Real-time, low-latency closed-loop feedback using markerless posture tracking

Task-specific motor units in the extrinsic hand muscles control single- and multidigit tasks of the human hand

Movements of the hand require a precise distribution of synaptic inputs to spinal motor neurons innervating intrinsic and extrinsic hand muscles. Humans can generate complex multidigit tasks as well as separate motions of individual digits. The specific mechanisms by which the central nervous system controls multidigit and single-digit tasks on a motor neuron level remain poorly understood. We recorded synchronized three-dimensional hand kinematics and high-density surface electromyographic data from extrinsic hand muscles, including all extrinsic thumb and digit flexors and extensors. Twelve participants each performed 13 dynamic periodic single-digit flexion and extension- and multidigit grasping tasks for 45 s per task. Multidigit tasks were composed of combinations of the performed single-digit tasks. We decoded single motor unit (MU) activity from 7.8 ± 1.8 MUs (means ± SD) per task and participant in all muscles and identified MUs across tasks. For single-digit tasks, as expected, the activity of some MUs was associated with digit kinematics (task-modulated MUs), whereas other MUs discharged in a tonic way with little modulation of their discharge rate. MUs showed task-modulated activity only for one specific single digit. Moreover, a relatively small proportion of task-modulated MUs active during single-digit tasks could be identified during the multidigit grasping tasks [median 7.5%, interquartile range (IQR) 2.2%-15.0%]. Similarly, only few task-modulated MUs were identified in more than one multidigit task (median 3.6%, IQR 0%-18.4%). These results indicate a high task specificity in the control of MUs determining hand motions.NEW & NOTEWORTHY We investigated the neural control of motor units in the extrinsic hand muscles during dynamic single- and multidigit movements. We consistently found motor units that modulated the generation of flexion and extension movements of individual digits. Only a small number of motor units were active in both single- and multidigit tasks. The findings suggest high neural specificity in the recruitment and modulation of discharge rates for motor units controlling single- and multidigit hand tasks.

Review on design of real-time posture monitoring system for the cervical region

In cervical health, the Posture Monitoring System (PMS) employs sensors to capture and transmit posture data to the cloud via Wi-Fi. This systematic review examines wearable PMS devices for cervical posture, analysing their attributes, findings, and limitations. Using systematic literature analysis, related studies were collected from diverse databases concentrating on wearable cervical posture devices. The review analysed the outcomes of each neck posture and each monitor type on the CVA ratio based on PMS. However, limitations, such as small sample sizes, limited functions, and privacy concerns were noted across the devices. The findings underscore the importance of considering user comfort and data accuracy in designing and implementing wearable posture monitors. Future studies should also explore the integration of advanced technologies and user-centred design principles to develop more accurate and user-friendly devices.

Of rats and robots: A mutual learning paradigm

Robots are increasingly used alongside Skinner boxes to train animals in operant conditioning tasks. Similarly, animals are being employed in artificial intelligence research to train various algorithms. However, both types of experiments rely on unidirectional learning, where one partner-the animal or the robot-acts as the teacher and the other as the student. Here, we present a novel animal-robot interaction paradigm that enables bidirectional, or mutual, learning between a Wistar rat and a robot. The two agents interacted with each other to achieve specific goals, dynamically adjusting their actions based on the positive (rewarding) or negative (punishing) signals provided by their partner. The paradigm was tested in silico with two artificial reinforcement learning agents and in vivo with different rat-robot pairs. In the virtual trials, both agents were able to adapt their behavior toward reward maximization, achieving mutual learning. The in vivo experiments revealed that rats rapidly acquired the behaviors necessary to receive the reward and exhibited passive avoidance learning for negative signals when the robot displayed a steep learning curve. The developed paradigm can be used in various animal-machine interactions to test the efficacy of different learning rules and reinforcement schedules.

Continuous Auditory Feedback Promotes Fine Motor Skill Learning in Mice

Motor skill learning enables organisms to interact effectively with their environment, relying on neural mechanisms that integrate sensory feedback with motor output. While sensory feedback, such as auditory cues linked to motor actions, enhances motor performance in humans, its mechanism of action is poorly understood. Developing a reliable animal model of augmented motor skill learning is crucial to begin dissecting the biological systems that underpin this enhancement. We hypothesized that continuous auditory feedback during a motor task would promote complex motor skill acquisition in mice. We developed a closed-loop system using DeepLabCut for real-time markerless tracking of mouse forepaw movements with high processing speed and low latency. By encoding forepaw movements into auditory tones of different frequencies, mice received continuous auditory feedback during a reaching task requiring vertical displacement of the left forepaw to a target. Adult mice were trained over 4 d with either auditory feedback or no feedback. Mice receiving auditory feedback exhibited significantly enhanced motor skill learning compared with controls. Clustering analysis of reaching trajectories showed that auditory feedback mice established consistent reaching trajectories by Day 2 of motor training. These findings demonstrate that real-time, movement-coded auditory feedback effectively promotes motor skill learning in mice. This closed-loop system, leveraging advanced machine learning and real-time tracking, offers new avenues for exploring motor control mechanisms and developing therapeutic strategies for motor disorders through augmented sensory feedback.

A real-time, multi-subject three-dimensional pose tracking system for the behavioral analysis of non-human primates

The ability to track the positions and poses of multiple animals in three-dimensional (3D) space in real time is highly desired by non-human primate (NHP) researchers in behavioral and systems neuroscience. This capability enables the analysis of social behaviors involving multiple NHPs and supports closed-loop experiments. Although several animal 3D pose tracking systems have been developed, most are difficult to deploy in new environments and lack real-time analysis capabilities. To address these limitations, we developed MarmoPose, a deep-learning-based, real-time 3D pose tracking system for multiple common marmosets, an increasingly critical NHP model in neuroscience research. This system can accurately track the 3D poses of multiple marmosets freely moving in their home cage with minimal hardware requirements. By employing a marmoset skeleton model, MarmoPose can further optimize 3D poses and estimate invisible body locations. Additionally, MarmoPose achieves high inference speeds and enables real-time closed-loop experimental control based on events detected from 3D poses.

