A quantitative framework for whole-body coordination reveals specific deficits in freely walking ataxic mice

Refined movement analysis in the staircase test reveals differential motor deficits in mouse models of stroke

Accurate assessment of post-stroke deficits is crucial in translational research. Recent advances in machine learning offer precise quantification of rodent motor behavior post-stroke, yet detecting lesion-specific upper extremity deficits remains unclear. Employing proximal middle cerebral artery occlusion (MCAO) and cortical photothrombosis (PT) in mice, we assessed post-stroke impairments via the Staircase test. Lesion locations were identified using 7 T-MRI. Machine learning was applied to reconstruct forepaw kinematic trajectories and feature analysis was achieved with MouseReach, a new data-processing toolbox. Lesion reconstructions pinpointed ischemic centers in the striatum (MCAO) and sensorimotor cortex (PT). Pellet retrieval alterations were observed, but were unrelated to overall stroke volume. Instead, forepaw slips and relative reaching success correlated with increasing cortical lesion size in both models. Striatal lesion size after MCAO was associated with prolonged reach durations that occurred with delayed symptom onset. Further analysis on the impact of selective serotonin reuptake inhibitors in the PT model revealed no clear treatment effects but replicated strong effect sizes of slips for post-stroke deficit detection. In summary, refined movement analysis unveiled specific deficits in two widely-used mouse stroke models, emphasizing the value of deep behavioral profiling in preclinical stroke research to enhance model validity for clinical translation.

Sensory feedback and central neuronal interactions in mouse locomotion

Locomotion is a complex process involving specific interactions between the central neural controller and the mechanical components of the system. The basic rhythmic activity generated by locomotor circuits in the spinal cord defines rhythmic limb movements and their central coordination. The operation of these circuits is modulated by sensory feedback from the limbs providing information about the state of the limbs and the body. However, the specific role and contribution of central interactions and sensory feedback in the control of locomotor gait and posture remain poorly understood. We use biomechanical data on quadrupedal locomotion in mice and recent findings on the organization of neural interactions within the spinal locomotor circuitry to create and analyse a tractable mathematical model of mouse locomotion. The model includes a simplified mechanical model of the mouse body with four limbs and a central controller composed of four rhythm generators, each operating as a state machine controlling the state of one limb. Feedback signals characterize the load and extension of each limb as well as postural stability (balance). We systematically investigate and compare several model versions and compare their behaviour to existing experimental data on mouse locomotion. Our results highlight the specific roles of sensory feedback and some central propriospinal interactions between circuits controlling fore and hind limbs for speed-dependent gait expression. Our models suggest that postural imbalance feedback may be critically involved in the control of swing-to-stance transitions in each limb and the stabilization of walking direction.

Characterizing the structure of mouse behavior using Motion Sequencing

Spontaneous mouse behavior is composed from repeatedly used modules of movement (e.g., rearing, running or grooming) that are flexibly placed into sequences whose content evolves over time. By identifying behavioral modules and the order in which they are expressed, researchers can gain insight into the effect of drugs, genes, context, sensory stimuli and neural activity on natural behavior. Here we present a protocol for performing Motion Sequencing (MoSeq), an ethologically inspired method that uses three-dimensional machine vision and unsupervised machine learning to decompose spontaneous mouse behavior into a series of elemental modules called 'syllables'. This protocol is based upon a MoSeq pipeline that includes modules for depth video acquisition, data preprocessing and modeling, as well as a standardized set of visualization tools. Users are provided with instructions and code for building a MoSeq imaging rig and acquiring three-dimensional video of spontaneous mouse behavior for submission to the modeling framework; the outputs of this protocol include syllable labels for each frame of the video data as well as summary plots describing how often each syllable was used and how syllables transitioned from one to the other. In addition, we provide instructions for analyzing and visualizing the outputs of keypoint-MoSeq, a recently developed variant of MoSeq that can identify behavioral motifs from keypoints identified from standard (rather than depth) video. This protocol and the accompanying pipeline significantly lower the bar for users without extensive computational ethology experience to adopt this unsupervised, data-driven approach to characterize mouse behavior.

Phenotypic analysis of ataxia in spinocerebellar ataxia type 6 mice using DeepLabCut

This study emphasizes the benefits of open-source software such as DeepLabCut (DLC) and R to automate, customize and enhance data analysis of motor behavior. We recorded 2 different spinocerebellar ataxia type 6 mouse models while performing the classic beamwalk test, tracked multiple body parts using the markerless pose-estimation software DLC and analyzed the tracked data using self-written scripts in the programming language R. The beamwalk analysis script (BAS) counts and classifies minor and major hindpaw slips with an 83% accuracy compared to manual scoring. Nose, belly and tail positions relative to the beam, as well as the angle at the tail base relative to the nose and tail tip were determined to characterize motor deficits in greater detail. Our results found distinct ataxic abnormalities such as an increase in major left hindpaw slips and a lower belly and tail position in both SCA6 ataxic mouse models compared to control mice at 18 months of age. Furthermore, a more detailed analysis of various body parts relative to the beam revealed an overall lower body position in the SCA684Q compared to the CT-longQ27PC mouse line at 18 months of age, indicating a more severe ataxic deficit in the SCA684Q group.

Dopamine lesions alter the striatal encoding of single-limb gait

The striatum serves an important role in motor control, and neurons in this area encode the body's initiation, cessation, and speed of locomotion. However, it remains unclear whether the same neurons also encode the step-by-step rhythmic motor patterns of individual limbs that characterize gait. By combining high-speed video tracking, electrophysiology, and optogenetic tagging, we found that a sizable population of both D1 and D2 receptor expressing medium spiny projection neurons (MSNs) were phase-locked to the gait cycle of individual limbs in mice. Healthy animals showed balanced limb phase-locking between D1 and D2 MSNs, while dopamine depletion led to stronger phase-locking in D2 MSNs. These findings indicate that striatal neurons represent gait on a single-limb and step basis, and suggest that elevated limb phase-locking of D2 MSNs may underlie some of the gait impairments associated with dopamine loss.

Cerebellar state estimation enables resilient coupling across behavioural domains

Cerebellar computations are necessary for fine behavioural control and may rely on internal models for estimation of behaviourally relevant states. Here, we propose that the central cerebellar function is to estimate how states interact with each other, and to use these estimates to coordinates extra-cerebellar neuronal dynamics underpinning a range of interconnected behaviours. To support this claim, we describe a cerebellar model for state estimation that includes state interactions, and link this model with the neuronal architecture and dynamics observed empirically. This is formalised using the free energy principle, which provides a dual perspective on a system in terms of both the dynamics of its physical-in this case neuronal-states, and the inferential process they entail. As a demonstration of this proposal, we simulate cerebellar-dependent synchronisation of whisking and respiration, which are known to be tightly coupled in rodents, as well as limb and tail coordination during locomotion. In summary, we propose that the ubiquitous involvement of the cerebellum in behaviour arises from its central role in precisely coupling behavioural domains.

