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4 Publications

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    06/16/16 | Motor control of fly feeding.
    McKellar CE
    Journal of Neurogenetics. 2016 Jun 16;30(2):101-11. doi: 10.1080/01677063.2016.1177047

    Following considerable progress on the molecular and cellular basis of taste perception in fly sensory neurons, the time is now ripe to explore how taste information, integrated with hunger and satiety, undergo a sensorimotor transformation to lead to the motor actions of feeding behavior. I examine what is known of feeding circuitry in adult flies from more than 250 years of work in larger flies and from newer work in Drosophila. I review the anatomy of the proboscis, its muscles and their functions (where known), its motor neurons, interneurons known to receive taste inputs, interneurons that diverge from taste circuitry to provide information to other circuits, interneurons from other circuits that converge on feeding circuits, proprioceptors that influence the motor control of feeding, and sites of integration of hunger and satiety on feeding circuits. In spite of the several neuron types now known, a connected pathway from taste inputs to feeding motor outputs has yet to be found. We are on the threshold of an era where these individual components will be assembled into circuits, revealing how nervous system architecture leads to the control of behavior.

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    06/01/16 | Editorial overview: Neurobiology of sex.
    Dulac C, Dickson BJ
    Current Opinion in Neurobiology. 2016 Jun;38:A1-3. doi: 10.1016/j.conb.2016.06.001
    01/28/16 | Visualization and quantification for interactive analysis of neural connectivity in Drosophila.
    Swoboda N, Moosburner J, Bruckner S, Yu J, Dickson BJ, Bühler K
    Computer Graphics Forum. 2016 Jan 28:. doi: 10.1111/cgf.12792

    Neurobiologists investigate the brain of the common fruit fly Drosophila melanogaster to discover neural circuits and link them to complex behaviour. Formulating new hypotheses about connectivity requires potential connectivity information between individual neurons, indicated by overlaps of arborizations of two or more neurons. As the number of higher order overlaps (i.e. overlaps of three or more arborizations) increases exponentially with the number of neurons under investigation, visualization is impeded by clutter and quantification becomes a burden. Existing solutions are restricted to visual or quantitative analysis of pairwise overlaps, as they rely on precomputed overlap data. We present a novel tool that complements existing methods for potential connectivity exploration by providing for the first time the possibility to compute and visualize higher order arborization overlaps on the fly and to interactively explore this information in both its spatial anatomical context and on a quantitative level. Qualitative evaluation by neuroscientists and non-experts demonstrated the utility and usability of the tool.

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    01/07/16 | Adaptive and background-aware GAL4 expression enhancement of co-registered confocal microscopy images.
    Trapp M, Schulze F, Novikov AA, Tirian L, J Dickson B, Bühler K
    Neuroinformatics. 2016 Jan 7;14(2):221-33. doi: 10.1007/s12021-015-9289-y

    GAL4 gene expression imaging using confocal microscopy is a common and powerful technique used to study the nervous system of a model organism such as Drosophila melanogaster. Recent research projects focused on high throughput screenings of thousands of different driver lines, resulting in large image databases. The amount of data generated makes manual assessment tedious or even impossible. The first and most important step in any automatic image processing and data extraction pipeline is to enhance areas with relevant signal. However, data acquired via high throughput imaging tends to be less then ideal for this task, often showing high amounts of background signal. Furthermore, neuronal structures and in particular thin and elongated projections with a weak staining signal are easily lost. In this paper we present a method for enhancing the relevant signal by utilizing a Hessian-based filter to augment thin and weak tube-like structures in the image. To get optimal results, we present a novel adaptive background-aware enhancement filter parametrized with the local background intensity, which is estimated based on a common background model. We also integrate recent research on adaptive image enhancement into our approach, allowing us to propose an effective solution for known problems present in confocal microscopy images. We provide an evaluation based on annotated image data and compare our results against current state-of-the-art algorithms. The results show that our algorithm clearly outperforms the existing solutions.

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