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Advances in optical neuroimaging techniques now allow neural activity to berecorded with cellular resolution in awake and behaving animals. Brain motionin these recordings pose a unique challenge. The location of individual neuronsmust be tracked in 3D over time to accurately extract single neuron activitytraces. Recordings from small invertebrates like C. elegans are especiallychallenging because they undergo very large brain motion and deformation duringanimal movement. Here we present an automated computer vision pipeline toreliably track populations of neurons with single neuron resolution in thebrain of a freely moving C. elegans undergoing large motion and deformation. 3Dvolumetric fluorescent images of the animal's brain are straightened, alignedand registered, and the locations of neurons in the images are found viasegmentation. Each neuron is then assigned an identity using a newtime-independent machine-learning approach we call Neuron Registration VectorEncoding. In this approach, non-rigid point-set registration is used to matcheach segmented neuron in each volume with a set of reference volumes taken fromthroughout the recording. The way each neuron matches with the referencesdefines a feature vector which is clustered to assign an identity to eachneuron in each volume. Finally, thin-plate spline interpolation is used tocorrect errors in segmentation and check consistency of assigned identities.The Neuron Registration Vector Encoding approach proposed here is uniquely wellsuited for tracking neurons in brains undergoing large deformations. Whenapplied to whole-brain calcium imaging recordings in freely moving C. elegans,this analysis pipeline located 150 neurons for the duration of an 8 minuterecording and consistently found more neurons more quickly than manual orsemi-automated approaches.

Automatically tracking neurons in a moving and deforming brain