In vivo electrophysiology recordings and computational modeling can predict octopus arm movement

The octopus has many features that make it advantageous for revealing principles of motor circuits and control and predicting behavior. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100 ms after stimulation were predictive of the resultant movement response. Machine learning models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. For kinematic analysis, deep learning models and unsupervised dimensionality reduction identified a consistent set of features that could be used to distinguish different types of arm movements. The neural circuits and the computational models identified here generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit. This study demonstrates how real-time motor behaviors can be predicted and distinguished, contributing to the development of brain-machine interfaces. The ability to accurately model and predict complex movement patterns has broad implications for advancing technologies in robotics, neuroprosthetics, and artificial intelligence, paving the way for more sophisticated and adaptable systems.

ONIX: a unified open-source platform for multimodal neural recording and perturbation during naturalistic behavior

Behavioral neuroscience faces two conflicting demands: long-duration recordings from large neural populations and unimpeded animal behavior. To meet this challenge we developed ONIX, an open-source data acquisition system with high data throughput (2 GB s-1) and low closed-loop latencies (<1 ms) that uses a 0.3-mm thin tether to minimize behavioral impact. Head position and rotation are tracked in three dimensions and used to drive active commutation without torque measurements. ONIX can acquire data from combinations of passive electrodes, Neuropixels probes, head-mounted microscopes, cameras, three-dimensional trackers and other data sources. We performed uninterrupted, long (~7 h) neural recordings in mice as they traversed complex three-dimensional terrain, and multiday sleep-tracking recordings (~55 h). ONIX enabled exploration with similar mobility as nonimplanted animals, in contrast to conventional tethered systems, which have restricted movement. By combining long recordings with full mobility, our technology will enable progress on questions that require high-quality neural recordings during ethologically grounded behaviors.

Low-cost physiology and behavioral monitor for intravital imaging in small mammals

Significance

Functional brain imaging experiments in awake animals require meticulous monitoring of animal behavior to screen for spontaneous behavioral events. Although these events occur naturally, they can alter cell signaling and hemodynamic activity in the brain and confound functional brain imaging measurements.

Aim

We developed a centralized, user-friendly, and stand-alone platform that includes an animal fixation frame, compact peripheral sensors, and a portable data acquisition system. The affordable, integrated platform can benefit imaging experiments by monitoring animal behavior for motion detection and alertness levels as complementary readouts for brain activity measurements.

Approach

A custom acquisition system was designed using a powerful, inexpensive microcomputer. We customized an accelerometer and miniature camera modules for efficient, real-time monitoring of animal motion detection and pupil diameter. We then tested and validated the platform's performance with optical intrinsic signal imaging and GCaMP fluorescence calcium imaging in functional activation experiments in awake mice.

Results

The integrated platform shows promise for detecting spontaneous motion and pupil dilation while imaging. Stimulus-induced pupil dilation was found to initiate earlier than cortical hemodynamics with a slower rise time. Compared with neuronal calcium response, stimulus-induced pupil dilation initiated later with a slower rise time.

Conclusions

We developed an integrated platform to monitor animal motion and pupil dynamics. The device can be easily coupled and synchronized with optical brain imaging systems to monitor behavior, alertness, and spontaneous motion for awake animal studies.

SARTAB, a scalable system for automated real-time behavior detection based on animal tracking and Region Of Interest analysis: validation on fish courtship behavior

Methods from Machine Learning (ML) and Computer Vision (CV) have proven powerful tools for quickly and accurately analyzing behavioral recordings. The computational complexity of these techniques, however, often precludes applications that require real-time analysis: for example, experiments where a stimulus must be applied in response to a particular behavior or samples must be collected soon after the behavior occurs. Here, we describe SARTAB (Scalable Automated Real-Time Analysis of Behavior), a system that achieves automated real-time behavior detection by continuously monitoring animal positions relative to behaviorally relevant Regions Of Interest (ROIs). We then show how we used this system to detect infrequent courtship behaviors in Pseudotropheus demasoni (a species of Lake Malawi African cichlid fish) to collect neural tissue samples from actively behaving individuals for multiomic profiling at single nucleus resolution. Within this experimental context, we achieve high ROI and animal detection accuracies (mAP@[.5 : .95] of 0.969 and 0.718, respectively) and 100% classification accuracy on a set of 32 manually selected behavioral clips. SARTAB is unique in that all analysis runs on low-cost, edge-deployed hardware, making it a highly scalable and energy-efficient solution for real-time experimental feedback. Although our solution was developed specifically to study cichlid courtship behavior, the intrinsic flexibility of neural network analysis ensures that our approach can be adapted to novel species, behaviors, and environments.

Integrating computer vision and molecular neurobiology to bridge the gap between behavior and the brain

The past decade of social insect research has seen rapid development in automated behavioral tracking and molecular profiling of the nervous system, two distinct but complementary lines of inquiry into phenotypic variation across individuals, colonies, populations, and species. These experimental strategies have developed largely in parallel, as automated tracking generates a continuous stream of behavioral data, while, in contrast, 'omics-based profiling provides a single 'snapshot' of the brain. Better integration of these approaches applied to studying variation in social behavior will reveal the underlying genetic and neurobiological mechanisms that shape the evolution and diversification of social life. In this review, we discuss relevant advances in both fields and propose new strategies to better elucidate the molecular and behavioral innovations that generate social life.

Cortico-thalamic communication for action coordination in a skilled motor sequence

The coordination of forelimb and orofacial movements to compose an ethological reach-to-consume behavior likely involves neural communication across brain regions. Leveraging wide-field imaging and photo-inhibition to survey across the cortex, we identified a cortical network and a high-order motor area (MOs-c), which coordinate action progression in a mouse reach-and-withdraw-to-drink (RWD) behavior. Electrophysiology and photo-inhibition across multiple projection neuron types within the MOs-c revealed differential contributions of pyramidal tract and corticothalamic (CTMOs) output channels to action progression and hand-mouth coordination. Notably, CTMOs display sustained firing throughout RWD sequence and selectively enhance RWD-relevant activity in postsynaptic thalamus neurons, which also contribute to action coordination. CTMOs receive converging monosynaptic inputs from forelimb and orofacial sensorimotor areas and are reciprocally connected to thalamic neurons, which project back to the cortical network. Therefore, motor cortex corticothalamic channel may selectively amplify the thalamic integration of cortical and subcortical sensorimotor streams to coordinate a skilled motor sequence.

