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High-throughput ethomics in large groups of Drosophila.

Branson K, Robie AA, Bender J, Perona P, Dickinson MH - Nat. Methods (2009)

Bottom Line: Our system includes machine-vision algorithms that accurately track many individuals without swapping identities and classification algorithms that detect behaviors.The data may be represented as an ethogram that plots the time course of behaviors exhibited by each fly or as a vector that concisely captures the statistical properties of all behaviors displayed in a given period.We expect that our software, which permits high-throughput screening, will complement existing molecular methods available in Drosophila, facilitating new investigations into the genetic and cellular basis of behavior.

View Article: PubMed Central - PubMed

Affiliation: California Institute of Technology, Pasadena, California, USA.

ABSTRACT
We present a camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting in a planar arena. Our system includes machine-vision algorithms that accurately track many individuals without swapping identities and classification algorithms that detect behaviors. The data may be represented as an ethogram that plots the time course of behaviors exhibited by each fly or as a vector that concisely captures the statistical properties of all behaviors displayed in a given period. We found that behavioral differences between individuals were consistent over time and were sufficient to accurately predict gender and genotype. In addition, we found that the relative positions of flies during social interactions vary according to gender, genotype and social environment. We expect that our software, which permits high-throughput screening, will complement existing molecular methods available in Drosophila, facilitating new investigations into the genetic and cellular basis of behavior.

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Differences within and among individual flies. (a) The first and second halves of trajectories for three male and three female flies from the same trial. (b) Scatter plots of walking statistics from each individual fly in the first 15 minutes of its trajectory against the same statistics from the last 15 minutes of its trajectory for flies in all trial types (female n = 132, male n = 159). M = male, F = female, B = both male and female. Walking statistics examined were: (left) Mean speed in frames in which fly was classified as walking: r = 0.889, P < 2.2 × 10-16 (r, Pearson’s correlation coefficient; P, the probability that the  hypothesis of r non-positive is correct), (center) Fraction of frames fly is classified as walking: r = 0.689, P < 2.2× 10-16 (right) Mean duration of sequences of consecutive walking frames: r = 0.765, P < 2.2× 10-16. (c) Chasing behavior differences. We repeated the above procedure for chasing behavioral statistics: (left) Frequency with which the fly begins chasing another fly: r = 0.592, P = 3.89× 10-16, (center) frequency with which a fly is chased by another fly: r = 0.213, P = 1.54× 10-03, and (right) mean duration of chases: r = 0.054, P = 0.261.
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Figure 4: Differences within and among individual flies. (a) The first and second halves of trajectories for three male and three female flies from the same trial. (b) Scatter plots of walking statistics from each individual fly in the first 15 minutes of its trajectory against the same statistics from the last 15 minutes of its trajectory for flies in all trial types (female n = 132, male n = 159). M = male, F = female, B = both male and female. Walking statistics examined were: (left) Mean speed in frames in which fly was classified as walking: r = 0.889, P < 2.2 × 10-16 (r, Pearson’s correlation coefficient; P, the probability that the hypothesis of r non-positive is correct), (center) Fraction of frames fly is classified as walking: r = 0.689, P < 2.2× 10-16 (right) Mean duration of sequences of consecutive walking frames: r = 0.765, P < 2.2× 10-16. (c) Chasing behavior differences. We repeated the above procedure for chasing behavioral statistics: (left) Frequency with which the fly begins chasing another fly: r = 0.592, P = 3.89× 10-16, (center) frequency with which a fly is chased by another fly: r = 0.213, P = 1.54× 10-03, and (right) mean duration of chases: r = 0.054, P = 0.261.

Mentions: To demonstrate that these ethograms are powerful descriptors of behavior, we tested whether we could predict the sex of a fly (male vs. female) and its genotype (wild type males vs. fru1/fru1 male), based solely on components of the automatically-generated behavioral vector (Fig. 3e). We found that predictors based on the statistics of each of the eight behaviors independently distinguished sex with accuracies all better than chance, with touch frequency performing best (96.8% accuracy), and sharp turn frequency performing best of the locomotor behaviors (83.9% accuracy). A predictor based on the combination of all behaviors had an accuracy of 96.9%. Even a predictor based solely on locomotor behaviors (excluding touches and chases) predicted sex with an accuracy of 95.5%. We emphasize that we are not advocating using behavioral statistics for sexing flies. Our mixed-sex trials (Figs. 4 and 5) used a fly’s median image area for determining sex, a technique that achieves 96.2% accuracy. Instead, these behavior prediction accuracies are evidence that the ethograms are strongly correlated with gender.


