Keeping track of worm trackers

C. elegans is used extensively as a model system in the neurosciences due to its well defined nervous system. However, the seeming simplicity of this nervous system in anatomical structure and neuronal connectivity, at least compared to higher animals, underlies a rich diversity of behaviors. The usefulness of the worm in genome-wide mutagenesis or RNAi screens, where thousands of strains are assessed for phenotype, emphasizes the need for computational methods for automated parameterization of generated behaviors. In addition, behaviors can be modulated upon external cues like temperature, O2 and CO2 concentrations, mechanosensory and chemosensory inputs. Different machine vision tools have been developed to aid researchers in their efforts to inventory and characterize defined behavioral “outputs”. Here we aim at providing an overview of different worm-tracking packages or video analysis tools designed to quantify different aspects of locomotion such as the occurrence of directional changes (turns, omega bends), curvature of the sinusoidal shape (amplitude, body bend angles) and velocity (speed, backward or forward movement)

Automated visual tracking for studying the ontogeny of zebrafish swimming

The zebrafish Danio rerio is a widely used model organism in studies of genetics, developmental biology, and recently, biomechanics. In order to quantify changes in swimming during all stages of development, we have developed a visual tracking system that estimates the posture of fish. Our current approach assumes planar motion of the fish, given image sequences taken from a top view. An accurate geometric fish model is automatically designed and fit to the images at each time frame. Our approach works across a range of fish shapes and sizes and is therefore well suited for studying the ontogeny of fish swimming, while also being robust to common environmental occlusions. Our current analysis focuses on measuring the influence of vertebra development on the swimming capabilities of zebrafish. We examine wild-type zebrafish and mutants with stiff vertebrae (stocksteif) and quantify their body kinematics as a function of their development from larvae to adult (mutants made available by the Hubrecht laboratory, The Netherlands). By tracking the fish, we are able to measure the curvature and net acceleration along the body that result from the fish's body wave. Here, we demonstrate the capabilities of the tracking system for the escape response of wild-type zebrafish and stocksteif mutant zebrafish. The response was filmed with a digital high-speed camera at 1500 frames s–1. Our approach enables biomechanists and ethologists to process much larger datasets than possible at present. Our automated tracking scheme can therefore accelerate insight in the swimming behavior of many species of (developing) fish

Thrifty swimming with shear-thinning

Microscale propulsion is integral to numerous biomedical systems, for example biofilm formation and human reproduction, where the surrounding fluids comprise suspensions of polymers. These polymers endow the fluid with non-Newtonian rheological properties, such as shear-thinning and viscoelasticity. Thus, the complex dynamics of non-Newtonian fluids presents numerous modelling challenges, strongly motivating experimental study. Here, we demonstrate that failing to account for "out-of-plane" effects when analysing experimental data of undulatory swimming through a shear-thinning fluid results in a significant overestimate of fluid viscosity around the model swimmer C. elegans. This miscalculation of viscosity corresponds with an overestimate of the power the swimmer expends, a key biophysical quantity important for understanding the internal mechanics of the swimmer. As experimental flow tracking techniques improve, accurate experimental estimates of power consumption using this technique will arise in similar undulatory systems, such as the planar beating of human sperm through cervical mucus, will be required to probe the interaction between internal power generation, fluid rheology, and the resulting waveform

Shape mode analysis exposes movement patterns in biology: flagella and flatworms as case studies

We illustrate shape mode analysis as a simple, yet powerful technique to concisely describe complex biological shapes and their dynamics. We characterize undulatory bending waves of beating flagella and reconstruct a limit cycle of flagellar oscillations, paying particular attention to the periodicity of angular data. As a second example, we analyze non-convex boundary outlines of gliding flatworms, which allows us to expose stereotypic body postures that can be related to two different locomotion mechanisms. Further, shape mode analysis based on principal component analysis allows to discriminate different flatworm species, despite large motion-associated shape variability. Thus, complex shape dynamics is characterized by a small number of shape scores that change in time. We present this method using descriptive examples, explaining abstract mathematics in a graphic way.Comment: 20 pages, 6 figures, accepted for publication in PLoS On

Automated Video Analysis of Animal Movements Using Gabor Orientation Filters

To quantify locomotory behavior, tools for determining the location and shape of an animal’s body are a first requirement. Video recording is a convenient technology to store raw movement data, but extracting body coordinates from video recordings is a nontrivial task. The algorithm described in this paper solves this task for videos of leeches or other quasi-linear animals in a manner inspired by the mammalian visual processing system: the video frames are fed through a bank of Gabor filters, which locally detect segments of the animal at a particular orientation. The algorithm assumes that the image location with maximal filter output lies on the animal’s body and traces its shape out in both directions from there. The algorithm successfully extracted location and shape information from video clips of swimming leeches, as well as from still photographs of swimming and crawling snakes. A Matlab implementation with a graphical user interface is available online, and should make this algorithm conveniently usable in many other contexts

Discrete modes of social information processing predict individual behavior of fish in a group

Individual computations and social interactions underlying collective behavior in groups of animals are of great ethological, behavioral, and theoretical interest. While complex individual behaviors have successfully been parsed into small dictionaries of stereotyped behavioral modes, studies of collective behavior largely ignored these findings; instead, their focus was on inferring single, mode-independent social interaction rules that reproduced macroscopic and often qualitative features of group behavior. Here we bring these two approaches together to predict individual swimming patterns of adult zebrafish in a group. We show that fish alternate between an active mode in which they are sensitive to the swimming patterns of conspecifics, and a passive mode where they ignore them. Using a model that accounts for these two modes explicitly, we predict behaviors of individual fish with high accuracy, outperforming previous approaches that assumed a single continuous computation by individuals and simple metric or topological weighing of neighbors behavior. At the group level, switching between active and passive modes is uncorrelated among fish, yet correlated directional swimming behavior still emerges. Our quantitative approach for studying complex, multi-modal individual behavior jointly with emergent group behavior is readily extensible to additional behavioral modes and their neural correlates, as well as to other species

Locomotion At Low Reynolds Number: Dynamics In Newtonian And Non-Newtonian Systems With Biomedical Applications

Swimming microorganisms such as bacteria, spermatozoa, algae, and nematodes are critical to ubiquitous biological phenomena such as disease and infection, ecosystem dynamics, and mammalian fertilization. While there has been much scientific and practical interest in studying these swimmers in Newtonian (water-like) fluids, there are fewer systematic experimental studies on swimming through non-Newtonian (non-water-like) fluids with biologically-relevant mechanical properties. These organisms commonly swim through viscoelastic, structured, or shear-rate-dependent fluids, such as blood, mucus, and living tissues. Furthermore, the small length scales of these organisms dictate that their motion is dominated by viscous forces and inertia is negligible. Using rheology, microscopy, particle tracking, and image processing techniques, we examine the interaction of low Reynolds number swimmers and non-Newtonian fluids including viscoelastic, locally-anisotropic, and shear-thinning fluids. We then apply our understanding of locomotion to the study of the genetic disease Spinal Muscular Atrophy