Temporal dynamics of nucleus accumbens neurons in male mice during reward seeking

The nucleus accumbens (NAc) regulates reward-motivated behavior, but the temporal dynamics of NAc neurons that enable "free-willed" animals to obtain rewards remain elusive. Here, we recorded Ca2+ activity from individual NAc neurons when mice performed self-paced lever-presses for sucrose. NAc neurons exhibited three temporally-sequenced clusters, defined by times at which they exhibited increased Ca2+ activity: approximately 0, -2.5 or -5 sec relative to the lever-pressing. Dopamine D1 receptor (D1)-expressing neurons and D2-neurons formed the majority of the -5-sec versus -2.5-sec clusters, respectively, while both neuronal subtypes were represented in the 0-sec cluster. We found that pre-press activity patterns of D1- or D2-neurons could predict subsequent lever-presses. Inhibiting D1-neurons at -5 sec or D2-neurons at -2.5 sec, but not at other timepoints, reduced sucrose-motivated lever-pressing. We propose that the time-specific activity of D1- and D2-neurons mediate key temporal features of the NAc through which reward motivation initiates reward-seeking behavior.

GoFish: a foray into open-source, aquatic behavioral automation

As the most species-rich vertebrate group, fish provide an array of opportunities to investigate the link between ecological interactions and the evolution of behavior and cognition, yet, as an animal model, they are relatively underutilized in studies of comparative cognition. To address this gap, we developed a fully automated platform for behavioral experiments in aquatic species, GoFish. GoFish includes closed-loop control of task contingencies using real-time video tracking, presentation of visual stimuli, automatic food reward dispensers, and built-in data acquisition. The hardware is relatively inexpensive and accessible, and all software components of the platform are open-source. GoFish facilitates experimental automation, allowing for customization of high-throughput protocols and the efficient acquisition of rich behavioral data. We hope this platform proves to be a useful tool for the research community, facilitating refined, reproducible behavioral experiments on aquatic species in comparative cognition, behavioral ecology, and neuroscience.

Single unit electrophysiology recordings and computational modeling can predict octopus arm movement

The octopus simplified nervous system holds the potential to reveal principles of motor circuits and improve brain-machine interface devices through computational modeling with machine learning and statistical analysis. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100ms after stimulation were predictive of the resultant movement response. Computational models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. Deep learning models and unsupervised dimension reduction identified a consistent set of features that could be used to distinguish different types of arm movements. These models generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit.

Using DeepLabCut-Live to probe state dependent neural circuits of behavior with closed-loop optogenetic stimulation

Background

Closed-loop behavior paradigms enable us to dissect the state-dependent neural circuits underlying behavior in real-time. However, studying context-dependent locomotor perturbations has been challenging due to limitations in molecular tools and techniques for real-time manipulation of spinal cord circuits.

New Method

We developed a novel closed-loop optogenetic stimulation paradigm that utilizes DeepLabCut-Live pose estimation to manipulate primary sensory afferent activity at specific phases of the locomotor cycle in mice. A compact DeepLabCut model was trained to track hindlimb kinematics in real-time and integrated into the Bonsai visual programming framework. This allowed an LED to be triggered to photo-stimulate sensory neurons expressing channelrhodopsin at user-defined pose-based criteria, such as during the stance or swing phase.

Results

Optogenetic activation of nociceptive TRPV1+ sensory neurons during treadmill locomotion reliably evoked paw withdrawal responses. Photoactivation during stance generated a brief withdrawal, while stimulation during swing elicited a prolonged response likely engaging stumbling corrective reflexes.

Comparison with Existing Methods

This new method allows for high spatiotemporal precision in manipulating spinal circuits based on the phase of the locomotor cycle. Unlike previous approaches, this closed-loop system can control for the state-dependent nature of sensorimotor responses during locomotion.

Conclusions

Integrating DeepLabCut-Live with optogenetics provides a powerful new approach to dissect the context-dependent role of sensory feedback and spinal interneurons in modulating locomotion. This technique opens new avenues for uncovering the neural substrates of state-dependent behaviors and has broad applicability for studies of real-time closed-loop manipulation based on pose estimation.

MiniXL: An open-source, large field-of-view epifluorescence miniature microscope for mice capable of single-cell resolution and multi-brain region imaging

Capturing the intricate dynamics of neural activity in freely behaving animals is essential for understanding the neural mechanisms underpinning specific behaviors. Miniaturized microscopy enables investigators to track population activity at cellular level, but the field of view (FOV) of these microscopes have been limited and does not allow multiple-brain region imaging. To fill this technological gap, we have developed the eXtra Large field-of-view Miniscope (MiniXL), a 3.5g lightweight miniaturized microscope with an FOV measuring 3.5 mm in diameter and an electrically adjustable working distance of 1.9 mm ± 200 μm. We demonstrated the capability of MiniXL recording the activity of large neuronal population in both subcortical area (hippocampal dorsal CA1) and deep brain regions (medial prefrontal cortex, mPFC and nucleus accumbens, NAc). The large FOV allows simultaneous imaging of multiple brain regions such as bilateral mPFCs or mPFC and NAc during complex social behavior and tracking cells across multiple sessions. As with all microscopes in the UCLA Miniscope ecosystem, the MiniXL is fully open-source and will be shared with the neuroscience community to lower the barriers for adoption of this technology.

SuperAnimal pretrained pose estimation models for behavioral analysis

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, reliable inference of poses currently requires domain knowledge and manual labeling effort to build supervised models. We present SuperAnimal, a method to develop unified foundation models that can be used on over 45 species, without additional manual labels. These models show excellent performance across six pose estimation benchmarks. We demonstrate how to fine-tune the models (if needed) on differently labeled data and provide tooling for unsupervised video adaptation to boost performance and decrease jitter across frames. If fine-tuned, SuperAnimal models are 10-100× more data efficient than prior transfer-learning-based approaches. We illustrate the utility of our models in behavioral classification and kinematic analysis. Collectively, we present a data-efficient solution for animal pose estimation.

Toward a neuroscience of natural behavior

One of the most exciting new developments in systems neuroscience is the progress being made toward neurophysiological experiments that move beyond simplified laboratory settings and address the richness of natural behavior. This is enabled by technological advances such as wireless recording in freely moving animals, automated quantification of behavior, and new methods for analyzing large data sets. Beyond new empirical methods and data, however, there is also a need for new theories and concepts to interpret that data. Such theories need to address the particular challenges of natural behavior, which often differ significantly from the scenarios studied in traditional laboratory settings. Here, we discuss some strategies for developing such novel theories and concepts and some example hypotheses being proposed.