Cerebellar Purkinje Cells Control Posture in Larval Zebrafish (Danio rerio)

Cerebellar dysfunction leads to postural instability. Recent work in freely moving rodents has transformed investigations of cerebellar contributions to posture. However, the combined complexity of terrestrial locomotion and the rodent cerebellum motivate development of new approaches to perturb cerebellar function in simpler vertebrates. Here, we used a powerful chemogenetic tool (TRPV1/capsaicin) to define the role of Purkinje cells - the output neurons of the cerebellar cortex - as larval zebrafish swam freely in depth. We achieved both bidirectional control (activation and ablation) of Purkinje cells while performing quantitative high-throughput assessment of posture and locomotion. Activation disrupted postural control in the pitch (nose-up/nose-down) axis. Similarly, ablations disrupted pitch-axis posture and fin-body coordination responsible for climbs. Postural disruption was more widespread in older larvae, offering a window into emergent roles for the developing cerebellum in the control of posture. Finally, we found that activity in Purkinje cells could individually and collectively encode tilt direction, a key feature of postural control neurons. Our findings delineate an expected role for the cerebellum in postural control and vestibular sensation in larval zebrafish, establishing the validity of TRPV1/capsaicin-mediated perturbations in a simple, genetically-tractable vertebrate. Moreover, by comparing the contributions of Purkinje cell ablations to posture in time, we uncover signatures of emerging cerebellar control of posture across early development. This work takes a major step towards understanding an ancestral role of the cerebellum in regulating postural maturation.

Roles for cerebellum and subsumption architecture in central pattern generation

Within vertebrates, central pattern generators drive rhythmical behaviours, such as locomotion and ventilation. Their pattern generation is also influenced by sensory input and various forms of neuromodulation. These capabilities arose early in vertebrate evolution, preceding the evolution of the cerebellum in jawed vertebrates. This later evolution of the cerebellum is suggestive of subsumption architecture that adds functionality to a pre-existing network. From a central-pattern-generator perspective, what additional functionality might the cerebellum provide? The suggestion is that the adaptive filter capabilities of the cerebellum may be able to use error learning to appropriately repurpose pattern output. Examples may include head and eye stabilization during locomotion, song learning, and context-dependent alternation between learnt motor-control sequences.

Different Purkinje cell pathologies cause specific patterns of progressive gait ataxia in mice

Gait ataxia is one of the most common and impactful consequences of cerebellar dysfunction. Purkinje cells, the sole output neurons of the cerebellar cortex, are often involved in the underlying pathology, but their specific functions during locomotor control in health and disease remain obfuscated. We aimed to describe the effect of gradual adult-onset Purkinje cell degeneration on gaiting patterns in mice, and to determine whether two different mechanisms that both lead to Purkinje cell degeneration cause different patterns in the development of gait ataxia. Using the ErasmusLadder together with a newly developed limb detection algorithm and machine learning-based classification, we subjected mice to a challenging locomotor task with detailed analysis of single limb parameters, intralimb coordination and whole-body movement. We tested two Purkinje cell-specific mouse models, one involving stochastic cell death due to impaired DNA repair mechanisms (Pcp2-Ercc1-/-), the other carrying the mutation that causes spinocerebellar ataxia type 1 (Pcp2-ATXN1[82Q]). Both mouse models showed progressive gaiting deficits, but the sequence with which gaiting parameters deteriorated was different between mouse lines. Our longitudinal approach revealed that gradual loss of Purkinje cell function can lead to a complex pattern of loss of function over time, and that this pattern depends on the specifics of the pathological mechanisms involved. We hypothesize that this variability will also be present in disease progression in patients, and that our findings will facilitate the study of therapeutic interventions in mice, as subtle changes in locomotor abilities can be quantified by our methods.

Dopamine lesions alter the striatal encoding of single-limb gait

The striatum serves an important role in motor control, and neurons in this area encode the body's initiation, cessation, and speed of locomotion. However, it remains unclear whether the same neurons also encode the step-by-step rhythmic motor patterns of individual limbs that characterize gait. By combining high-speed video tracking, electrophysiology, and optogenetic tagging, we found that a sizable population of both D1 and D2 receptor expressing medium spiny projection neurons (MSNs) were phase-locked to the gait cycle of individual limbs in mice. Healthy animals showed balanced limb phase-locking between D1 and D2 MSNs, while dopamine depletion led to stronger phase-locking in D2 MSNs. These findings indicate that striatal neurons represent gait on a single-limb and step basis, and suggest that elevated limb phase-locking of D2 MSNs may underlie some of the gait impairments associated with dopamine loss.

Methods for evaluating gait associated dynamic balance and coordination in rodents

Balance is the dynamic and unconscious control of the body's centre of mass to maintain postural equilibrium. Regulated by the vestibular system, head movement and acceleration are processed by the brain to adjust joints. Several conditions result in a loss of balance, including Alzheimer's Disease, Parkinson's Disease, Menière's Disease and cervical spondylosis, all of which are caused by damage to certain parts of the vestibular pathways. Studies about the impairment of the vestibular system are challenging to carry out in human trials due to smaller study sizes limiting applications of the results and a lacking understanding of the human balance control mechanism. In contrast, more controlled research can be performed in animal studies which have fewer confounding factors than human models and allow specific conditions that affect balance to be replicated. Balance control can be studied using rodent balance-related behavioural tests after spinal or brain lesions, such as the Basso, Beattie and Bresnahan (BBB) Locomotor Scale, Foot Fault Scoring System, Ledged Beam Test, Beam Walking Test, and Ladder Beam Test, which are discussed in this review article along with their advantages and disadvantages. These tests can be performed in preclinical rodent models of femoral nerve injury, stroke, spinal cord injury and neurodegenerative diseases.

