Leg-tracking and automated behavioural classification in Drosophila

Kain, Jamey; Stokes, Chris R.; Gaudry, Quentin; Song, Xiangzhi; Foley, James W.; Wilson, Rachel I.; Bivort, Benjamin de

doi:10.1038/ncomms2908

Cited by 97 publications

(129 citation statements)

References 23 publications

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“…These findings are consistent with behavioral individuality in Drosophila phototaxis (Kain et al 2012), locomotor handedness (Buchanan et al 2015) and thermal preference . However, the clear evidence of individuality in PCA 20 -GMM-SW cluster abundance distributions is notable when compared to the lack of signal of such individuality in the supervised classification results of Kain et al (2013). This indicates that despite the challenges in evaluating and implementing unsupervised methods, relatively unbiased clustering approaches may indeed live up to their promise of providing more power to map behavior.…”

Section: Discussionmentioning

confidence: 99%

“…2-6 day old animals were marked with dye as described elsewhere (Kain et al 2013), and recorded on the legtracker for 2 h at room temperature and approximately 15% humidity, under white LED lighting. Digital video was acquired for the first 10 min of each wild type experiment using a filter which blocked the HeNe laser emission used to excite the dye.…”

Section: Fly Husbandry and Experimentsmentioning

confidence: 99%

“…Data pre-processing and error correction The raw data from the instrument (Kain et al 2013) was pre-processed before being subjected to analysis. First we used a 3-frame median filter to reject singleframe noise in the data, then we resampled the variable-rate instrument data to 100 Hz.…”

Section: Fly Husbandry and Experimentsmentioning

confidence: 99%

“…Our data consists of a 15-dimensional 100 Hz time series: the x-and y-coordinates of each of the six legs, and the three rotational velocities of the trackball as published by Kain et al (2013) and five recordings from nan flies which are new to this publication. The first step in all of our mapping methods is a wavelet transform which might be included in most analyses of behavior.…”

Section: Data Preparationmentioning

confidence: 99%

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Systematic exploration of unsupervised methods for mapping behavior

Todd

Kain²,

Bivort

2017

Phys. Biol.

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To fully understand the mechanisms giving rise to behavior, we need to be able to precisely measure it. When coupled with large behavioral data sets, unsupervised clustering methods offer the potential of unbiased mapping of behavioral spaces. However, unsupervised techniques to map behavioral spaces are in their infancy, and there have been few systematic considerations of all the methodological options. We compared the performance of seven distinct mapping methods in clustering a wavelettransformed data set consisting of the x-and y-positions of the six legs of individual flies. Legs were automatically tracked by small pieces of fluorescent dye, while the fly was tethered and walking on an air-suspended ball. We find that there is considerable variation in the performance of these mapping methods, and that better performance is attained when clustering is done in higher dimensional spaces (which are otherwise less preferable because they are hard to visualize). High dimensionality means that some algorithms, including the non-parametric watershed cluster assignment algorithm, cannot be used. We developed an alternative watershed algorithm which can be used in high-dimensional spaces when a probability density estimate can be computed directly. With these tools in hand, we examined the behavioral space of fly leg postural dynamics and locomotion. We find a striking division of behavior into modes involving the fore legs and modes involving the hind legs, with few direct transitions between them. By computing behavioral clusters using the data from all flies simultaneously, we show that this division appears to be common to all flies. We also identify individual-to-individual differences in behavior and behavioral transitions. Lastly, we suggest a computational pipeline that can achieve satisfactory levels of performance without the taxing computational demands of a systematic combinatorial approach.

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Section: Discussionmentioning

confidence: 99%

Section: Fly Husbandry and Experimentsmentioning

confidence: 99%

Section: Fly Husbandry and Experimentsmentioning

confidence: 99%

Section: Data Preparationmentioning

confidence: 99%

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Systematic exploration of unsupervised methods for mapping behavior

Todd

Kain²,

Bivort

2017

Phys. Biol.