A cortical locus for modulation of arousal states

Fluctuations in global arousal are key determinants of spontaneous cortical activity and function. Several subcortical structures, including neuromodulator nuclei like the locus coeruleus (LC), are involved in the regulation of arousal. However, much less is known about the role of cortical circuits that provide top-down inputs to arousal-related subcortical structures. Here, we investigated the role of a major subdivision of the prefrontal cortex, the anterior cingulate cortex (ACC), in arousal modulation. Pupil size, facial movements, heart rate, and locomotion were used as non-invasive measures of arousal and behavioral state. We designed a closed loop optogenetic system based on machine vision and found that real time inhibition of ACC activity during pupil dilations suppresses ongoing arousal events. In contrast, inhibiting activity in a control cortical region had no effect on arousal. Fiber photometry recordings showed that ACC activity scales with the magnitude of spontaneously occurring pupil dilations/face movements independently of locomotion. Moreover, optogenetic ACC activation increases arousal independently of locomotion. In addition to modulating global arousal, ACC responses to salient sensory stimuli scaled with the size of evoked pupil dilations. Consistent with a role in sustaining saliency-linked arousal events, pupil responses to sensory stimuli were suppressed with ACC inactivation. Finally, our results comparing arousal-related ACC and norepinephrinergic LC neuron activity support a role for the LC in initiation of arousal events which are modulated in real time by the ACC. Collectively, our experiments identify the ACC as a key cortical site for sustaining momentary increases in arousal and provide the foundation for understanding cortical-subcortical dynamics underlying the modulation of arousal states.

A-SOiD, an active-learning platform for expert-guided, data-efficient discovery of behavior

To identify and extract naturalistic behavior, two methods have become popular: supervised and unsupervised. Each approach carries its own strengths and weaknesses (for example, user bias, training cost, complexity and action discovery), which the user must consider in their decision. Here, an active-learning platform, A-SOiD, blends these strengths, and in doing so, overcomes several of their inherent drawbacks. A-SOiD iteratively learns user-defined groups with a fraction of the usual training data, while attaining expansive classification through directed unsupervised classification. In socially interacting mice, A-SOiD outperformed standard methods despite requiring 85% less training data. Additionally, it isolated ethologically distinct mouse interactions via unsupervised classification. We observed similar performance and efficiency using nonhuman primate and human three-dimensional pose data. In both cases, the transparency in A-SOiD's cluster definitions revealed the defining features of the supervised classification through a game-theoretic approach. To facilitate use, A-SOiD comes as an intuitive, open-source interface for efficient segmentation of user-defined behaviors and discovered sub-actions.

BioLuminescent OptoGenetics in the choroid plexus: integrated opto- and chemogenetic control in vivo

Significance

The choroid plexus (ChP) epithelial network displays diverse dynamics, including propagating calcium waves and individuated fluctuations in single cells. These rapid events underscore the possibility that ChP dynamics may reflect behaviorally relevant and clinically important changes in information processing and signaling. Optogenetic and chemogenetic tools provide spatiotemporally precise and sustained approaches for testing such dynamics in vivo. Here, we describe the feasibility of a novel combined opto- and chemogenetic tool, BioLuminescent-OptoGenetics (BL-OG), for the ChP in vivo. In the "LuMinOpsin" (LMO) BL-OG strategy, a luciferase is tethered to an adjacent optogenetic element. This molecule allows chemogenetic activation when the opsin is driven by light produced through luciferase binding a small molecule (luciferin) or by conventional optogenetic light sources and BL-OG report of activation through light production.

Aim

To test the viability of BL-OG/LMO for ChP control.

Approach

Using transgenic and Cre-directed targeting to the ChP, we expressed LMO3 (a Gaussia luciferase-VChR1 fusion), a highly effective construct in neural systems. In mice expressing LMO3 in ChP, we directly imaged BL light production following multiple routes of coelenterazine (CTZ: luciferin) administration using an implanted cannula system. We also used home-cage videography with Deep LabCut analysis to test for any impact of repeated CTZ administration on basic health and behavioral indices.

Results

Multiple routes of CTZ administration drove BL photon production, including intracerebroventricular, intravenous, and intraperitoneal injection. Intravenous administration resulted in fast "flash" kinetics that diminished in seconds to minutes, and intraperitoneal administration resulted in slow rising activity that sustained hours. Mice showed no consistent impact of 1 week of intraperitoneal CTZ administration on weight, drinking, motor behavior, or sleep/wake cycles.

Conclusions

BL-OG/LMO provides unique advantages for testing the role of ChP dynamics in biological processes.

Automated procedure to detect subtle motor alterations in the balance beam test in a mouse model of early Parkinson's disease

Parkinson's disease (PD) is the most common motor neurodegenerative disorder, characterised by aggregated α-synuclein (α-syn) constituting Lewy bodies. We aimed to investigate temporal changes in motor impairments in a PD mouse model induced by overexpression of α-syn with the conventional manual analysis of the balance beam test and a novel approach using machine learning algorithms to automate behavioural analysis. We combined automated animal tracking using markerless pose estimation in DeepLabCut, with automated behavioural classification in Simple Behavior Analysis. Our automated procedure was able to detect subtle motor deficits in mouse performances in the balance beam test that the manual analysis approach could not assess. The automated model revealed time-course significant differences for the "walking" behaviour in the mean interval between each behavioural bout, the median event bout duration and the classifier probability of occurrence in male PD mice, even though no statistically significant loss of tyrosine hydroxylase in the nigrostriatal system was found in either sex. These findings are valuable for early detection of motor impairment in early PD animal models. We provide a user-friendly, step-by-step guide for automated assessment of mouse performances in the balance beam test, which aims to be replicable without any significant computational and programming knowledge.

Facemap: a framework for modeling neural activity based on orofacial tracking

Recent studies in mice have shown that orofacial behaviors drive a large fraction of neural activity across the brain. To understand the nature and function of these signals, we need better computational models to characterize the behaviors and relate them to neural activity. Here we developed Facemap, a framework consisting of a keypoint tracker and a deep neural network encoder for predicting neural activity. Our algorithm for tracking mouse orofacial behaviors was more accurate than existing pose estimation tools, while the processing speed was several times faster, making it a powerful tool for real-time experimental interventions. The Facemap tracker was easy to adapt to data from new labs, requiring as few as 10 annotated frames for near-optimal performance. We used the keypoints as inputs to a deep neural network which predicts the activity of ~50,000 simultaneously-recorded neurons and, in visual cortex, we doubled the amount of explained variance compared to previous methods. Using this model, we found that the neuronal activity clusters that were well predicted from behavior were more spatially spread out across cortex. We also found that the deep behavioral features from the model had stereotypical, sequential dynamics that were not reversible in time. In summary, Facemap provides a stepping stone toward understanding the function of the brain-wide neural signals and their relation to behavior.

