Multimodal evidence for cerebellar influence on cortical development in autism: structural growth amidst functional disruption.

Despite perinatal damage to the cerebellum being one of the highest risk factors for later being diagnosed with autism spectrum disorder (ASD), it is not yet clear how the cerebellum might influence the development of cerebral cortex and whether this co-developmental process is distinct between neurotypical and ASD children. Leveraging a large structural brain MRI dataset of neurotypical children and those diagnosed with ASD, we examined whether structural variation in cerebellar tissue across individuals was correlated with neocortical variation during development, including the thalamus as a coupling factor. We found that the thalamus plays a distinct role in moderating cerebro-cerebellar structural coordination in ASD. Notably, structural coupling between cerebellum, thalamus, and neocortex was strongest in younger childhood and waned by early adolescence, mirroring a previously undescribed trajectory of behavioral development between ASD and neurotypical children. Complementary functional connectivity analyses likewise revealed atypical connectivity between cerebellum and neocortex in ASD. This relationship was particularly prominent in a model of cerebellar structure predicting functional connectivity, where ASD and neurotypical children showed divergent patterns. Interestingly, these functional-structural relationships became more prominent with age, while structural effects were most prominent earlier in childhood, and showed significant lateralization. This pattern may suggest a developmental sequence where early uncoordinated structural growth amongst regions is followed by increasingly atypical functional synchronization. These findings provide multimodal evidence in the living brain for a cerebellar diaschisis model of autism, where both increased cerebellar-cerebral structural coupling and altered functional connectivity in cerebro-cerebellar pathways contribute to the ontogeny of this neurodevelopmental disorder.

A neural mechanism for learning from delayed postingestive feedback.

Animals learn the value of foods based on their postingestive effects and thereby develop aversions to foods that are toxic1-6 and preferences to those that are nutritious7-14. However, it remains unclear how the brain is able to assign credit to flavors experienced during a meal with postingestive feedback signals that can arise after a substantial delay. Here, we reveal an unexpected role for postingestive reactivation of neural flavor representations in this temporal credit assignment process. To begin, we leverage the fact that mice learn to associate novel15-18, but not familiar, flavors with delayed gastric malaise signals to investigate how the brain represents flavors that support aversive postingestive learning. Surveying cellular resolution brainwide activation patterns reveals that a network of amygdala regions is unique in being preferentially activated by novel flavors across every stage of the learning process: the initial meal, delayed malaise, and memory retrieval. By combining high-density recordings in the amygdala with optogenetic stimulation of genetically defined hindbrain malaise cells, we find that postingestive malaise signals potently and specifically reactivate amygdalar novel flavor representations from a recent meal. The degree of malaise-driven reactivation of individual neurons predicts strengthening of flavor responses upon memory retrieval, leading to stabilization of the population-level representation of the recently consumed flavor. In contrast, meals without postingestive consequences degrade neural flavor representations as flavors become familiar and safe. Thus, our findings demonstrate that interoceptive reactivation of amygdalar flavor representations provides a neural mechanism to resolve the temporal credit assignment problem inherent to postingestive learning.

Princeton RAtlas: A Common Coordinate Framework for Fully cleared, Whole Rattus norvegicus Brains.

Whole-brain clearing and imaging methods are becoming more common in mice but have yet to become standard in rats, at least partially due to inadequate clearing from most available protocols. Here, we build on recent mouse-tissue clearing and light-sheet imaging methods and develop and adapt them to rats. We first used cleared rat brains to create an open-source, 3D rat atlas at 25 µm resolution. We then registered and imported other existing labeled volumes and made all of the code and data available for the community (https://github.com/emilyjanedennis/PRA) to further enable modern, whole-brain neuroscience in the rat. Key features • This protocol adapts iDISCO (Renier et al., 2014) and uDISCO (Pan et al., 2016) tissue-clearing techniques to consistently clear rat brains. • This protocol also decreases the number of working hours per day to fit in an 8 h workday. Graphical overview.

Cerebellar contributions to a brainwide network for flexible behavior in mice.

The cerebellum regulates nonmotor behavior, but the routes of influence are not well characterized. Here we report a necessary role for the posterior cerebellum in guiding a reversal learning task through a network of diencephalic and neocortical structures, and in flexibility of free behavior. After chemogenetic inhibition of lobule VI vermis or hemispheric crus I Purkinje cells, mice could learn a water Y-maze but were impaired in ability to reverse their initial choice. To map targets of perturbation, we imaged c-Fos activation in cleared whole brains using light-sheet microscopy. Reversal learning activated diencephalic and associative neocortical regions. Distinctive subsets of structures were altered by perturbation of lobule VI (including thalamus and habenula) and crus I (including hypothalamus and prelimbic/orbital cortex), and both perturbations influenced anterior cingulate and infralimbic cortex. To identify functional networks, we used correlated variation in c-Fos activation within each group. Lobule VI inactivation weakened within-thalamus correlations, while crus I inactivation divided neocortical activity into sensorimotor and associative subnetworks. In both groups, high-throughput automated analysis of whole-body movement revealed deficiencies in across-day behavioral habituation to an open-field environment. Taken together, these experiments reveal brainwide systems for cerebellar influence that affect multiple flexible responses.

