity using a new time-independent machine-learning approach we call Neuron Registration Vector Encoding. In this approach, non-rigid point-set registration is used to match each segmented neuron in each volume with a set of reference volumes taken from throughout the recording. The way each neuron matches with the references defines a feature vector which is clustered to assign an identity to each neuron in each volume. Finally, thin-plate spline interpolation is used to correct errors in segmentation and check consistency of assigned identities. The Neuron Registration Vector Encoding approach proposed here is uniquely well suited for tracking neurons in brains undergoing large deformations. When applied to whole-brain calcium imaging recordings in freely moving C....