Pose analysis in free-swimming adult zebrafish, Danio rerio : "fishy" origins of movement design

Movement requires maneuvers that generate thrust to either make turns or move the body forward in physical space. The computational space for perpetually controlling the relative position of every point on the body surface can be vast. We hypothesize the evolution of efficient design for movement that minimizes active (neural) control by leveraging the passive (reactive) forces between the body and the surrounding medium at play. To test our hypothesis, we investigate the presence of stereotypical postures during free-swimming in adult zebrafish, Danio rerio . We perform markerless tracking using DeepLabCut, a deep learning pose estimation toolkit, to track geometric relationships between body parts. To identify putative clusters of postural configurations obtained from twelve freely behaving zebrafish, we use unsupervised multivariate time-series analysis (B-SOiD machine learning software). When applied to single individuals, this method reveals a best-fit for 36 to 50 clusters in contrast 86 clusters for data pooled from all 12 animals. The centroids of each cluster obtained over 14,000 sequential frames recorded for a single fish represent an apriori classification into relatively stable "target body postures" and inter-pose "transitional postures" that lead to and away from a target pose. We use multidimensional scaling of mean parameter values for each cluster to map cluster-centroids within two dimensions of postural space. From a post-priori visual analysis, we condense neighboring postural variants into 15 superclusters or core body configurations. We develop a nomenclature specifying the anteroposterior level/s (upper, mid and lower) and degree of bending. Our results suggest that constraining bends to mainly three levels in adult zebrafish preempts the neck, fore- and hindlimb design for maneuverability in land vertebrates.

Reproducible and fully automated testing of nocifensive behavior in mice

Pain in rodents is often inferred from their withdrawal from noxious stimulation. Threshold stimulus intensity or response latency is used to quantify pain sensitivity. This usually involves applying stimuli by hand and measuring responses by eye, which limits reproducibility and throughput. We describe a device that standardizes and automates pain testing by providing computer-controlled aiming, stimulation, and response measurement. Optogenetic and thermal stimuli are applied using blue and infrared light, respectively. Precise mechanical stimulation is also demonstrated. Reflectance of red light is used to measure paw withdrawal with millisecond precision. We show that consistent stimulus delivery is crucial for resolving stimulus-dependent variations in withdrawal and for testing with sustained stimuli. Moreover, substage video reveals "spontaneous" behaviors for consideration alongside withdrawal metrics to better assess the pain experience. The entire process was automated using machine learning. RAMalgo (reproducible automated multimodal algometry) improves the standardization, comprehensiveness, and throughput of preclinical pain testing.

Protocol to investigate the neural basis for copulation posture of Drosophila using a closed-loop real-time optogenetic system

In internal fertilization animals, maintaining a copulation posture facilitates the process of transporting gametes from male to female. Here, we present a protocol to investigate the neural basis for copulation posture of fruit flies using a closed-loop real-time optogenetic system. We describe steps for using deep learning analysis to enable optogenetic manipulation of neural activity only during copulation with high efficiency. This system can be applied to various animal behaviors other than copulation. For complete details on the use and execution of this protocol, please refer to Yamanouchi et al. (2023).1.

Protocol for the integration of fiber photometry and social behavior in rodent models

Fiber photometry offers insight into cell-type-specific activity underlying social interactions. We provide a protocol for the integration of fiber photometry recordings into the analysis of social behavior in rodent models. This includes considerations during surgery, notes on synchronizing fiber photometry with behavioral recordings, advice on using multi-animal behavioral tracking software, and scripts for the analysis of fiber photometry recordings. For complete details on the use and execution of this protocol, please refer to Dawson et al. (2023).1.

Neurons in the primary visual cortex of freely moving rats encode both sensory and non-sensory task variables

Neurons in primary visual cortex (area V1) are strongly driven by both sensory stimuli and non-sensory events. However, although the representation of sensory stimuli has been well characterized, much less is known about the representation of non-sensory events. Here, we characterize the specificity and organization of non-sensory representations in rat V1 during a freely moving visual decision task. We find that single neurons encode diverse combinations of task features simultaneously and across task epochs. Despite heterogeneity at the level of single neuron response patterns, both visual and nonvisual task variables could be reliably decoded from small neural populations (5 to 40 units) throughout a trial. Interestingly, in animals trained to make an auditory decision following passive observation of a visual stimulus, some but not all task features could also be decoded from V1 activity. Our results support the view that even in V1-the earliest stage of the cortical hierarchy-bottom-up sensory information may be combined with top-down non-sensory information in a task-dependent manner.

Three-dimensional surface motion capture of multiple freely moving pigs using MAMMAL

Understandings of the three-dimensional social behaviors of freely moving large-size mammals are valuable for both agriculture and life science, yet challenging due to occlusions in close interactions. Although existing animal pose estimation methods captured keypoint trajectories, they ignored deformable surfaces which contained geometric information essential for social interaction prediction and for dealing with the occlusions. In this study, we develop a Multi-Animal Mesh Model Alignment (MAMMAL) system based on an articulated surface mesh model. Our self-designed MAMMAL algorithms automatically enable us to align multi-view images into our mesh model and to capture 3D surface motions of multiple animals, which display better performance upon severe occlusions compared to traditional triangulation and allow complex social analysis. By utilizing MAMMAL, we are able to quantitatively analyze the locomotion, postures, animal-scene interactions, social interactions, as well as detailed tail motions of pigs. Furthermore, experiments on mouse and Beagle dogs demonstrate the generalizability of MAMMAL across different environments and mammal species.

A unified open-source platform for multimodal neural recording and perturbation during naturalistic behavior

Behavioral neuroscience faces two conflicting demands: long-duration recordings from large neural populations and unimpeded animal behavior. To meet this challenge, we developed ONIX, an open-source data acquisition system with high data throughput (2GB/sec) and low closed-loop latencies (<1ms) that uses a novel 0.3 mm thin tether to minimize behavioral impact. Head position and rotation are tracked in 3D and used to drive active commutation without torque measurements. ONIX can acquire from combinations of passive electrodes, Neuropixels probes, head-mounted microscopes, cameras, 3D-trackers, and other data sources. We used ONIX to perform uninterrupted, long (~7 hours) neural recordings in mice as they traversed complex 3-dimensional terrain. ONIX allowed exploration with similar mobility as non-implanted animals, in contrast to conventional tethered systems which restricted movement. By combining long recordings with full mobility, our technology will enable new progress on questions that require high-quality neural recordings during ethologically grounded behaviors.

