@article {69658, title = {The Fly Disco: Hardware and software for optogenetics and fine-grained fly behavior analysis}, journal = {bioRxiv}, year = {2024}, month = {2024 Nov 04}, abstract = {

In the fruit fly, Drosophila melanogaster, connectome data and genetic tools provide a unique opportunity to study complex behaviors including navigation, mating, aggression, and grooming in an organism with a tractable nervous system of 140,000 neurons. Here we present the Fly Disco, a flexible system for high quality video collection, optogenetic manipulation, and fine-grained behavioral analysis of freely walking and socializing fruit fly groups. The data collection hardware and software automates the collection of videos synced to programmable optogenetic stimuli. Key pipeline features include behavioral analysis based on trajectories of 21 keypoints and optogenetic-specific summary statistics and data visualization. We created the multifly dataset for pose estimation that includes 9701 examples enriched in complex behaviors. All hardware designs, software, and the multifly dataset are freely available.

}, doi = {10.1101/2024.11.04.621948}, url = {https://doi.org/10.1101/2024.11.04.621948}, author = {Robie, Alice A and Taylor, Adam L and Schretter, Catherine E and Kabra, Mayank and Branson, Kristin} } @article {69301, title = {Motor neurons generate pose-targeted movements via proprioceptive sculpting.}, journal = {Nature}, year = {2024}, month = {2024 Mar 20}, abstract = {

Motor neurons are the final common pathway through which the brain controls movement of the body, forming the basic elements from which all movement is composed. Yet how a single motor neuron contributes to control during natural movement remains unclear. Here we anatomically and functionally characterize the individual roles of the motor neurons that control head movement in the fly, Drosophila melanogaster. Counterintuitively, we find that activity in a single motor neuron rotates the head in different directions, depending on the starting posture of the head, such that the head converges towards a pose determined by the identity of the stimulated motor neuron. A feedback model predicts that this convergent behaviour results from motor neuron drive interacting with proprioceptive feedback. We identify and genetically suppress a single class of proprioceptive neuron that changes the motor neuron-induced convergence as predicted by the feedback model. These data suggest a framework for how the brain controls movements: instead of directly generating movement in a given direction by activating a fixed set of motor neurons, the brain controls movements by adding bias to a continuing proprioceptive-motor loop.

}, issn = {1476-4687}, doi = {10.1038/s41586-024-07222-5}, author = {Gorko, Benjamin and Siwanowicz, Igor and Close, Kari and Christoforou, Christina and Hibbard, Karen L and Kabra, Mayank and Lee, Allen and Park, Jin-Yong and Li, Si Ying and Chen, Alex B and Namiki, Shigehiro and Chen, Chenghao and Tuthill, John C and Bock, Davi D and Rouault, Herv{\'e} and Branson, Kristin and Ihrke, Gudrun and Huston, Stephen J} } @article {65259, title = {Cortical pattern generation during dexterous movement is input-driven.}, journal = {Nature}, volume = {577}, year = {2020}, month = {2020 Jan 16}, pages = {386-91}, abstract = {

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.

}, issn = {1476-4687}, doi = {10.1038/s41586-019-1869-9}, author = {Sauerbrei, Britton A and Guo, Jian-Zhong and Cohen, Jeremy D and Mischiati, Matteo and Guo, Wendy and Kabra, Mayank and Verma, Nakul and Mensh, Brett and Branson, Kristin and Hantman, Adam W} } @article {49585, title = {Motor cortex is an input-driven dynamical system controlling dexterous movement.}, journal = {bioRxiv}, year = {2018}, month = {2018-12-14}, pages = {266320}, abstract = {

Skillful control of movement is central to our ability to sense and manipulate the world. A large body of work in nonhuman primates has demonstrated that motor cortex provides flexible, time-varying activity patterns that control the arm during reaching and grasping. Previous studies have suggested that these patterns are generated by strong local recurrent dynamics operating autonomously from inputs during movement execution. An alternative possibility is that motor cortex requires coordination with upstream brain regions throughout the entire movement in order to yield these patterns. Here, we developed an experimental preparation in the mouse to directly test these possibilities using optogenetics and electrophysiology during a skilled reach-to-grab-to-eat task. To validate this preparation, we first established that a specific, time-varying pattern of motor cortical activity was required to produce coordinated movement. Next, in order to disentangle the contribution of local recurrent motor cortical dynamics from external input, we optogenetically held the recurrent contribution constant, then observed how motor cortical activity recovered following the end of this perturbation. Both the neural responses and hand trajectory varied from trial to trial, and this variability reflected variability in external inputs. To directly probe the role of these inputs, we used optogenetics to perturb activity in the thalamus. Thalamic perturbation at the start of the trial prevented movement initiation, and perturbation at any stage of the movement prevented progression of the hand to the target; this demonstrates that input is required throughout the movement. By comparing motor cortical activity with and without thalamic perturbation, we were able to estimate the effects of external inputs on motor cortical population activity. Thus, unlike pattern-generating circuits that are local and autonomous, such as those in the spinal cord that generate left-right alternation during locomotion, the pattern generator for reaching and grasping is distributed across multiple, strongly-interacting brain regions.

