Detecting Social Actions of Fruit Flies

Eyjolfsdottir, Eyrun; Branson, Steve; Burgos-Artizzu, Xavier P.; Hoopfer, Eric D.; Schor, Jonathan S; Anderson, David J.; Perona, Pietro

doi:10.1007/978-3-319-10605-2_50

Cited by 106 publications

(171 citation statements)

References 25 publications

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“…Recently several techniques have been developed to track social behaviors in animals with rigid exoskeletons, such as the fruit fly Drosophila, which have relatively few degrees of freedom in their movements (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23). These techniques have had a transformative impact on the study of social behaviors in that species (2).…”

mentioning

confidence: 99%

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

Hong

Kennedy

Burgos-Artizzu

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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273

234

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A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body "pose" of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.

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mentioning

confidence: 99%

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

Hong

Kennedy

Burgos-Artizzu

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

273

234

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“…Videos were processed using a custom batch processing script that cropped videos into single arena videos and uncompressed to avi format. These preprocessed videos were then tracked using FlyTracker software (Eyjolfsdottir et al, 2014). The tracker returned the centroid position of the fly, orientation and instantaneous translational velocity.…”

Section: Analysis Of Walking During Activation Assaysmentioning

confidence: 99%

Two brain pathways initiate distinct forward walking programs inDrosophila

Bidaye¹,

Laturney²,

Chang³

et al. 2019

Preprint

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An animal at rest or engaged in stationary behaviors can instantaneously initiate goaldirected walking. How descending brain inputs trigger rapid transitions from a non-walking state to an appropriate walking state is unclear. Here, we identify two specific neuronal classes in the Drosophila brain that drive two distinct forward walking programs in a context-specific manner.The first class, named P9, consists of descending neurons that drive forward walking with ipsilateral turning. P9 receives inputs from central courtship-promoting neurons and visual projection neurons and is necessary for a male to track a female during courtship. The second class comprises novel, higher order neurons, named BPN, that drives straight, forward walking. BPN is required for high velocity walking and is active during long, fast, straight walking bouts. Thus, this study reveals separate brain pathways for object-directed steering and fast straight walking, providing insight into how the brain initiates different walking programs.Raw and processed data along with custom Matlab analysis scripts will be made available upon request. Design files for custom parts will be available upon request.Ache, J.M., Haupt, S.S., and Dürr, V. (2015). A direct descending pathway informing locomotor networks about tactile sensor movement.

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“…Most of these methods have been used for behavioral studies. [23][24][25] However, a multi-camera system was developed to capture frames from flying insects in real-time. 26 There, an extended KF and the nearest neighbor standard filter data association algorithm were used to track multiple targets.…”

Section: Introductionmentioning

confidence: 99%

Three‐dimensional‐based landmark tracker employing a superpixels method for neuroscience, biomechanics, and biology studies

Maghsoudi

Vahedipour

Spence

2019

Int J Imaging Syst Tech

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Examining locomotion has improved our basic understanding of motor control and aided in treating motor impairment. Mice and rats are premier models of human disease and increasingly the model systems of choice for basic neuroscience. High frame rates (250 Hz) are needed to quantify the kinematics of these running rodents. Manual tracking, especially for multiple markers, becomes time‐consuming and impossible for large sample sizes. Therefore, the need for automatic segmentation of these markers has grown in recent years. Here, we address this need by presenting a method to segment the markers using the simple linear iterative clustering superpixel method. The two‐dimensional coordinates on the image plane are projected to a three‐dimensional (3D) domain using direct linear transform and a 3D Kalman filter has been used to predict the position of markers based on the speed and position of markers from the previous frames. A probabilistic function is used to find the best match from among superpixels. The method is evaluated for different difficulties for tracking of the markers, and it achieves 95% correct labeling of markers. Finally, the method was used to analyze data from an ongoing study seeking to improve recovery from spinal cord injury in rats

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Detecting Social Actions of Fruit Flies

Cited by 106 publications

References 25 publications

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

Two brain pathways initiate distinct forward walking programs inDrosophila

Three‐dimensional‐based landmark tracker employing a superpixels method for neuroscience, biomechanics, and biology studies

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