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“…This is a hard-wired behaviour but is influenced by environmental factors, such as desiccation (Perttunen, 1963). It is also influenced by normal behavioural variation within a fly strain (Kain et al, 2013). For example, Kain et al (2013) …”

Section: Dscam2 Mutant Flies Exhibit Reduced Phototaxismentioning

confidence: 99%

Analysing visual behaviour in Drosophila Dscam2 mutants using optimised optomotor and operant control assays

Bosch¹

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The patterns of synaptic connections between neurons in the brain are key determinants of behaviour, but how specific neural circuits lead to distinct behaviours is largely unknown. Cell recognition molecules that mediate interactions between neurons play a crucial role in establishing synaptic connections. Down Syndrome Cell Adhesion Molecule 2 (Dscam2) is required for establishing modularity in the visual system. Dscam2 null flies have a disorganized visual system, lack boundaries between neighbouring modules in the optic lobe and exhibit changes in the postsynaptic composition of photoreceptor synapses. The behavioural consequences of these wiring defects have not been explored previously. In this thesis, I begin to dissect how these changes in neural circuitry affect visual system behaviours such as the optomotor response, object-orientation preference, and attention-like object-tracking. In order to do so, a population and single tethered-fly assay have been optimized and used to explore motion perception by assessing the visual response to a wide range of psychophysical parameters of black/green moving gratings. In addition, a single tethered-fly virtual reality assay has been set-up and used to explore orientation preference and attention-like tracking.Through the population and single tethered-fly optomotor assays, it is shown that Dscam2 null flies can track motion but that their response is opposite to control flies under defined experimental conditions.Through the single tethered-fly virtual reality assay, it is shown that Dscam2 null flies anti-fixate on a dark bar, again the opposite behaviour to control flies. Responses to a light bar are in contrast, the same as controls. It is shown that this anti-fixation is bar width dependent. Together these results demonstrate that the disrupted visual system of the Dscam2 null flies can dramatically change the perception of specific visual cues and modify behaviour. Lastly, other perturbations of Dscam2 were studied such as Dscam2 single isoform and Dscam2 trisomic flies. Dscam2B single isoform flies displayed some motion detection phenotypes. In addition, they were not responding to either a dark or light bar in the tethered virtual reality assay. Dscam2 trisomic flies were able to detect gratings with different spatial and temporal frequencies but had a change in orientation behaviour as they displayed fixation behaviour to both a dark and a light bar. The results provide a foundation for understanding how brain miswiring can lead to changes in behaviour.iii In chapter 1, I outline the background information to provide context for the experiments and overall research aim. In chapter 2, I contrast a population and single fly assay to explore motion detection in Drosophila.In chapter 3, I present a new single fly virtual reality assay which goes beyond simple visual reflexes by measuring object-orientation preferences and attention-like tracking. In chapter 4, I test motion detection in Dscam2 null flies. In chapter 5, I test object-orientation prefe...

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Aggregation‐Induced Nonlinear Optical Effects of AIEgen Nanocrystals for Ultradeep In Vivo Bioimaging

et al. 2019

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Nonlinear optical microscopy has become a powerful tool in bioimaging research due to its unique capabilities of deep optical sectioning, high‐spatial‐resolution imaging, and 3D reconstruction of biological specimens. Developing organic fluorescent probes with strong nonlinear optical effects, in particular third‐harmonic generation (THG), is promising for exploiting nonlinear microscopic imaging for biomedical applications. Herein, a simple method for preparing organic nanocrystals based on an aggregation‐induced emission (AIE) luminogen (DCCN) with bright near‐infrared emission is successfully demonstrated. Aggregation‐induced nonlinear optical effects, including two‐photon fluorescence (2PF), three‐photon fluorescence (3PF), and THG, of DCCN are observed in nanoparticles, especially for crystalline nanoparticles. The nanocrystals of DCCN are successfully applied for 2PF microscopy at 1040 nm NIR‐II excitation and THG microscopy at 1560 nm NIR‐II excitation, respectively, to reconstruct the 3D vasculature of the mouse cerebral vasculature. Impressively, the THG microscopy provides much higher spatial resolution and brightness than the 2PF microscopy and can visualize small vessels with diameters of ≈2.7 µm at the deepest depth of 800 µm in a mouse brain. Thus, this is expected to inspire new insights into the development of advanced AIE materials with multiple nonlinearity, in particular THG, for multimodal nonlinear optical microscopy.

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Leg-tracking and automated behavioural classification in Drosophila

Cited by 97 publications

References 23 publications

Systematic exploration of unsupervised methods for mapping behavior

Systematic exploration of unsupervised methods for mapping behavior

Analysing visual behaviour in Drosophila Dscam2 mutants using optimised optomotor and operant control assays

Aggregation‐Induced Nonlinear Optical Effects of AIEgen Nanocrystals for Ultradeep In Vivo Bioimaging

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