Somatostatin peptide signaling dampens cortical circuits and promotes exploratory behavior

We sought to characterize the unique role of somatostatin (SST) in the prelimbic (PL) cortex in mice. We performed slice electrophysiology in pyramidal and GABAergic neurons to characterize the pharmacological mechanism of SST signaling and fiber photometry of GCaMP6f fluorescent calcium signals from SST neurons to characterize the activity profile of SST neurons during exploration of an elevated plus maze (EPM) and open field test (OFT). We used local delivery of a broad SST receptor (SSTR) agonist and antagonist to test causal effects of SST signaling. SSTR activation hyperpolarizes layer 2/3 pyramidal neurons, an effect that is recapitulated with optogenetic stimulation of SST neurons. SST neurons in PL are activated during EPM and OFT exploration, and SSTR agonist administration directly into the PL enhances open arm exploration in the EPM. This work describes a broad ability for SST peptide signaling to modulate microcircuits within the prefrontal cortex and related exploratory behaviors.

Open-MAC: A low-cost open-source motorized commutator for electro- and opto-physiological recordings in freely moving rodents

In vivo electro- and optophysiology experiments in rodents reveal the neural mechanisms underlying behavior and brain disorders but mostly involve a cable connection between an implant in the animal and an external recording device. Standard tethers with thin cables or non-motorized commutators require constant monitoring and often manual interference to untwist the cable. Motorized commutators offer a solution, but those few that are commercially available are expensive and often not adapted to widely used connector standards of the open-source community like 12-channel SPI. Here we introduce an open-source motorized all-in-one commutator (Open-MAC): a low-cost (240-390 EUR), low-torque motorized commutator that can operate with minimal audible noise in a torque-based mode relying on dual magnetic Hall sensors. It further includes electronics to operate in a torque-free, online pose-estimation-based mode, with future developments. Operation is controlled by an onboard microcontroller (XIAO SAMD21) powered by a USB-C cable or DC power supply. The body and movable parts are 3D-printed. Different Open-MAC versions can support electrophysiology with up to 64 recording channels using the Open-Ephys / Intan^TM recording systems as well as miniature endoscope (miniscope) recordings using the UCLA Miniscope v3/4, and can host a fibre for optogenetic modulation.

Piezo-mediated mechanosensation contributes to stabilizing copulation posture and reproductive success in Drosophila males

In internal fertilization animals, reproductive success depends on maintaining copulation until gametes are transported from male to female. In Drosophila melanogaster, mechanosensation in males likely contributes to copulation maintenance, but its molecular underpinning remains to be identified. Here we show that the mechanosensory gene piezo and its' expressing neurons are responsible for copulation maintenance. An RNA-seq database search and subsequent mutant analysis revealed the importance of piezo for maintaining male copulation posture. piezo-GAL4-positive signals were found in the sensory neurons of male genitalia bristles, and optogenetic inhibition of piezo-expressing neurons in the posterior side of the male body during copulation destabilized posture and terminated copulation. Our findings suggest that the mechanosensory system of male genitalia through Piezo channels plays a key role in copulation maintenance and indicate that Piezo may increase male fitness during copulation in flies.

Miniscope-LFOV: A large-field-of-view, single-cell-resolution, miniature microscope for wired and wire-free imaging of neural dynamics in freely behaving animals

Imaging large-population, single-cell fluorescent dynamics in freely behaving animals larger than mice remains a key endeavor of neuroscience. We present a large-field-of-view open-source miniature microscope (MiniLFOV) designed for large-scale (3.6 mm × 2.7 mm), cellular resolution neural imaging in freely behaving rats. It has an electrically adjustable working distance of up to 3.5 mm ± 100 μm, incorporates an absolute head orientation sensor, and weighs only 13.9 g. The MiniLFOV is capable of both deep brain and cortical imaging and has been validated in freely behaving rats by simultaneously imaging >1000 GCaMP7s-expressing neurons in the hippocampal CA1 layer and in head-fixed mice by simultaneously imaging ~2000 neurons in the dorsal cortex through a cranial window. The MiniLFOV also supports optional wire-free operation using a novel, wire-free data acquisition expansion board. We expect that this new open-source implementation of the UCLA Miniscope platform will enable researchers to address novel hypotheses concerning brain function in freely behaving animals.

Video-driven simulation of lower limb mechanical loading during aquatic exercises

Understanding the mechanical demands of an exercise on the musculoskeletal system is crucial to prescribe effective training or therapeutic interventions. Yet, that knowledge is currently limited in water, mostly because of the difficulty in evaluating external resistance. Here I reconcile recent advances in 3D markerless pose and mesh estimation, biomechanical simulations, and hydrodynamic modeling, to predict lower limb mechanical loading during aquatic exercises. Simulations are driven exclusively from a single video. Fluid forces were estimated within 12.5±4.1% of the peak forces determined through computational fluid dynamics analyses, at a speed three orders of magnitude greater. In silico hip and knee resultant joint forces agreed reasonably well with in vivo instrumented implant recordings (R²=0.74) downloaded from the OrthoLoad database, both in magnitude (RMSE =251±125 N) and direction (cosine similarity = 0.92±0.09). Hip flexors, glutes, adductors, and hamstrings were the main contributors to hip joint compressive forces (40.4±12.7%, 25.6±9.7%, 14.2±4.8%, 13.0±8.2%, respectively), while knee compressive forces were mostly produced by the gastrocnemius (39.1±15.9%) and vasti (29.4±13.7%). Unlike dry-land locomotion, non-hip- and non-knee-spanning muscles provided little to no offloading effect via dynamic coupling. This noninvasive method has the potential to standardize the reporting of exercise intensity, inform the design of rehabilitation protocols and improve their reproducibility.

Open-source tools for behavioral video analysis: Setup, methods, and best practices

Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional 'center of mass' tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording. We also discuss best practices for developing and using video analysis methods, including community-wide standards and critical needs for the open sharing of datasets and code, more widespread comparisons of video analysis methods, and better documentation for these methods especially for new users. We encourage broader adoption and continued development of these tools, which have tremendous potential for accelerating scientific progress in understanding the brain and behavior.

