Bjoern Brembs

Open source tracking and analysis of adult Drosophila locomotion in Buridan's paradigm with and without visual targets

Background Insects have been among the most widely used model systems for studying the control of locomotion by nervous systems. In Drosophila, we implemented a simple test for locomotion: in Buridans paradigm, flies walk back and forth between two inaccessible visual targets [1]. Until today, the lack of easily accessible tools for tracking the fly position and analyzing its trajectory has probably contributed to the slow acceptance of Buridans paradigm. Methodology/Principal Findings We present here a package of open source software designed to track a single animal walking in a homogenous environment (Buritrack) and to analyze its trajectory. The Centroid Trajectory Analysis (CeTrAn) software is coded in the open source statistics project R. It extracts eleven metrics and includes correlation analyses and a Principal Components Analysis (PCA). It was designed to be easily customized to personal requirements. In combination with inexpensive hardware, these tools can readily be used for teaching and research purposes. We demonstrate the capabilities of our package by measuring the locomotor behavior of adult Drosophila melanogaster (whose wings were clipped), either in the presence or in the absence of visual targets, and comparing the latter to different computer-generated data. The analysis of the trajectories confirms that flies are centrophobic and shows that inaccessible visual targets can alter the orientation of the flies without changing their overall patterns of activity. Conclusions/Significance Using computer generated data, the analysis software was tested, and chance values for some metrics (as well as chance value for their correlation) were set. Our results prompt the hypothesis that fixation behavior is observed only if negative phototaxis can overcome the propensity of the flies to avoid the center of the platform. Together with our companion paper, we provide new tools to promote Open Science as well as the collection and analysis of digital behavioral data.

Operant Learning of Drosophila at the Torque Meter

For experiments at the torque meter, flies are kept on standard fly medium at 25°C and 60% humidity with a 12hr light/12hr dark regime. A standardized breeding regime assures proper larval density and age-matched cohorts. Cold-anesthetized flies are glued with head and thorax to a triangle-shaped hook the day before the experiment. Attached to the torque meter via a clamp, the flys intended flight maneuvers are measured as the angular momentum around its vertical body axis. The fly is placed in the center of a cylindrical panorama to accomplish stationary flight. An analog to digital converter card feeds the yaw torque signal into a computer which stores the trace for later analysis. The computer also controls a variety of stimuli which can be brought under the flys control by closing the feedback loop between these stimuli and the yaw torque trace. Punishment is achieved by applying heat from an adjustable infrared laser.

A decision underlies phototaxis in an insect

Like a moth into the flame—phototaxis is an iconic example for innate preferences. Such preferences probably reflect evolutionary adaptations to predictable situations and have traditionally been conceptualized as hard-wired stimulus–response links. Perhaps for that reason, the century-old discovery of flexibility in Drosophila phototaxis has received little attention. Here, we report that across several different behavioural tests, light/dark preference tested in walking is dependent on various aspects of flight. If we temporarily compromise flying ability, walking photopreference reverses concomitantly. Neuronal activity in circuits expressing dopamine and octopamine, respectively, plays a differential role in photopreference, suggesting a potential involvement of these biogenic amines in this case of behavioural flexibility. We conclude that flies monitor their ability to fly, and that flying ability exerts a fundamental effect on action selection in Drosophila. This work suggests that even behaviours which appear simple and hard-wired comprise a value-driven decision-making stage, negotiating the external situation with the animals internal state, before an action is selected.

PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila

Tethering a fly for stationary flight allows for exquisite control of its sensory input, such as visual or olfactory stimuli or a punishing infrared laser beam. A torque meter measures the turning attempts of the tethered fly around its vertical body axis. By punishing, say, left turning attempts (in a homogeneous environment), one can train a fly to restrict its behaviour to right turning attempts. It was recently discovered that this form of operant conditioning (called operant self-learning), may constitute a form of motor learning in Drosophila. Previous work had shown that Protein Kinase C (PKC) and the transcription factor dFoxP were specifically involved in self-learning, but not in other forms of learning. These molecules are specifically involved in various forms of motor learning in other animals, such as compulsive biting in Aplysia, song-learning in birds, procedural learning in mice or language acquisition in humans. Here we describe our efforts to decipher which PKC gene is involved in self-learning in Drosophila. We also provide evidence that motorneurons may be one part of the neuronal network modified during self-learning experiments. The collected evidence is reminiscent of one of the simplest, clinically relevant forms of motor learning in humans, operant reflex conditioning, which also relies on motorneuron plasticity.

Trajectory parameters of real flies and computer-generated data (grouping codes given below the graph).

Both A. Meander (turning angle divided by speed) and B. stripe deviation are similar in fly and computer-generated data. Red line denotes 45°, the mean value for computer-generated data. C–D. Centrophobism score for sitting (C) or for moving (D) is positive only for fly data. E. The distance traveled is different between the three types of data. Bars represent means and error bars standard errors, asterisks denote significant differences after a MANOVA analysis, n = 20 in each group.

Calculation of angles and number of walks.

A. The inner circle represents the platform, while the outer circle represents the arena and the light source (to scale). The bars represent the stripes (wide or narrow). Considering the movement from P₀ to P₁, α₀ is the absolute movement angle (similarly α₋₁} is the absolute movement angle of the movement P₋₁} to P₀). The turning angle γ can be calculated as α₀ - α₋₁}, it represents the change in direction at time 0. β is the “stripe deviation” angle, the angle from the movement to a vector going straight toward the middle of the stripe that is in the direction of the movement. In the “ltraj” object, α is assigned to P₀, β to P₁. Gray areas denote the sectors used to start and end a walk between stripes: a walk is counted for each passage from one gray area to the other. B. Trajectory example, zoomed on the platform size. The disposition of the stripes are at 90 and −90° as in A. Dots represent the position of the fly during the three first minutes of a test with narrow stripes, after down sampling to 10 Hz.