Fast and sensitive GCaMP calcium indicators for imaging neural populations.

Calcium imaging with protein-based indicators1,2 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 indicators3-8. 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.

Automated high-throughput mouse transsynaptic viral tracing using iDISCO+ tissue clearing, light-sheet microscopy, and BrainPipe.

Transsynaptic viral tracing requires tissue sectioning, manual cell counting, and anatomical assignment, all of which are time intensive. We describe a protocol for BrainPipe, a scalable software for automated anatomical alignment and object counting in light-sheet microscopy volumes. BrainPipe can be generalized to new counting tasks by using a new atlas and training a neural network for object detection. Combining viral tracing, iDISCO+ tissue clearing, and BrainPipe facilitates mapping of cerebellar connectivity to the rest of the murine brain. For complete details on the use and execution of this protocol, please refer to Pisano et al. (2021).

Transcriptomic mapping uncovers Purkinje neuron plasticity driving learning.

Cellular diversification is critical for specialized functions of the brain including learning and memory1. Single-cell RNA sequencing facilitates transcriptomic profiling of distinct major types of neuron2-4, but the divergence of transcriptomic profiles within a neuronal population and their link to function remain poorly understood. Here we isolate nuclei tagged5 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.

SLEAP: A deep learning system for multi-animal pose tracking.

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.

Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models.

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.

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning.

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement by a detector. We describe the components and setup and provide a detailed protocol for training and data analysis. This platform allows the incorporation of optogenetic stimulation and fluorescence imaging. The design allows a single host computer to control multiple platforms for training multiple animals simultaneously.

A systems framework for remedying dysfunction in US democracy.

Democracy often fails to meet its ideals, and these failures may be made worse by electoral institutions. Unwanted outcomes include elite polarization, unresponsive representatives, and the ability of a faction of voters to gain power at the expense of the majority. Various reforms have been proposed to address these problems, but their effectiveness is difficult to predict against a backdrop of complex interactions. Here we outline a path for systems-level modeling to help understand and optimize repairs to US democracy. Following the tradition of engineering and biology, models of systems include mechanisms with dynamical properties that include nonlinearities and amplification (voting rules), positive feedback mechanisms (single-party control, gerrymandering), negative feedback (checks and balances), integration over time (lifetime judicial appointments), and low dimensionality (polarization). To illustrate a systems-level approach, we analyze three emergent phenomena: low dimensionality, elite polarization, and antimajoritarianism in legislatures. In each case, long-standing rules now contribute to undesirable outcomes as a consequence of changes in the political environment. Theoretical understanding at a general level will also help evaluate whether a proposed reform's benefits will materialize and be lasting, especially as conditions change again. In this way, rigorous modeling may not only shape new lines of research but aid in the design of effective and lasting reform.

Homologous organization of cerebellar pathways to sensory, motor, and associative forebrain.

Cerebellar outputs take polysynaptic routes to reach the rest of the brain, impeding conventional tracing. Here, we quantify pathways between the cerebellum and forebrain by using transsynaptic tracing viruses and a whole-brain analysis pipeline. With retrograde tracing, we find that most descending paths originate from the somatomotor cortex. Anterograde tracing of ascending paths encompasses most thalamic nuclei, especially ventral posteromedial, lateral posterior, mediodorsal, and reticular nuclei. In the neocortex, sensorimotor regions contain the most labeled neurons, but we find higher densities in associative areas, including orbital, anterior cingulate, prelimbic, and infralimbic cortex. Patterns of ascending expression correlate with c-Fos expression after optogenetic inhibition of Purkinje cells. Our results reveal homologous networks linking single areas of the cerebellar cortex to diverse forebrain targets. We conclude that shared areas of the cerebellum are positioned to provide sensory-motor information to regions implicated in both movement and nonmotor function.

The p75NTR Influences Cerebellar Circuit Development and Adult Behavior via Regulation of Cell Cycle Duration of Granule Cell Progenitors.