Cre-dependent ACR2-expressing reporter mouse strain for efficient long-lasting inhibition of neuronal activity

Optogenetics is a powerful tool for manipulating neuronal activity by light illumination with high temporal and spatial resolution. Anion-channelrhodopsins (ACRs) are light-gated anion channels that allow researchers to efficiently inhibit neuronal activity. A blue light-sensitive ACR2 has recently been used in several in vivo studies; however, the reporter mouse strain expressing ACR2 has not yet been reported. Here, we generated a new reporter mouse strain, LSL-ACR2, in which ACR2 is expressed under the control of Cre recombinase. We crossed this strain with a noradrenergic neuron-specific driver mouse (NAT-Cre) to generate NAT-ACR2 mice. We confirmed Cre-dependent expression and function of ACR2 in the targeted neurons by immunohistochemistry and electrophysiological recordings in vitro, and confirmed physiological function using an in vivo behavioral experiment. Our results show that the LSL-ACR2 mouse strain can be applied for optogenetic inhibition of targeted neurons, particularly for long-lasting continuous inhibition, upon crossing with Cre-driver mouse strains. The LSL-ACR2 strain can be used to prepare transgenic mice with homogenous expression of ACR2 in targeted neurons with a high penetration ratio, good reproducibility, and no tissue invasion.

Multiscale imaging informs translational mouse modeling of neurological disease

Multiscale neurophysiology reveals that simple motor actions are associated with changes in neuronal firing in virtually every brain region studied. Accordingly, the assessment of focal pathology such as stroke or progressive neurodegenerative diseases must also extend widely across brain areas. To derive mechanistic information through imaging, multiple resolution scales and multimodal factors must be included, such as the structure and function of specific neurons and glial cells and the dynamics of specific neurotransmitters. Emerging multiscale methods in preclinical animal studies that span micro- to macroscale examinations fill this gap, allowing a circuit-based understanding of pathophysiological mechanisms. Combined with high-performance computation and open-source data repositories, these emerging multiscale and large field-of-view techniques include live functional ultrasound, multi- and single-photon wide-scale light microscopy, video-based miniscopes, and tissue-penetrating fiber photometry, as well as variants of post-mortem expansion microscopy. We present these technologies and outline use cases and data pipelines to uncover new knowledge within animal models of stroke, Alzheimer's disease, and movement disorders.

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.

Automatic extraction of upper-limb kinematic activity using deep learning-based markerless tracking during deep brain stimulation implantation for Parkinson's disease: A proof of concept study

Optimal placement of deep brain stimulation (DBS) therapy for treating movement disorders routinely relies on intraoperative motor testing for target determination. However, in current practice, motor testing relies on subjective interpretation and correlation of motor and neural information. Recent advances in computer vision could improve assessment accuracy. We describe our application of deep learning-based computer vision to conduct markerless tracking for measuring motor behaviors of patients undergoing DBS surgery for the treatment of Parkinson's disease. Video recordings were acquired during intraoperative kinematic testing (N = 5 patients), as part of standard of care for accurate implantation of the DBS electrode. Kinematic data were extracted from videos post-hoc using the Python-based computer vision suite DeepLabCut. Both manual and automated (80.00% accuracy) approaches were used to extract kinematic episodes from threshold derived kinematic fluctuations. Active motor epochs were compressed by modeling upper limb deflections with a parabolic fit. A semi-supervised classification model, support vector machine (SVM), trained on the parameters defined by the parabolic fit reliably predicted movement type. Across all cases, tracking was well calibrated (i.e., reprojection pixel errors 0.016-0.041; accuracies >95%). SVM predicted classification demonstrated high accuracy (85.70%) including for two common upper limb movements, arm chain pulls (92.30%) and hand clenches (76.20%), with accuracy validated using a leave-one-out process for each patient. These results demonstrate successful capture and categorization of motor behaviors critical for assessing the optimal brain target for DBS surgery. Conventional motor testing procedures have proven informative and contributory to targeting but have largely remained subjective and inaccessible to non-Western and rural DBS centers with limited resources. This approach could automate the process and improve accuracy for neuro-motor mapping, to improve surgical targeting, optimize DBS therapy, provide accessible avenues for neuro-motor mapping and DBS implantation, and advance our understanding of the function of different brain areas.

Laser stimulation of the skin for quantitative study of decision-making and motivation

Neuroeconomics studies how decision-making is guided by the value of rewards and punishments. But to date, little is known about how noxious experiences impact decisions. A challenge is the lack of an aversive stimulus that is dynamically adjustable in intensity and location, readily usable over many trials in a single experimental session, and compatible with multiple ways to measure neuronal activity. We show that skin laser stimulation used in human studies of aversion can be used for this purpose in several key animal models. We then use laser stimulation to study how neurons in the orbitofrontal cortex (OFC), an area whose many roles include guiding decisions among different rewards, encode the value of rewards and punishments. We show that some OFC neurons integrated the positive value of rewards with the negative value of aversive laser stimulation, suggesting that the OFC can play a role in more complex choices than previously appreciated.

Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis

A key aspect of neuroscience research is the development of powerful, general-purpose data analyses that process large datasets. Unfortunately, modern data analyses have a hidden dependence upon complex computing infrastructure (e.g., software and hardware), which acts as an unaddressed deterrent to analysis users. Although existing analyses are increasingly shared as open-source software, the infrastructure and knowledge needed to deploy these analyses efficiently still pose significant barriers to use. In this work, we develop Neuroscience Cloud Analysis As a Service (NeuroCAAS): a fully automated open-source analysis platform offering automatic infrastructure reproducibility for any data analysis. We show how NeuroCAAS supports the design of simpler, more powerful data analyses and that many popular data analysis tools offered through NeuroCAAS outperform counterparts on typical infrastructure. Pairing rigorous infrastructure management with cloud resources, NeuroCAAS dramatically accelerates the dissemination and use of new data analyses for neuroscientific discovery.

