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The need for automated and efficient systems for tracking full animal pose has increased with the complexity of behavioral data and analyses. Here we introduce LEAP (LEAP estimates animal pose), a deep-learning-based method for predicting the positions of animal body parts. This framework consists of a graphical interface for labeling of body parts and training the network. LEAP offers fast prediction on new data, and training with as few as 100 frames results in 95% of peak performance. We validated LEAP using videos of freely behaving fruit flies and tracked 32 distinct points to describe the pose of the head, body, wings and legs, with an error rate of <3% of body length. We recapitulated reported findings on insect gait dynamics and demonstrated LEAP’s applicability for unsupervised behavioral classification. Finally, we extended the method to more challenging imaging situations and videos of freely moving mice.

Calcium imaging with protein-based indicators 1 , 2 is widely used to follow neural activity in intact nervous systems, but current protein sensors report neural activity at timescales much slower than electrical signalling and are limited by trade-offs between sensitivity and kinetics. Here we used large-scale screening and structure-guided mutagenesis to develop and optimize several fast and sensitive GCaMP-type indicators 3 – 8 . The resulting ‘jGCaMP8’ sensors, based on the calcium-binding protein calmodulin and a fragment of endothelial nitric oxide synthase, have ultra-fast kinetics (half-rise times of 2 ms) and the highest sensitivity for neural activity reported for a protein-based calcium sensor. jGCaMP8 sensors will allow tracking of large populations of neurons on timescales relevant to neural computation. Using large-scale screening and structure-guided mutagenesis, fast and sensitive GCaMP sensors are developed and optimized with improved kinetics without compromising sensitivity or brightness.

Cellular diversification is critical for specialized functions of the brain including learning and memory 1 . Single-cell RNA sequencing facilitates transcriptomic profiling of distinct major types of neuron 2 – 4 , but the divergence of transcriptomic profiles within a neuronal population and their link to function remain poorly understood. Here we isolate nuclei tagged 5 in specific cell types followed by single-nucleus RNA sequencing to profile Purkinje neurons and map their responses to motor activity and learning. We find that two major subpopulations of Purkinje neurons, identified by expression of the genes Aldoc and Plcb4 , bear distinct transcriptomic features. Plcb4 + , but not Aldoc + , Purkinje neurons exhibit robust plasticity of gene expression in mice subjected to sensorimotor and learning experience. In vivo calcium imaging and optogenetic perturbation reveal that Plcb4 + Purkinje neurons have a crucial role in associative learning. Integrating single-nucleus RNA sequencing datasets with weighted gene co-expression network analysis uncovers a learning gene module that includes components of FGFR2 signalling in Plcb4 + Purkinje neurons. Knockout of Fgfr2 in Plcb4 + Purkinje neurons in mice using CRISPR disrupts motor learning. Our findings define how diversification of Purkinje neurons is linked to their responses in motor learning and provide a foundation for understanding their differential vulnerability to neurological disorders. Subpopulations of Purkinje neurons display distinct transcriptomic responses and functions in associative learning.

Since the U.S. Supreme Court's Davis v. Bandemer ruling in 1986, partisan gerrymandering for statewide electoral advantage has been held to be justiciable. The existing Supreme Court standard, culminating in Vieth v. Jubelirer and LULAC v. holds that a test for gerrymandering should demonstrate both intents and effects and that partisan gerrymandering may be recognizable by its asymmetry: for a given distribution of popular votes, if the parties switch places in popular vote, the numbers of seats will change in an unequal fashion. However, the asymmetry standard is only a broad statement of principle, and no analytical method for assessing asymmetry has yet been held by the Supreme Court to be manageable. This Article proposes three statistical tests to reliably assess asymmetry in state-level districting schemes: (1) an unrepresentative distortion in the number of seats won based on expectations from nationwide district characteristics; (2) a discrepancy in winning vote margins between the two parties; and (3) the construction of reliable wins for the party in charge of redistricting, as measured by either the difference between mean and median vote share, or an unusually even distribution of votes across districts. The first test relies on computer simulation to estimate appropriate levels of representation for a given level of popular vote and provides a way to measure the effects of a gerrymander. The second and third tests, which can be used to help evaluate redistricting intent, rely on well-established statistical principles and can be carried out using a hand calculator without examination of maps or redistricting procedures. I apply these standards to a variety of districting schemes, starting from the original \"Gerry-mander\" of 1812, up to modern cases. In post-2010 congressional elections, partisan gerrymandering in a handful of states generated effects that are larger than the total nationwide effect of population clustering. By applying these standards in two recent cases, I show that Arizona legislative districts (Harris v. Arizona Independent Redistricting Commission) fail to qualify as a partisan gerrymander, but Maryland's congressional districts (Shapiro v. McManus) do. I propose that an intents-and-effects standard based on these tests is robust enough to mitigate the need to demonstrate predominant partisan intent. The three statistical standards offered here add to the judge s toolkit for rapidly and rigorously identifying the partisan consequences of redistricting.

A common feature of autism spectrum disorder (ASD) is the impairment of motor control and learning, occurring in a majority of children with autism, consistent with perturbation in cerebellar function. Here we report alterations in motor behaviour and cerebellar synaptic plasticity in a mouse model (patDp/+) for the human 15q11-13 duplication, one of the most frequently observed genetic aberrations in autism. These mice show ASD-resembling social behaviour deficits. We find that in patDp/+ mice delay eyeblink conditioning—a form of cerebellum-dependent motor learning—is impaired, and observe deregulation of a putative cellular mechanism for motor learning, long-term depression (LTD) at parallel fibre-Purkinje cell synapses. Moreover, developmental elimination of surplus climbing fibres—a model for activity-dependent synaptic pruning—is impaired. These findings point to deficits in synaptic plasticity and pruning as potential causes for motor problems and abnormal circuit development in autism. Impairments of cerebellar-dependent motor control and learning are implicated in some forms of autism spectrum disorder (ASD). In this study, the authors provide a characterization of the motor deficits and cerebellar function abnormalities in a transgenic mouse model of ASD.