}, doi = {10.1101/266320}, author = {Sauerbrei, Britton and Guo, Jian-Zhong and Mischiati, Matteo and Guo, Wendy and Kabra, Mayank and Verma, Nakul and Branson, Kristin M and Hantman, Adam W} } @article {48919, title = {Network-size independent covering number bounds for deep networks.}, journal = {arXiv}, year = {2017}, month = {2017 Nov 02}, pages = {arXiv:1711.00753 }, abstract = {

We give a covering number bound for deep learning networks that is independent of the size of the network. The key for the simple analysis is that for linear classifiers, rotating the data doesn{\textquoteright}t affect the covering number. Thus, we can ignore the rotation part of each layer{\textquoteright}s linear transformation, and get the covering number bound by concentrating on the scaling part.

}, url = {https://arxiv.org/abs/1711.00753}, author = {Kabra, Mayank and Branson, Kristin M} } @article {45458, title = {Behavioral variability through stochastic choice and its gating by anterior cingulate cortex.}, journal = {Cell}, volume = {159}, year = {2014}, month = {2014 Sep 25}, pages = {21-32}, abstract = {

Behavioral choices that ignore prior experience promote exploration and unpredictability but are seemingly at odds with the brain{\textquoteright}s tendency to use experience to optimize behavioral choice. Indeed, when faced with virtual competitors, primates resort to strategic counterprediction rather than to stochastic choice. Here, we show that rats also use history- and model-based strategies when faced with similar competitors but can switch to a "stochastic" mode when challenged with a competitor that they cannot defeat by counterprediction. In this mode, outcomes associated with an animal{\textquoteright}s actions are ignored, and normal engagement of anterior cingulate cortex (ACC) is suppressed. Using circuit perturbations in\ transgenic rats, we demonstrate that switching between strategic and stochastic behavioral modes is controlled by locus coeruleus input into ACC. Our\ findings suggest that, under conditions of uncertainty about environmental rules, changes in noradrenergic input alter ACC output and prevent erroneous beliefs from guiding decisions, thus enabling behavioral variation.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2014.08.037}, author = {Tervo, Dougal G R and Proskurin, Mikhail and Manakov, Maxim and Kabra, Mayank and Vollmer, Alison and Branson, Kristin and Karpova, Alla Y} } @article {5763, title = {JAABA: interactive machine learning for automatic annotation of animal behavior.}, journal = {Nature Methods}, volume = {10}, year = {2013}, month = {2012 Dec}, pages = {64-7}, abstract = {

We present a machine learning{\textendash}based system for automatically computing interpretable, quantitative measures of animal behavior. Through our interactive system, users encode their intuition about behavior by annotating a small set of video frames. These manual labels are converted into classifiers that can automatically annotate behaviors in screen-scale data sets. Our general-purpose system can create a variety of accurate individual and social behavior classifiers for different organisms, including mice and adult and larval Drosophila.

}, issn = {1548-7091}, author = {Kabra, Mayank and Robie, Alice A and Rivera-Alba, Marta and Branson, Steven and Branson, Kristin} } @article {4064, title = {Towards automated high-throughput screening of C. elegans on agar.}, journal = {Arxiv}, year = {2010}, abstract = {

High-throughput screening (HTS) using model organisms is a promising method to identify a small number of genes or drugs potentially relevant to human biology or disease. In HTS experiments, robots and computers do a significant portion of the experimental work. However, one remaining major bottleneck is the manual analysis of experimental results, which is commonly in the form of microscopy images. This manual inspection is labor intensive, slow and subjective. Here we report our progress towards applying computer vision and machine learning methods to analyze HTS experiments that use Caenorhabditis elegans (C. elegans) worms grown on agar. Our main contribution is a robust segmentation algorithm for separating the worms from the background using brightfield images. We also show that by combining the output of this segmentation algorithm with an algorithm to detect the fluorescent dye, Nile Red, we can reliably distinguish different fluorescence-based phenotypes even though the visual differences are subtle. The accuracy of our method is similar to that of expert human analysts. This new capability is a significant step towards fully automated HTS experiments using C. elegans.

}, author = {Kabra, Mayank and Conery, Annie L. and O{\textquoteright}Rourke, Eyleen J. and Xie, Xin and Ljosa. Vebjorn and Jones, Thouis R. and Ausubel, Frederick M. and Ruvkun, Gary and Carpenter, Anne E. and Freund, Yoav} }