Myomatrix arrays for high-definition muscle recording

Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous system's actual motor output - the activation of muscle fibers by motor neurons - typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices ('Myomatrix arrays') that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a 'motor unit,' during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous system's motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and identifying pathologies of the motor system.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Sensory Feedback and Central Neuronal Interactions in Mouse Locomotion

Locomotion is a complex process involving specific interactions between the central neural controller and the mechanical components of the system. The basic rhythmic activity generated by locomotor circuits in the spinal cord defines rhythmic limb movements and their central coordination. The operation of these circuits is modulated by sensory feedback from the limbs providing information about the state of the limbs and the body. However, the specific role and contribution of central interactions and sensory feedback in the control of locomotor gait and posture remain poorly understood. We use biomechanical data on quadrupedal locomotion in mice and recent findings on the organization of neural interactions within the spinal locomotor circuitry to create and analyze a tractable mathematical model of mouse locomotion. The model includes a simplified mechanical model of the mouse body with four limbs and a central controller composed of four rhythm generators, each operating as a state machine controlling the state of one limb. Feedback signals characterize the load and extension of each limb as well as postural stability (balance). We systematically investigate and compare several model versions and compare their behavior to existing experimental data on mouse locomotion. Our results highlight the specific roles of sensory feedback and some central propriospinal interactions between circuits controlling fore and hind limbs for speed-dependent gait expression. Our models suggest that postural imbalance feedback may be critically involved in the control of swing-to-stance transitions in each limb and the stabilization of walking direction.

Large-scale capture of hidden fluorescent labels for training generalizable markerless motion capture models

Deep learning-based markerless tracking has revolutionized studies of animal behavior. Yet the generalizability of trained models tends to be limited, as new training data typically needs to be generated manually for each setup or visual environment. With each model trained from scratch, researchers track distinct landmarks and analyze the resulting kinematic data in idiosyncratic ways. Moreover, due to inherent limitations in manual annotation, only a sparse set of landmarks are typically labeled. To address these issues, we developed an approach, which we term GlowTrack, for generating orders of magnitude more training data, enabling models that generalize across experimental contexts. We describe: a) a high-throughput approach for producing hidden labels using fluorescent markers; b) a multi-camera, multi-light setup for simulating diverse visual conditions; and c) a technique for labeling many landmarks in parallel, enabling dense tracking. These advances lay a foundation for standardized behavioral pipelines and more complete scrutiny of movement.

Gait disorders induced by photothrombotic cerebellar stroke in mice

Patients with cerebellar stroke display relatively mild ataxic gaits. These motor deficits often improve dramatically; however, the neural mechanisms of this improvement have yet to be elucidated. Previous studies in mouse models of gait ataxia, such as ho15J mice and cbln1-null mice, have shown that they have a dysfunction of parallel fiber-Purkinje cell synapses in the cerebellum. However, the effects of cerebellar stroke on the locomotor kinematics of wild-type mice are currently unknown. Here, we performed a kinematic analysis of gait ataxia caused by a photothrombotic stroke in the medial, vermal, and intermediate regions of the cerebellum of wild-type mice. We used the data and observations from this analysis to develop a model that will allow locomotive prognosis and indicate potential treatment regimens following a cerebellar stroke. Our analysis showed that mice performed poorly in a ladder rung test after a stroke. During walking on a treadmill, the mice with induced cerebellar stroke had an increased duty ratio of the hindlimb caused by shortened duration of the swing phase. Overall, our findings suggest that photothrombotic cerebellar infarction and kinematic gait analyses will provide a useful model for quantification of different types of acute management of cerebellar stroke in rodents.

Myomatrix arrays for high-definition muscle recording

Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous system's actual motor output - the activation of muscle fibers by motor neurons - typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices ("Myomatrix arrays") that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a "motor unit", during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous system's motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and in identifying pathologies of the motor system.

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.

Improved 3D Markerless Mouse Pose Estimation Using Temporal Semi-Supervision

Three-dimensional markerless pose estimation from multi-view video is emerging as an exciting method for quantifying the behavior of freely moving animals. Nevertheless, scientifically precise 3D animal pose estimation remains challenging, primarily due to a lack of large training and benchmark datasets and the immaturity of algorithms tailored to the demands of animal experiments and body plans. Existing techniques employ fully supervised convolutional neural networks (CNNs) trained to predict body keypoints in individual video frames, but this demands a large collection of labeled training samples to achieve desirable 3D tracking performance. Here, we introduce a semi-supervised learning strategy that incorporates unlabeled video frames via a simple temporal constraint applied during training. In freely moving mice, our new approach improves the current state-of-the-art performance of multi-view volumetric 3D pose estimation and further enhances the temporal stability and skeletal consistency of 3D tracking.

Dynamic synchronization between hippocampal representations and stepping

The hippocampus is a mammalian brain structure that expresses spatial representations1 and is crucial for navigation2,3. Navigation, in turn, intricately depends on locomotion; however, current accounts suggest a dissociation between hippocampal spatial representations and the details of locomotor processes. Specifically, the hippocampus is thought to represent mainly higher-order cognitive and locomotor variables such as position, speed and direction of movement4-7, whereas the limb movements that propel the animal can be computed and represented primarily in subcortical circuits, including the spinal cord, brainstem and cerebellum8-11. Whether hippocampal representations are actually decoupled from the detailed structure of locomotor processes remains unknown. To address this question, here we simultaneously monitored hippocampal spatial representations and ongoing limb movements underlying locomotion at fast timescales. We found that the forelimb stepping cycle in freely behaving rats is rhythmic and peaks at around 8 Hz during movement, matching the approximately 8 Hz modulation of hippocampal activity and spatial representations during locomotion12. We also discovered precisely timed coordination between the time at which the forelimbs touch the ground ('plant' times of the stepping cycle) and the hippocampal representation of space. Notably, plant times coincide with hippocampal representations that are closest to the actual position of the nose of the rat, whereas between these plant times, the hippocampal representation progresses towards possible future locations. This synchronization was specifically detectable when rats approached spatial decisions. Together, our results reveal a profound and dynamic coordination on a timescale of tens of milliseconds between central cognitive representations and peripheral motor processes. This coordination engages and disengages rapidly in association with cognitive demands and is well suited to support rapid information exchange between cognitive and sensory-motor circuits.

