The behavior of the nematode Caenorhabditis elegans has proven increasingly useful for the genetic dissection of neurobiological signaling pathways and for investigating the neural and molecular basis of nervous system function. Locomotion is among the most complex aspects of C. elegans behavior, and involves a number of discrete motor activities such as omega bends (deep bends typically on the ventral side of the body which reorient the direction of forward locomotion) and reversals (changes in the direction of the locomotion wave that cause a switch from forward to backward crawling). Reliable methods for detecting and quantifying these movements are critical for escape reflexes and navigation behaviors. Here we describe a novel algorithm to automatically detect omega bends, which relies in part on a new method for obtaining a morphological skeleton describing the body posture of coiled worms. We also present an optimized algorithm to detect reversals, which showed improved performance over previously described methods. Together, these new algorithms have made it possible to reliably detect events that are time-consuming and laborious to detect by real-time observation or human video analysis. They have also made it possible to identify mutants with subtle behavioral abnormalities, such as those in which omega bends are dorsoventrally unbiased or uncorrelated with reversals. These methods should therefore facilitate quantitative analysis of a wide range of locomotion-related behaviors in this important neurobiological model organism.

Foraging is a rapid, side-to-side movement of the nose generated by Caenorhabditis elegans as it explores its environment. In this paper, we present an automated method to detect and analyze foraging behavior of C. elegans in a video sequence. Several morphological image-processing methods are used to locate the precise nose position of the worm in each image. Then foraging events are detected by measuring the bending angle of the nose and investigating the overall bending curve using periodograms. We measure foraging-related parameters which have not previously been studied. The algorithm has applications in classifying and characterizing genetic mutations associated with this behavior.

We present a method for tracking and distinguishing multiple C. elegans in a video sequence, including when they are in physical contact with one another. The worms are modeled with an articulated model composed of rectangular blocks, arranged in a deformable configuration represented by a spring-like connection between adjacent parts. Dynamic programming is applied to reduce the computational complexity of the matching process. Our method makes it possible to identify two worms correctly before and after they touch each other, and to find body poses for feature extraction.

We present a method for tracking and distinguishing multiple C. elegans in a video sequence, including when they are in physical contact with one another. The worms are modeled with an articulated model composed of rectangular blocks, arranged in a deformable configuration represented by a spring-like connection between adjacent parts. Dynamic programming is applied to reduce the computational complexity of the matching process. Our method makes it possible to identify two worms correctly before and after they touch each other, and to find the body poses for further feature extraction. All joint points in our model can be also considered to be the pseudo skeleton points of the worm body. It solves the problem that a previously presented morphological skeleton-based reversal detection algorithm fails when two worms touch each other. The algorithm has many applications in the study of physical interactions between C. elegans.

foraging [5], and the G-protein alpha-subunit gene Foraging is a rapid, side-to-side movement of the nose goa-1 as well as other genes in the Go/Gq signaling generated by C. elegans as it explores its environment.pathway affect the rate of foraging [6]. However, the We present an automated method to detect and analyze precise nature of the foraging movements in wild-type foraging behavior in a video sequence. Morphological and mutant strains has not been characterized. image processing methods are used to locate the nose We provide the first quantitative description of position of the worm in each image. Then foraging foraging movements in C. elegans. Using video data events are detected by measuring the bending angle of collected with an automated tracking system, we detect the nose and analyzing it with periodograms. We foraging events and measure the depth and frequency measure foraging-related parameters which have not of nose bends. These analyses provide more precise previously been studied. The algorithm can be used to methods for defining the effects of specific genes and characterize genetic mutations associated with this neurons on C. elegans behavior. In section 2, we behavior.describe the foraging detection algorithm, including image acquisition and pre-processing. In section 3, we test the algorithm on a variety of videos of mutant