Development of brain circuitry requires precise regulation and timing of proliferation and differentiation of neural progenitor cells. The p75 neurotrophin receptor (p75NTR) is highly expressed in the proliferating granule cell precursors (GCPs) during development of the cerebellum. In a previous paper, we showed that proNT3 promoted GCP cell cycle exit via p75NTR. Here we used genetically modified rats and mice of both sexes to show that p75NTR regulates the duration of the GCP cell cycle, requiring activation of RhoA. Rats and mice lacking p75NTR have dysregulated GCP proliferation, with deleterious effects on cerebellar circuit development and behavioral consequences persisting into adulthood. In the absence of p75NTR, the GCP cell cycle is accelerated, leading to delayed cell cycle exit, prolonged GCP proliferation, increased glutamatergic input to Purkinje cells, and a deficit in delay eyeblink conditioning, a cerebellum-dependent form of learning. These results demonstrate the necessity of appropriate developmental timing of the cell cycle for establishment of proper connectivity and associated behavior.SIGNIFICANCE STATEMENT The cerebellum has been shown to be involved in numerous behaviors in addition to its classic association with motor function. Cerebellar function is disrupted in a variety of psychiatric disorders, including those on the autism spectrum. Here we show that the p75 neurotrophin receptor, which is abundantly expressed in the proliferating cerebellar granule cell progenitors, regulates the cell cycle of these progenitors. In the absence of this receptor, the cell cycle is dysregulated, leading to excessive progenitor proliferation, which alters the balance of inputs to Purkinje cells, disrupting the circuitry and leading to functional deficits that persist into adulthood.

Complex spike clusters and false-positive rejection in a cerebellar supervised learning rule.

KEY POINTS: Spike doublets comprise ∼10% of in vivo complex spike events under spontaneous conditions and ∼20% (up to 50%) under evoked conditions. Under near-physiological slice conditions, single complex spikes do not induce parallel fibre long-term depression. Doublet stimulation is required to induce long-term depression with an optimal parallel-fibre to first-complex-spike timing interval of 150 ms. ABSTRACT: The classic example of biological supervised learning occurs at cerebellar parallel fibre (PF) to Purkinje cell synapses, comprising the most abundant synapse in the mammalian brain. Long-term depression (LTD) at these synapses is driven by climbing fibres (CFs), which fire continuously about once per second and therefore generate potential false-positive events. We show that pairs of complex spikes are required to induce LTD. In vivo, sensory stimuli evoked complex-spike doublets with intervals ≤150 ms in up to 50% of events. Using realistic [Ca2+ ]o and [Mg2+ ]o concentrations in slices, we determined that complex-spike doublets delivered 100-150 ms after PF stimulus onset were required to trigger PF-LTD, which is consistent with the requirements for eyeblink conditioning. Inter-complex spike intervals of 50-150 ms provided optimal decoding. This stimulus pattern prolonged evoked spine calcium signals and promoted CaMKII activation. Doublet activity may provide a means for CF instructive signals to stand out from background firing.

Cerebellar disruption impairs working memory during evidence accumulation.

To select actions based on sensory evidence, animals must create and manipulate representations of stimulus information in memory. Here we report that during accumulation of somatosensory evidence, optogenetic manipulation of cerebellar Purkinje cells reduces the accuracy of subsequent memory-guided decisions and causes mice to downweight prior information. Behavioral deficits are consistent with the addition of noise and leak to the evidence accumulation process. We conclude that the cerebellum can influence the accurate maintenance of working memory.

Author Correction: Stability, affinity, and chromatic variants of the glutamate sensor iGluSnFR.

The version of this paper originally published cited a preprint version of ref. 12 instead of the published version (Proc. Natl. Acad. Sci. USA 115, 5594-5599; 2018), which was available before this Nature Methods paper went to press. The reference information has been updated in the PDF and HTML versions of the article.

Publisher Correction: Stability, affinity, and chromatic variants of the glutamate sensor iGluSnFR.

In the version of this paper originally published, important figure labels in Fig. 3d were not visible. An image layer present in the authors' original figure that included two small dashed outlines and text labels indicating ROI 1 and ROI 2, as well as a scale bar and the name of the cell label, was erroneously altered during image processing. The figure has been corrected in the HTML and PDF versions of the paper.

Fast animal pose estimation using deep neural networks.

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.

Stability, affinity, and chromatic variants of the glutamate sensor iGluSnFR.

Single-wavelength fluorescent reporters allow visualization of specific neurotransmitters with high spatial and temporal resolution. We report variants of intensity-based glutamate-sensing fluorescent reporter (iGluSnFR) that are functionally brighter; detect submicromolar to millimolar amounts of glutamate; and have blue, cyan, green, or yellow emission profiles. These variants could be imaged in vivo in cases where original iGluSnFR was too dim, resolved glutamate transients in dendritic spines and axonal boutons, and allowed imaging at kilohertz rates.