Baseline Gait and Motor Function Predict Long-Term Severity of Neurological Outcomes of Viral Infection

Neurological dysfunction following viral infection varies among individuals, largely due to differences in their genetic backgrounds. Gait patterns, which can be evaluated using measures of coordination, balance, posture, muscle function, step-to-step variability, and other factors, are also influenced by genetic background. Accordingly, to some extent gait can be characteristic of an individual, even prior to changes in neurological function. Because neuromuscular aspects of gait are under a certain degree of genetic control, the hypothesis tested was that gait parameters could be predictive of neuromuscular dysfunction following viral infection. The Collaborative Cross (CC) mouse resource was utilized to model genetically diverse populations and the DigiGait treadmill system used to provide quantitative and objective measurements of 131 gait parameters in 142 mice from 23 CC and SJL/J strains. DigiGait measurements were taken prior to infection with the neurotropic virus Theiler's Murine Encephalomyelitis Virus (TMEV). Neurological phenotypes were recorded over 90 days post-infection (d.p.i.), and the cumulative frequency of the observation of these phenotypes was statistically associated with discrete baseline DigiGait measurements. These associations represented spatial and postural aspects of gait influenced by the 90 d.p.i. phenotype score. Furthermore, associations were found between these gait parameters with sex and outcomes considered to show resistance, resilience, or susceptibility to severe neurological symptoms after long-term infection. For example, higher pre-infection measurement values for the Paw Drag parameter corresponded with greater disease severity at 90 d.p.i. Quantitative trait loci significantly associated with these DigiGait parameters revealed potential relationships between 28 differentially expressed genes (DEGs) and different aspects of gait influenced by viral infection. Thus, these potential candidate genes and genetic variations may be predictive of long-term neurological dysfunction. Overall, these findings demonstrate the predictive/prognostic value of quantitative and objective pre-infection DigiGait measurements for viral-induced neuromuscular dysfunction.

Deciphering the Tubulin Language: Molecular Determinants and Readout Mechanisms of the Tubulin Code in Neurons

Microtubules (MTs) are dynamic components of the cell cytoskeleton involved in several cellular functions, such as structural support, migration and intracellular trafficking. Despite their high similarity, MTs have functional heterogeneity that is generated by the incorporation into the MT lattice of different tubulin gene products and by their post-translational modifications (PTMs). Such regulations, besides modulating the tubulin composition of MTs, create on their surface a "biochemical code" that is translated, through the action of protein effectors, into specific MT-based functions. This code, known as "tubulin code", plays an important role in neuronal cells, whose highly specialized morphologies and activities depend on the correct functioning of the MT cytoskeleton and on its interplay with a myriad of MT-interacting proteins. In recent years, a growing number of mutations in genes encoding for tubulins, MT-interacting proteins and enzymes that post-translationally modify MTs, which are the main players of the tubulin code, have been linked to neurodegenerative processes or abnormalities in neural migration, differentiation and connectivity. Nevertheless, the exact molecular mechanisms through which the cell writes and, downstream, MT-interacting proteins decipher the tubulin code are still largely uncharted. The purpose of this review is to describe the molecular determinants and the readout mechanisms of the tubulin code, and briefly elucidate how they coordinate MT behavior during critical neuronal events, such as neuron migration, maturation and axonal transport.

A Layered, Hybrid Machine Learning Analytic Workflow for Mouse Risk Assessment Behavior

Accurate and efficient quantification of animal behavior facilitates the understanding of the brain. An emerging approach within machine learning (ML) field is to combine multiple ML-based algorithms to quantify animal behavior. These so-called hybrid models have emerged because of limitations associated with supervised [e.g., random forest (RF)] and unsupervised [e.g., hidden Markov model (HMM)] ML models. For example, RF models lack temporal information across video frames, and HMM latent states are often difficult to interpret. We sought to develop a hybrid model, and did so in the context of a study of mouse risk assessment behavior. We used DeepLabCut to estimate the positions of mouse body parts. Positional features were calculated using DeepLabCut outputs and were used to train RF and HMM models with equal number of states, separately. The per-frame predictions from RF and HMM models were then passed to a second HMM model layer ("reHMM"). The outputs of the reHMM layer showed improved interpretability over the initial HMM output. Finally, we combined predictions from RF and HMM models with selected positional features to train a third HMM model ("reHMM+"). This reHMM+ layered hybrid model unveiled distinctive temporal and human-interpretable behavioral patterns. We applied this workflow to investigate risk assessment to trimethylthiazoline and snake feces odor, finding unique behavioral patterns to each that were separable from attractive and neutral stimuli. We conclude that this layered, hybrid ML workflow represents a balanced approach for improving the depth and reliability of ML classifiers in chemosensory and other behavioral contexts.

Neurological scoring and gait kinematics to assess functional outcome in an ovine model of ischaemic stroke

Background

Assessment of functional impairment following ischaemic stroke is essential to determine outcome and efficacy of intervention in both clinical patients and pre-clinical models. Although paradigms are well described for rodents, comparable methods for large animals, such as sheep, remain limited. This study aimed to develop methods to assess function in an ovine model of ischaemic stroke using composite neurological scoring and gait kinematics from motion capture.

Methods

Merino sheep (n = 26) were anaesthetised and subjected to 2 hours middle cerebral artery occlusion. Animals underwent functional assessment at baseline (8-, 5-, and 1-day pre-stroke), and 3 days post-stroke. Neurological scoring was carried out to determine changes in neurological status. Ten infrared cameras measured the trajectories of 42 retro-reflective markers for calculation of gait kinematics. Magnetic resonance imaging (MRI) was performed at 3 days post-stroke to determine infarct volume. Intraclass Correlation Coefficients (ICC's) were used to assess the repeatability of neurological scoring and gait kinematics across baseline trials. The average of all baselines was used to compare changes in neurological scoring and kinematics at 3 days post-stroke. A principal component analysis (PCA) was performed to determine the relationship between neurological score, gait kinematics, and infarct volume post-stroke.

Results

Neurological scoring was moderately repeatable across baseline trials (ICC > 0.50) and detected marked impairment post-stroke (p < 0.05). Baseline gait measures showed moderate to good repeatability for the majority of assessed variables (ICC > 0.50). Following stroke, kinematic measures indicative of stroke deficit were detected including an increase in stance and stride duration (p < 0.05). MRI demonstrated infarction involving the cortex and/or thalamus (median 2.7 cm³, IQR 1.4 to 11.9). PCA produced two components, although association between variables was inconclusive.

Conclusion

This study developed repeatable methods to assess function in sheep using composite scoring and gait kinematics, allowing for the evaluation of deficit 3 days post-stroke. Despite utility of each method independently, there was poor association observed between gait kinematics, composite scoring, and infarct volume on PCA. This suggests that each of these measures has discreet utility for the assessment of stroke deficit, and that multimodal approaches are necessary to comprehensively characterise functional impairment.