Pi USB Cam: A Simple and Affordable DIY Solution That Enables High-Quality, High-Throughput Video Capture for Behavioral Neuroscience Research

Video recording is essential for behavioral neuroscience research, but the majority of available systems suffer from poor cost-to-functionality ratio. Commercial options frequently come at high financial cost that prohibits scalability and throughput, whereas DIY solutions often require significant expertise and time investment unaffordable to many researchers. To address this, we combined a low-cost Raspberry Pi microcomputer, DIY electronics peripherals, freely available open-source firmware, and custom 3D-printed casings to create Pi USB Cam, a simple yet powerful and highly versatile video recording solution. Pi USB Cam is constructed using affordable and widely available components and requires no expertise to build and implement. The result is a system that functions as a plug-and-play USB camera that can be easily installed in various animal testing and housing sites and is readily compatible with popular behavioral and neural recording software. Here, we provide a comprehensive parts list and step-by-step instructions for users to build and implement their own Pi USB Cam system. In a series of benchmark comparisons, Pi USB Cam was able to capture ultra-wide fields of view of behaving rats given limited object distance and produced high image quality while maintaining consistent frame rates even under low-light and no-light conditions relative to a standard, commercially available USB camera. Video recordings were easily scaled using free, open-source software. Altogether, Pi USB Cam presents an elegant yet simple solution for behavioral neuroscientists seeking an affordable and highly flexible system to enable quality video recordings.

A one-photon endoscope for simultaneous patterned optogenetic stimulation and calcium imaging in freely behaving mice

Optogenetics and calcium imaging can be combined to simultaneously stimulate and record neural activity in vivo. However, this usually requires two-photon microscopes, which are not portable nor affordable. Here we report the design and implementation of a miniaturized one-photon endoscope for performing simultaneous optogenetic stimulation and calcium imaging. By integrating digital micromirrors, the endoscope makes it possible to activate any neuron of choice within the field of view, and to apply arbitrary spatiotemporal patterns of photostimulation while imaging calcium activity. We used the endoscope to image striatal neurons from either the direct pathway or the indirect pathway in freely moving mice while activating any chosen neuron in the field of view. The endoscope also allows for the selection of neurons based on their relationship with specific animal behaviour, and to recreate the behaviour by mimicking the natural neural activity with photostimulation. The miniaturized endoscope may facilitate the study of how neural activity gives rise to behaviour in freely moving animals.

Blueprints for measuring natural behavior

Until recently laboratory tasks for studying behavior were highly artificial, simplified, and designed without consideration for the environmental or social context. Although such an approach offers good control over behavior, it does not allow for researching either voluntary responses or individual differences. Importantly for neuroscience studies, the activity of the neural circuits involved in producing unnatural, artificial behavior is variable and hard to predict. In addition, different ensembles may be activated depending on the strategy the animal adopts to deal with the spurious problem. Thus, artificial and simplified tasks based on responses, which do not occur spontaneously entail problems with modeling behavioral impairments and underlying brain deficits. To develop valid models of human disorders we need to test spontaneous behaviors consistently engaging well-defined, evolutionarily conserved neuronal circuits. Such research focuses on behavioral patterns relevant for surviving and thriving under varying environmental conditions, which also enable high reproducibility across different testing settings.

Multimodal cues displayed by submissive rats promote prosocial choices by dominants

Animals often display prosocial behaviors, performing actions that benefit others. Although prosociality is essential for social bonding and cooperation, we still know little about how animals integrate behavioral cues from those in need to make decisions that increase their well-being. To address this question, we used a two-choice task where rats can provide rewards to a conspecific in the absence of self-benefit and investigated which conditions promote prosociality by manipulating the social context of the interacting animals. Although sex or degree of familiarity did not affect prosocial choices in rats, social hierarchy revealed to be a potent modulator, with dominant decision-makers showing faster emergence and higher levels of prosocial choices toward their submissive cage mates. Leveraging quantitative analysis of multimodal social dynamics prior to choice, we identified that pairs with dominant decision-makers exhibited more proximal interactions. Interestingly, these closer interactions were driven by submissive animals that modulated their position and movement following their dominants and whose 50-kHz vocalization rate correlated with dominants' prosociality. Moreover, Granger causality revealed stronger bidirectional influences in pairs with dominant focals and submissive recipients, indicating increased behavioral coordination. Finally, multivariate analysis highlighted body language as the main information dominants use on a trial-by-trial basis to learn that their actions have effects on others. Our results provide a refined understanding of the behavioral dynamics that rats use for action-selection upon perception of socially relevant cues and navigate social decision-making.

AI-enabled, implantable, multichannel wireless telemetry for photodynamic therapy

Photodynamic therapy (PDT) offers several advantages for treating cancers, but its efficacy is highly dependent on light delivery to activate a photosensitizer. Advances in wireless technologies enable remote delivery of light to tumors, but suffer from key limitations, including low levels of tissue penetration and photosensitizer activation. Here, we introduce DeepLabCut (DLC)-informed low-power wireless telemetry with an integrated thermal/light simulation platform that overcomes the above constraints. The simulator produces an optimized combination of wavelengths and light sources, and DLC-assisted wireless telemetry uses the parameters from the simulator to enable adequate illumination of tumors through high-throughput (<20 mice) and multi-wavelength operation. Together, they establish a range of guidelines for effective PDT regimen design. In vivo Hypericin and Foscan mediated PDT, using cancer xenograft models, demonstrates substantial suppression of tumor growth, warranting further investigation in research and/or clinical settings.

Measuring Locomotor Activity and Behavioral Aspects of Rodents Living in the Home-Cage

Automatization and technological advances have led to a larger number of methods and systems to monitor and measure locomotor activity and more specific behavior of a wide variety of animal species in various environmental conditions in laboratory settings. In rodents, the majority of these systems require the animals to be temporarily taken away from their home-cage into separate observation cage environments which requires manual handling and consequently evokes distress for the animal and may alter behavioral responses. An automated high-throughput approach can overcome this problem. Therefore, this review describes existing automated methods and technologies which enable the measurement of locomotor activity and behavioral aspects of rodents in their most meaningful and stress-free laboratory environment: the home-cage. In line with the Directive 2010/63/EU and the 3R principles (replacement, reduction, refinement), this review furthermore assesses their suitability and potential for group-housed conditions as a refinement strategy, highlighting their current technological and practical limitations. It covers electrical capacitance technology and radio-frequency identification (RFID), which focus mainly on voluntary locomotor activity in both single and multiple rodents, respectively. Infrared beams and force plates expand the detection beyond locomotor activity toward basic behavioral traits but discover their full potential in individually housed rodents only. Despite the great premises of these approaches in terms of behavioral pattern recognition, more sophisticated methods, such as (RFID-assisted) video tracking technology need to be applied to enable the automated analysis of advanced behavioral aspects of individual animals in social housing conditions.