High-throughput ethomics in large groups of Drosophila.

Branson K, Robie AA, Bender J, Perona P, Dickinson MH - Nat. Methods (2009)

Differences within and among individual flies. (a) The first and second halves of trajectories for three male and three female flies from the same trial. (b) Scatter plots of walking statistics from each individual fly in the first 15 minutes of its trajectory against the same statistics from the last 15 minutes of its trajectory for flies in all trial types (female n = 132, male n = 159). M = male, F = female, B = both male and female. Walking statistics examined were: (left) Mean speed in frames in which fly was classified as walking: r = 0.889, P < 2.2 × 10-16 (r, Pearson’s correlation coefficient; P, the probability that the  hypothesis of r non-positive is correct), (center) Fraction of frames fly is classified as walking: r = 0.689, P < 2.2× 10-16 (right) Mean duration of sequences of consecutive walking frames: r = 0.765, P < 2.2× 10-16. (c) Chasing behavior differences. We repeated the above procedure for chasing behavioral statistics: (left) Frequency with which the fly begins chasing another fly: r = 0.592, P = 3.89× 10-16, (center) frequency with which a fly is chased by another fly: r = 0.213, P = 1.54× 10-03, and (right) mean duration of chases: r = 0.054, P = 0.261.
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Figure 4: Differences within and among individual flies. (a) The first and second halves of trajectories for three male and three female flies from the same trial. (b) Scatter plots of walking statistics from each individual fly in the first 15 minutes of its trajectory against the same statistics from the last 15 minutes of its trajectory for flies in all trial types (female n = 132, male n = 159). M = male, F = female, B = both male and female. Walking statistics examined were: (left) Mean speed in frames in which fly was classified as walking: r = 0.889, P < 2.2 × 10-16 (r, Pearson’s correlation coefficient; P, the probability that the hypothesis of r non-positive is correct), (center) Fraction of frames fly is classified as walking: r = 0.689, P < 2.2× 10-16 (right) Mean duration of sequences of consecutive walking frames: r = 0.765, P < 2.2× 10-16. (c) Chasing behavior differences. We repeated the above procedure for chasing behavioral statistics: (left) Frequency with which the fly begins chasing another fly: r = 0.592, P = 3.89× 10-16, (center) frequency with which a fly is chased by another fly: r = 0.213, P = 1.54× 10-03, and (right) mean duration of chases: r = 0.054, P = 0.261.
Mentions: To demonstrate that these ethograms are powerful descriptors of behavior, we tested whether we could predict the sex of a fly (male vs. female) and its genotype (wild type males vs. fru1/fru1 male), based solely on components of the automatically-generated behavioral vector (Fig. 3e). We found that predictors based on the statistics of each of the eight behaviors independently distinguished sex with accuracies all better than chance, with touch frequency performing best (96.8% accuracy), and sharp turn frequency performing best of the locomotor behaviors (83.9% accuracy). A predictor based on the combination of all behaviors had an accuracy of 96.9%. Even a predictor based solely on locomotor behaviors (excluding touches and chases) predicted sex with an accuracy of 95.5%. We emphasize that we are not advocating using behavioral statistics for sexing flies. Our mixed-sex trials (Figs. 4 and 5) used a fly’s median image area for determining sex, a technique that achieves 96.2% accuracy. Instead, these behavior prediction accuracies are evidence that the ethograms are strongly correlated with gender.

Bottom Line: Our system includes machine-vision algorithms that accurately track many individuals without swapping identities and classification algorithms that detect behaviors.The data may be represented as an ethogram that plots the time course of behaviors exhibited by each fly or as a vector that concisely captures the statistical properties of all behaviors displayed in a given period.We expect that our software, which permits high-throughput screening, will complement existing molecular methods available in Drosophila, facilitating new investigations into the genetic and cellular basis of behavior.

View Article: PubMed Central - PubMed

Affiliation: California Institute of Technology, Pasadena, California, USA.

ABSTRACT
We present a camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting in a planar arena. Our system includes machine-vision algorithms that accurately track many individuals without swapping identities and classification algorithms that detect behaviors. The data may be represented as an ethogram that plots the time course of behaviors exhibited by each fly or as a vector that concisely captures the statistical properties of all behaviors displayed in a given period. We found that behavioral differences between individuals were consistent over time and were sufficient to accurately predict gender and genotype. In addition, we found that the relative positions of flies during social interactions vary according to gender, genotype and social environment. We expect that our software, which permits high-throughput screening, will complement existing molecular methods available in Drosophila, facilitating new investigations into the genetic and cellular basis of behavior.

Show MeSH