Automated preclinical detection of mechanical pain hypersensitivity and analgesia

ABSTRACT

The lack of sensitive and robust behavioral assessments of pain in preclinical models has been a major limitation for both pain research and the development of novel analgesics. Here, we demonstrate a novel data acquisition and analysis platform that provides automated, quantitative, and objective measures of naturalistic rodent behavior in an observer-independent and unbiased fashion. The technology records freely behaving mice, in the dark, over extended periods for continuous acquisition of 2 parallel video data streams: (1) near-infrared frustrated total internal reflection for detecting the degree, force, and timing of surface contact and (2) simultaneous ongoing video graphing of whole-body pose. Using machine vision and machine learning, we automatically extract and quantify behavioral features from these data to reveal moment-by-moment changes that capture the internal pain state of rodents in multiple pain models. We show that these voluntary pain-related behaviors are reversible by analgesics and that analgesia can be automatically and objectively differentiated from sedation. Finally, we used this approach to generate a paw luminance ratio measure that is sensitive in capturing dynamic mechanical hypersensitivity over a period and scalable for high-throughput preclinical analgesic efficacy assessment.

Recent Advances at the Interface of Neuroscience and Artificial Neural Networks

Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural networks (ANNs) have exploited biological properties to solve complex problems. However, despite their effectiveness for specific tasks, ANNs are yet to realize the flexibility and adaptability of biological cognition. This review highlights recent advances in computational and experimental research to advance our understanding of biological and artificial intelligence. In particular, we discuss critical mechanisms from the cellular, systems, and cognitive neuroscience fields that have contributed to refining the architecture and training algorithms of ANNs. Additionally, we discuss how recent work used ANNs to understand complex neuronal correlates of cognition and to process high throughput behavioral data.

Estimation of skeletal kinematics in freely moving rodents

Forming a complete picture of the relationship between neural activity and skeletal kinematics requires quantification of skeletal joint biomechanics during free behavior; however, without detailed knowledge of the underlying skeletal motion, inferring limb kinematics using surface-tracking approaches is difficult, especially for animals where the relationship between the surface and underlying skeleton changes during motion. Here we developed a videography-based method enabling detailed three-dimensional kinematic quantification of an anatomically defined skeleton in untethered freely behaving rats and mice. This skeleton-based model was constrained using anatomical principles and joint motion limits and provided skeletal pose estimates for a range of body sizes, even when limbs were occluded. Model-inferred limb positions and joint kinematics during gait and gap-crossing behaviors were verified by direct measurement of either limb placement or limb kinematics using inertial measurement units. Together we show that complex decision-making behaviors can be accurately reconstructed at the level of skeletal kinematics using our anatomically constrained model.

Deep learning-based behavioral profiling of rodent stroke recovery

BACKGROUND

Stroke research heavily relies on rodent behavior when assessing underlying disease mechanisms and treatment efficacy. Although functional motor recovery is considered the primary targeted outcome, tests in rodents are still poorly reproducible and often unsuitable for unraveling the complex behavior after injury.

RESULTS

Here, we provide a comprehensive 3D gait analysis of mice after focal cerebral ischemia based on the new deep learning-based software (DeepLabCut, DLC) that only requires basic behavioral equipment. We demonstrate a high precision 3D tracking of 10 body parts (including all relevant joints and reference landmarks) in several mouse strains. Building on this rigor motion tracking, a comprehensive post-analysis (with >100 parameters) unveils biologically relevant differences in locomotor profiles after a stroke over a time course of 3 weeks. We further refine the widely used ladder rung test using deep learning and compare its performance to human annotators. The generated DLC-assisted tests were then benchmarked to five widely used conventional behavioral set-ups (neurological scoring, rotarod, ladder rung walk, cylinder test, and single-pellet grasping) regarding sensitivity, accuracy, time use, and costs.

CONCLUSIONS

We conclude that deep learning-based motion tracking with comprehensive post-analysis provides accurate and sensitive data to describe the complex recovery of rodents following a stroke. The experimental set-up and analysis can also benefit a range of other neurological injuries that affect locomotion.

Biallelic PAX5 mutations cause hypogammaglobulinemia, sensorimotor deficits, and autism spectrum disorder

The genetic causes of primary antibody deficiencies and autism spectrum disorder (ASD) are largely unknown. Here, we report a patient with hypogammaglobulinemia and ASD who carries biallelic mutations in the transcription factor PAX5. A patient-specific Pax5 mutant mouse revealed an early B cell developmental block and impaired immune responses as the cause of hypogammaglobulinemia. Pax5 mutant mice displayed behavioral deficits in all ASD domains. The patient and the mouse model showed aberrant cerebellar foliation and severely impaired sensorimotor learning. PAX5 deficiency also caused profound hypoplasia of the substantia nigra and ventral tegmental area due to loss of GABAergic neurons, thus affecting two midbrain hubs, controlling motor function and reward processing, respectively. Heterozygous Pax5 mutant mice exhibited similar anatomic and behavioral abnormalities. Lineage tracing identified Pax5 as a crucial regulator of cerebellar morphogenesis and midbrain GABAergic neurogenesis. These findings reveal new roles of Pax5 in brain development and unravel the underlying mechanism of a novel immunological and neurodevelopmental syndrome.

The what, how, and why of naturalistic behavior

In the past few years, advances in machine learning have fueled an explosive growth of descriptive and generative models of animal behavior. These new approaches offer higher levels of detail and granularity than has previously been possible, allowing for fine-grained segmentation of animals' actions and precise quantitative mappings between an animal's sensory environment and its behavior. How can these new methods help us understand the governing principles shaping complex and naturalistic behavior? In this review, we will recap ways in which our ability to detect and model behavior have improved in recent years, and consider how these techniques might be used to revisit classical normative theories of behavioral control.

A Large-Scale Mouse Pose Dataset for Mouse Pose Estimation

Mouse pose estimations have important applications in the fields of animal behavior research, biomedicine, and animal conservation studies. Accurate and efficient mouse pose estimations using computer vision are necessary. Although methods for mouse pose estimations have developed, bottlenecks still exist. One of the most prominent problems is the lack of uniform and standardized training datasets. Here, we resolve this difficulty by introducing the mouse pose dataset. Our mouse pose dataset contains 40,000 frames of RGB images and large-scale 2D ground-truth motion images. All the images were captured from interacting lab mice through a stable single viewpoint, including 5 distinct species and 20 mice in total. Moreover, to improve the annotation efficiency, five keypoints of mice are creatively proposed, in which one keypoint is at the center and the other two pairs of keypoints are symmetric. Then, we created simple, yet effective software that works for annotating images. It is another important link to establish a benchmark model for 2D mouse pose estimations. We employed modified object detections and pose estimation algorithms to achieve precise, effective, and robust performances. As the first large and standardized mouse pose dataset, our proposed mouse pose dataset will help advance research on animal pose estimations and assist in application areas related to animal experiments.

