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3 Janelia Publications

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    09/03/20 | A connectome of the adult drosophila central brain.
    Xu CS, Januszewski M, Lu Z, Takemura S, Hayworth KJ, Huang G, Shinomiya K, Maitin-Shepard J, Ackerman D, Berg S, Blakely T, Bogovic J, Clements J, Dolafi T, Hubbard P, Kainmueller D, Katz W, Kawase T, Khairy KA, Leavitt L, Li PH, Lindsey L, Neubarth N, Olbris DJ, Otsuna H, Troutman ET, Umayam L, Zhao T, Ito M, Goldammer J, Wolff T, Svirskas R, Schlegel P, Neace ER, Knecht CJ, Alvarado CX, Bailey DA, Ballinger S, Borycz JA, Canino BS
    eLife. 2020 Sep 03:. doi:

    The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions.

    Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain.

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    01/16/20 | Cortical pattern generation during dexterous movement is input-driven.
    Sauerbrei BA, Guo J, Cohen JD, Mischiati M, Guo W, Kabra M, Verma N, Mensh B, Branson K, Hantman AW
    Nature. 2020 Jan 16;577(7790):386-91. doi: 10.1038/s41586-019-1869-9

    The motor cortex controls skilled arm movement by sending temporal patterns of activity to lower motor centres. Local cortical dynamics are thought to shape these patterns throughout movement execution. External inputs have been implicated in setting the initial state of the motor cortex, but they may also have a pattern-generating role. Here we dissect the contribution of local dynamics and inputs to cortical pattern generation during a prehension task in mice. Perturbing cortex to an aberrant state prevented movement initiation, but after the perturbation was released, cortex either bypassed the normal initial state and immediately generated the pattern that controls reaching or failed to generate this pattern. The difference in these two outcomes was probably a result of external inputs. We directly investigated the role of inputs by inactivating the thalamus; this perturbed cortical activity and disrupted limb kinematics at any stage of the movement. Activation of thalamocortical axon terminals at different frequencies disrupted cortical activity and arm movement in a graded manner. Simultaneous recordings revealed that both thalamic activity and the current state of cortex predicted changes in cortical activity. Thus, the pattern generator for dexterous arm movement is distributed across multiple, strongly interacting brain regions.

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    09/30/19 | ilastik: interactive machine learning for (bio)image analysis.
    Berg S, Kutra D, Kroeger T, Straehle CN, Kausler BX, Haubold C, Schiegg M, Ales J, Beier T, Rudy M, Eren K, Cervantes JI, Xu B, Beuttenmueller F, Wolny A, Zhang C, Koethe U, Hamprecht FA, Kreshuk A
    Nature Methods. 2019 Sep 30;16:1226-32. doi: 10.1038/s41592-019-0582-9

    We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.

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