Background Repetitive action, resistance to environmental change and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. We then investigated the behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice. Both of these mutations are found in clinical conditions and have been associated with ASD. Methods We used advances in computer vision and deep learning, namely a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple timescales using a single popular behavioral assay, the open-field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait. Results Both Cntnap2 knockouts and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants and Cntnap2 knockouts showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure to adapt took the form of maintained ambling, turning and locomotion, and an overall decrease in grooming. However, adaptation in these traits was similar between wild-type mice and Cntnap2 knockouts. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. Limitations Genetic risk factors for autism are numerous, and we tested only two. Our pipeline was only done under conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability. Conclusions Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as quantitative diagnosis criteria.

Excitatory drive enters the cerebellum via mossy fibers, which activate granule cells, and climbing fibers, which activate Purkinje cell dendrites. Until now, the coordinated regulation of these pathways has gone unmonitored in spatially resolved neuronal ensembles, especially in awake animals. We imaged cerebellar activity using functional two-photon microscopy and extracellular recording in awake mice locomoting on an air-cushioned spherical treadmill. We recorded from putative granule cells, molecular layer interneurons, and Purkinje cell dendrites in zone A of lobule IV/V, representing sensation and movement from trunk and limbs. Locomotion was associated with widespread increased activity in granule cells and interneurons, consistent with an increase in mossy fiber drive. At the same time, dendrites of different Purkinje cells showed increased co-activation, reflecting increased synchrony of climbing fiber activity. In resting animals, aversive stimuli triggered increased activity in granule cells and interneurons, as well as increased Purkinje cell co-activation that was strongest for neighboring dendrites and decreased smoothly as a function of mediolateral distance. In contrast with anesthetized recordings, no 1-10 Hz oscillations in climbing fiber activity were evident. Once locomotion began, responses to external stimuli in all three cell types were strongly suppressed. Thus climbing and mossy fiber representations can shift together within a fraction of a second, reflecting in turn either movement-associated activity or external stimuli.

The use of genetically encodable calcium indicator proteins to monitor neuronal activity is hampered by slow response times and a narrow Ca 2+ -sensitive range. Here we identify three performance-limiting features of GCaMP3, a popular genetically encodable calcium indicator protein. First, we find that affinity is regulated by the calmodulin domain’s Ca 2+ -chelating residues. Second, we find that off-responses to Ca 2+ are rate-limited by dissociation of the RS20 domain from calmodulin’s hydrophobic pocket. Third, we find that on-responses are limited by fast binding to the N-lobe at high Ca 2+ and by slow binding to the C-lobe at lower Ca 2+ . We develop Fast-GCaMPs, which have up to 20-fold accelerated off-responses and show that they have a 200-fold range of K D , allowing coexpression of multiple variants to span an expanded range of Ca 2+ concentrations. Finally, we show that Fast-GCaMPs track natural song in Drosophila auditory neurons and generate rapid responses in mammalian neurons, supporting the utility of our approach. Genetically encoded calcium indicators are commonly used to study cellular activity, but their usefulness is limited by their response kinetics. Here the authors generate indicators with faster responses to calcium events in both Drosophila melanogaster and mammalian neurons.

Multicellular glial calcium waves may locally regulate neural activity or brain energetics. Here, we report a diffusion-driven astrocytic signal in the normal, intact brain that spans many astrocytic processes in a confined volume without fully encompassing any one cell. By using 2-photon microscopy in rodent cerebellar cortex labeled with fluorescent indicator dyes or the calcium-sensor protein G-CaMP2, we discovered spontaneous calcium waves that filled approximately ellipsoidal domains of Bergmann glia processes. Waves spread in 3 dimensions at a speed of 4-11 μm/s to a diameter of [almost equal to]50 μm, slowed during expansion, and were reversibly blocked by P2 receptor antagonists. Consistent with the hypothesis that ATP acts as a diffusible trigger of calcium release waves, local ejection of ATP triggered P2 receptor-mediated waves that were refractory to repeated activation. Transglial waves represent a means for purinergic signals to act with local specificity to modulate activity or energetics in local neural circuits.

The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimation in individual animals, extending these to multiple animals presents unique challenges for studies of social behaviors or animals in their natural environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking. This system enables versatile workflows for data labeling, model training and inference on previously unseen data. SLEAP features an accessible graphical user interface, a standardized data model, a reproducible configuration system, over 30 model architectures, two approaches to part grouping and two approaches to identity tracking. We applied SLEAP to seven datasets across flies, bees, mice and gerbils to systematically evaluate each approach and architecture, and we compare it with other existing approaches. SLEAP achieves greater accuracy and speeds of more than 800 frames per second, with latencies of less than 3.5 ms at full 1,024 × 1,024 image resolution. This makes SLEAP usable for real-time applications, which we demonstrate by controlling the behavior of one animal on the basis of the tracking and detection of social interactions with another animal. SLEAP is a versatile deep learning-based multi-animal pose-tracking tool designed to work on videos of diverse animals, including during social behavior.