Behavioral and physiological monitoring for awake neurovascular coupling experiments: a how-to guide

Significance: Functional brain imaging in awake animal models is a popular and powerful technique that allows the investigation of neurovascular coupling (NVC) under physiological conditions. However, ubiquitous facial and body motions (fidgeting) are prime drivers of spontaneous fluctuations in neural and hemodynamic signals. During periods without movement, animals can rapidly transition into sleep, and the hemodynamic signals tied to arousal state changes can be several times larger than sensory-evoked responses. Given the outsized influence of facial and body motions and arousal signals in neural and hemodynamic signals, it is imperative to detect and monitor these events in experiments with un-anesthetized animals. Aim: To cover the importance of monitoring behavioral state in imaging experiments using un-anesthetized rodents, and describe how to incorporate detailed behavioral and physiological measurements in imaging experiments. Approach: We review the effects of movements and sleep-related signals (heart rate, respiration rate, electromyography, intracranial pressure, whisking, and other body movements) on brain hemodynamics and electrophysiological signals, with a focus on head-fixed experimental setup. We summarize the measurement methods currently used in animal models for detection of those behaviors and arousal changes. We then provide a guide on how to incorporate this measurements with functional brain imaging and electrophysiology measurements. Results: We provide a how-to guide on monitoring and interpreting a variety of physiological signals and their applications to NVC experiments in awake behaving mice. Conclusion: This guide facilitates the application of neuroimaging in awake animal models and provides neuroscientists with a standard approach for monitoring behavior and other associated physiological parameters in head-fixed animals.

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.

Detecting fine and elaborate movements with piezo sensors provides non-invasive access to overlooked behavioral components

Behavioral phenotyping devices have been successfully used to build ethograms, but many aspects of behavior remain out of reach of available phenotyping systems. We now report on a novel device, which consists in an open-field platform resting on highly sensitive piezoelectric (electromechanical) pressure-sensors, with which we could detect the slightest movements (up to individual heart beats during rest) from freely moving rats and mice. The combination with video recordings and signal analysis based on time-frequency decomposition, clustering, and machine learning algorithms provided non-invasive access to previously overlooked behavioral components. The detection of shaking/shivering provided an original readout of fear, distinct from but complementary to behavioral freezing. Analyzing the dynamics of momentum in locomotion and grooming allowed to identify the signature of gait and neurodevelopmental pathological phenotypes. We believe that this device represents a significant progress and offers new opportunities for the awaited advance of behavioral phenotyping.

Stride-level analysis of mouse open field behavior using deep-learning-based pose estimation

Gait and posture are often perturbed in many neurological, neuromuscular, and neuropsychiatric conditions. Rodents provide a tractable model for elucidating disease mechanisms and interventions. Here, we develop a neural-network-based assay that adopts the commonly used open field apparatus for mouse gait and posture analysis. We quantitate both with high precision across 62 strains of mice. We characterize four mutants with known gait deficits and demonstrate that multiple autism spectrum disorder (ASD) models show gait and posture deficits, implying this is a general feature of ASD. Mouse gait and posture measures are highly heritable and fall into three distinct classes. We conduct a genome-wide association study to define the genetic architecture of stride-level mouse movement in the open field. We provide a method for gait and posture extraction from the open field and one of the largest laboratory mouse gait and posture data resources for the research community.

Sheppard et al. present a method for gait and posture analysis in the common open field apparatus using neural-network-based pose estimation. They apply this high-throughput method to dissect the genetic architecture of mouse movement.

Large-scale neural recordings call for new insights to link brain and behavior

Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. In the present review, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modeling frameworks to interpret these data, and argue that the interpretation of brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain.

The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions

Multi-agent behavior modeling aims to understand the interactions that occur between agents. We present a multi-agent dataset from behavioral neuroscience, the Caltech Mouse Social Interactions (CalMS21) Dataset. Our dataset consists of trajectory data of social interactions, recorded from videos of freely behaving mice in a standard resident-intruder assay. To help accelerate behavioral studies, the CalMS21 dataset provides benchmarks to evaluate the performance of automated behavior classification methods in three settings: (1) for training on large behavioral datasets all annotated by a single annotator, (2) for style transfer to learn inter-annotator differences in behavior definitions, and (3) for learning of new behaviors of interest given limited training data. The dataset consists of 6 million frames of unlabeled tracked poses of interacting mice, as well as over 1 million frames with tracked poses and corresponding frame-level behavior annotations. The challenge of our dataset is to be able to classify behaviors accurately using both labeled and unlabeled tracking data, as well as being able to generalize to new settings.

Anipose: A toolkit for robust markerless 3D pose estimation

Quantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals move in 3D. Here, we introduce Anipose, an open-source toolkit for robust markerless 3D pose estimation. Anipose is built on the 2D tracking method DeepLabCut, so users can expand their existing experimental setups to obtain accurate 3D tracking. It consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial regularization, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on a calibration board as well as mice, flies, and humans. By analyzing 3D leg kinematics tracked with Anipose, we identify a key role for joint rotation in motor control of fly walking. To help users get started with 3D tracking, we provide tutorials and documentation at http://anipose.org/.

The Childhood-Onset Neurodegeneration with Cerebellar Atrophy (CONDCA) Disease Caused by AGTPBP1 Gene Mutations: The Purkinje Cell Degeneration Mouse as an Animal Model for the Study of this Human Disease

Recent reports have identified rare, biallelic damaging variants of the AGTPBP1 gene that cause a novel and documented human disease known as childhood-onset neurodegeneration with cerebellar atrophy (CONDCA), linking loss of function of the AGTPBP1 protein to human neurodegenerative diseases. CONDCA patients exhibit progressive cognitive decline, ataxia, hypotonia or muscle weakness among other clinical features that may be fatal. Loss of AGTPBP1 in humans recapitulates the neurodegenerative course reported in a well-characterised murine animal model harbouring loss-of-function mutations in the AGTPBP1 gene. In particular, in the Purkinje cell degeneration (pcd) mouse model, mutations in AGTPBP1 lead to early cerebellar ataxia, which correlates with the massive loss of cerebellar Purkinje cells. In addition, neurodegeneration in the olfactory bulb, retina, thalamus and spinal cord were also reported. In addition to neurodegeneration, pcd mice show behavioural deficits such as cognitive decline. Here, we provide an overview of what is currently known about the structure and functional role of AGTPBP1 and discuss the various alterations in AGTPBP1 that cause neurodegeneration in the pcd mutant mouse and humans with CONDCA. The sequence of neuropathological events that occur in pcd mice and the mechanisms governing these neurodegenerative processes are also reported. Finally, we describe the therapeutic strategies that were applied in pcd mice and focus on the potential usefulness of pcd mice as a promising model for the development of new therapeutic strategies for clinical trials in humans, which may offer potential beneficial options for patients with AGTPBP1 mutation-related CONDCA.

LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals

Markerless three-dimensional (3D) pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D poses by multi-view triangulation of deep network-based two-dimensional (2D) pose estimates. However, triangulation requires multiple synchronized cameras and elaborate calibration protocols that hinder its widespread adoption in laboratory studies. Here we describe LiftPose3D, a deep network-based method that overcomes these barriers by reconstructing 3D poses from a single 2D camera view. We illustrate LiftPose3D's versatility by applying it to multiple experimental systems using flies, mice, rats and macaques, and in circumstances where 3D triangulation is impractical or impossible. Our framework achieves accurate lifting for stereotypical and nonstereotypical behaviors from different camera angles. Thus, LiftPose3D permits high-quality 3D pose estimation in the absence of complex camera arrays and tedious calibration procedures and despite occluded body parts in freely behaving animals.

Shifts in Gait Signatures Mark the End of Lifespan in Mice, With Sex Differences in Timing

Reduced walking speed is a hallmark of functional decline in aging across species. An age-related change in walking style may represent an additional key marker signifying deterioration of the nervous system. Due to the speed dependence of gait metrics combined with slowing of gait during aging, it has been challenging to determine whether changes in gait metrics represent a change in style. In this longitudinal study we employed gait signatures to separate changes in walking style and speed in mice. We compared gait signatures at mature adult age with middle aged, old and geriatric time points and included female and male sub-cohorts to examine sex differences in nature or timing signature shifts. To determine whether gait signature shifts occurred independently from a decline in other mobility domains we measured balance and locomotor activity. We found that walking speed declined early, whereas gait signatures shifted very late during the aging process. Shifts represented longer swing time and stride length than expected for speed, as in slow motion, and were preceded by a decline in other mobility domains. The pattern of shifts was similar between female and male cohorts, but with sex differences in timing. We conclude that changes in walking style, speed and other mobility domains represent separate age-related phenomena. These findings call for careful, sex specific selection of type and timing of outcome measures in mechanistic or interventional studies. The pattern of age-related gait signature shifts is distinct from patterns seen in neurodegenerative conditions and may be a translatable marker for the end of the lifespan.

A Neural Network Reveals Motoric Effects of Maternal Preconception Exposure to Nicotine on Rat Pup Behavior: A New Approach for Movement Disorders Diagnosis

Neurodevelopmental disorders can stem from pharmacological, genetic, or environmental causes and early diagnosis is often a key to successful treatment. To improve early detection of neurological motor impairments, we developed a deep neural network for data-driven analyses. The network was applied to study the effect of maternal nicotine exposure prior to conception on 10-day-old rat pup motor behavior in an open field task. Female Long-Evans rats were administered nicotine (15 mg/L) in sweetened drinking water (1% sucralose) for seven consecutive weeks immediately prior to mating. The neural network outperformed human expert designed animal locomotion measures in distinguishing rat pups born to nicotine exposed dams vs. control dams (87 vs. 64% classification accuracy). Notably, the network discovered novel movement alterations in posture, movement initiation and a stereotypy in "warm-up" behavior (repeated movements along specific body dimensions) that were predictive of nicotine exposure. The results suggest novel findings that maternal preconception nicotine exposure delays and alters offspring motor development. Similar behavioral symptoms are associated with drug-related causes of disorders such as autism spectrum disorder and attention-deficit/hyperactivity disorder in human children. Thus, the identification of motor impairments in at-risk offspring here shows how neuronal networks can guide the development of more accurate behavioral tests to earlier diagnose symptoms of neurodevelopmental disorders in infants and children.

Pupal behavior emerges from unstructured muscle activity in response to neuromodulation in Drosophila

Identifying neural substrates of behavior requires defining actions in terms that map onto brain activity. Brain and muscle activity naturally correlate via the output of motor neurons, but apart from simple movements it has been difficult to define behavior in terms of muscle contractions. By mapping the musculature of the pupal fruit fly and comprehensively imaging muscle activation at single-cell resolution, we here describe a multiphasic behavioral sequence in Drosophila. Our characterization identifies a previously undescribed behavioral phase and permits extraction of major movements by a convolutional neural network. We deconstruct movements into a syllabary of co-active muscles and identify specific syllables that are sensitive to neuromodulatory manipulations. We find that muscle activity shows considerable variability, with sequential increases in stereotypy dependent upon neuromodulation. Our work provides a platform for studying whole-animal behavior, quantifying its variability across multiple spatiotemporal scales, and analyzing its neuromodulatory regulation at cellular resolution.

How do we find out how the brain works? One way is to use imaging techniques to visualise an animal’s brain in action as it performs simple behaviours: as the animal moves, parts of its brain light up under the microscope. For laboratory animals like fruit flies, which have relatively small brains, this lets us observe their brain activity right down to the level of individual brain cells.

The brain directs movements via collective activity of the body’s muscles. Our ability to track the activity of individual muscles is, however, more limited than our ability to observe single brain cells: even modern imaging technology still cannot monitor the activity of all the muscle cells in an animal’s body as it moves about. Yet this is precisely the information that scientists need to fully understand how the brain generates behaviour.

Fruit flies perform specific behaviours at certain stages of their life cycle. When the fly pupa begins to metamorphose into an adult insect, it performs a fixed sequence of movements involving a set number of muscles, which is called the pupal ecdysis sequence. This initial movement sequence and the rest of metamorphosis both occur within the confines of the pupal case, which is a small, hardened shell surrounding the whole animal. Elliott et al. set out to determine if the fruit fly pupa’s ecdysis sequence could be used as a kind of model, to describe a simple behaviour at the level of individual muscles.

Imaging experiments used fly pupae that were genetically engineered to produce an activity-dependent fluorescent protein in their muscle cells. Pupal cases were treated with a chemical to make them transparent, allowing easy observation of their visually ‘labelled’ muscles. This yielded a near-complete record of muscle activity during metamorphosis.

Initially, individual muscles became active in small groups. The groups then synchronised with each other over the different regions of the pupa’s body to form distinct movements, much as syllables join to form words. This synchronisation was key to progression through metamorphosis and was co-ordinated at each step by specialised nerve cells that produce or respond to specific hormones.

These results reveal how the brain might direct muscle activity to produce movement patterns. In the future, Elliott et al. hope to compare data on muscle activity with comprehensive records of brain cell activity, to shed new light on how the brain, muscles, and other factors work together to control behaviour.

GluA4 facilitates cerebellar expansion coding and enables associative memory formation

AMPA receptors (AMPARs) mediate excitatory neurotransmission in the central nervous system (CNS) and their subunit composition determines synaptic efficacy. Whereas AMPAR subunits GluA1–GluA3 have been linked to particular forms of synaptic plasticity and learning, the functional role of GluA4 remains elusive. Here, we demonstrate a crucial function of GluA4 for synaptic excitation and associative memory formation in the cerebellum. Notably, GluA4-knockout mice had ~80% reduced mossy fiber to granule cell synaptic transmission. The fidelity of granule cell spike output was markedly decreased despite attenuated tonic inhibition and increased NMDA receptor-mediated transmission. Computational network modeling incorporating these changes revealed that deletion of GluA4 impairs granule cell expansion coding, which is important for pattern separation and associative learning. On a behavioral level, while locomotor coordination was generally spared, GluA4-knockout mice failed to form associative memories during delay eyeblink conditioning. These results demonstrate an essential role for GluA4-containing AMPARs in cerebellar information processing and associative learning.

Distinct Kinematic Adjustments over Multiple Timescales Accompany Locomotor Skill Development in Mice

Robust locomotion is critical to many species' survival, yet the mechanisms by which efficient locomotion is learned and maintained are poorly understood. In mice, a common paradigm for assaying locomotor learning is the rotarod task, in which mice learn to maintain balance atop of an accelerating rod. However, the standard metric for learning in this task is improvements in latency to fall, which gives little insight into the rich kinematic adjustments that accompany locomotor learning. In this study, we developed a rotarod-like task called the RotaWheel in which changes in paw kinematics are tracked using high-speed cameras as mice learn to stay atop an accelerating wheel. Using this device, we found that learning was accompanied by stereotyped progressions of paw kinematics that correlated with early, intermediate, and late stages of performance. Within the first day, mice sharpened their interlimb coordination using a timed pause in the forward swing of their forepaws. Over the next several days, mice reduced their stride length and took shorter, quicker steps. By the second week of training, mice began to use a more variable locomotor strategy, where consecutive overshoots or undershoots in strides were selected across paws to drive forward and backward exploration of the wheel. Collectively, our results suggest that mouse locomotor learning occurs through multiple mechanisms evolving over separate time courses and involving distinct corrective actions. These data provide insights into the kinematic strategies that accompany locomotor learning and establish an experimental platform for studying locomotor skill learning in mice.

Common and distinct muscle synergies during level and slope locomotion in the cat

Although it is well established that the motor control system is modular, the organization of muscle synergies during locomotion and their change with ground slope are not completely understood. For example, typical reciprocal flexor-extensor muscle synergies of level walking in cats break down in downslope: one-joint hip extensors are silent throughout the stride cycle, whereas hindlimb flexors demonstrate an additional stance phase-related electromyogram (EMG) burst (Smith JL, Carlson-Kuhta P, Trank TV. J Neurophysiol 79: 1702-1716, 1998). Here, we investigated muscle synergies during level, upslope (27°), and downslope (-27°) walking in adult cats to examine common and distinct features of modular organization of locomotor EMG activity. Cluster analysis of EMG burst onset-offset times of 12 hindlimb muscles revealed five flexor and extensor burst groups that were generally shared across slopes. Stance-related bursts of flexor muscles in downslope were placed in a burst group from level and upslope walking formed by the rectus femoris. Walking upslope changed swing/stance phase durations of level walking but not the cycle duration. Five muscle synergies computed using non-negative matrix factorization accounted for at least 95% of variance in EMG patterns in each slope. Five synergies were shared between level and upslope walking, whereas only three of those were shared with downslope synergies; these synergies were active during the swing phase and phase transitions. Two stance-related synergies of downslope walking were distinct; they comprised a mixture of flexors and extensors. We suggest that the modular organization of muscle activity during level and slope walking results from interactions between motion-related sensory feedback, CPG, and supraspinal inputs.NEW & NOTEWORTHY We demonstrated that the atypical EMG activities during cat downslope walking, silent one-joint hip extensors and stance-related EMG bursts in flexors, have many features shared with activities of level and upslope walking. Majority of EMG burst groups and muscle synergies were shared among these slopes, and upslope modulated the swing/stance phase duration but not cycle duration. Thus, synergistic EMG activities in all slopes might result from a shared CPG receiving somatosensory and supraspinal inputs.

OptiFlex: Multi-Frame Animal Pose Estimation Combining Deep Learning With Optical Flow

Animal pose estimation tools based on deep learning have greatly improved animal behaviour quantification. These tools perform pose estimation on individual video frames, but do not account for variability of animal body shape in their prediction and evaluation. Here, we introduce a novel multi-frame animal pose estimation framework, referred to as OptiFlex. This framework integrates a flexible base model (i.e., FlexibleBaseline), which accounts for variability in animal body shape, with an OpticalFlow model that incorporates temporal context from nearby video frames. Pose estimation can be optimised using multi-view information to leverage all four dimensions (3D space and time). We evaluate FlexibleBaseline using datasets of four different lab animal species (mouse, fruit fly, zebrafish, and monkey) and introduce an intuitive evaluation metric-adjusted percentage of correct key points (aPCK). Our analyses show that OptiFlex provides prediction accuracy that outperforms current deep learning based tools, highlighting its potential for studying a wide range of behaviours across different animal species.

Cerebellum-Specific Deletion of the GABAA Receptor δ Subunit Leads to Sex-Specific Disruption of Behavior

Granule cells (GCs) of the cerebellar input layer express high-affinity δ GABA_A subunit-containing GABA_A receptors (δGABA_ARs) that respond to ambient GABA levels and context-dependent neuromodulators like steroids. We find that GC-specific deletion of δGABA_A (cerebellar [cb] δ knockout [KO]) decreases tonic inhibition, makes GCs hyperexcitable, and in turn, leads to differential activation of cb output regions as well as many cortical and subcortical brain areas involved in cognition, anxiety-like behaviors, and the stress response. Cb δ KO mice display deficits in many behaviors, but motor function is normal. Strikingly, δGABA_A deletion alters maternal behavior as well as spontaneous, stress-related, and social behaviors specifically in females. Our findings establish that δGABA_ARs enable the cerebellum to control diverse behaviors not previously associated with the cerebellum in a sex-dependent manner. These insights may contribute to a better understanding of the mechanisms that underlie behavioral abnormalities in psychiatric and neurodevelopmental disorders that display a gender bias.

Rudolph et al. show that deletion of the neuromodulator and hormone-sensitive δGABA_A receptor subunit from cerebellar granule cells results in anxiety-like behaviors and female-specific deficits in social behavior and maternal care. δGABA_A deletion is associated with hyperexcitability of the cerebellar input layer and altered activation of many stress-related brain regions.