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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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Dopamine and norepinephrine differentially mediate the exploration-exploitation tradeoff

The catecholamines dopamine (DA) and norepinephrine (NE) have been implicated in neuropsychiatric vulnerability, in part via their roles in mediating the decision making processes. Although the two neuromodulators share a synthesis pathway and are co-activated, they engage in distinct circuits and roles in modulating neural activity across the brain. However, in the computational neuroscience literature, they have been assigned similar roles in modulating the exploration-exploitation tradeoff. Revealing how each neuromodulator contributes to this explore-exploit process is important in guiding mechanistic hypotheses emerging from computational psychiatric approaches. To understand the differences and overlaps of the roles of dopamine and norepinephrine in mediating exploration, a direct comparison using the same dynamic decision making task is needed. Here, we ran mice in a restless bandit task, which encourages both exploration and exploitation. We systemically administered a nonselective DA antagonist (flupenthixol), a nonselective DA agonist (apomorphine), a NE beta-receptor antagonist (propranolol), and a NE beta-receptor agonist (isoproterenol), and examined changes in exploration within subjects across sessions. We found a bidirectional modulatory effect of dopamine receptor activity on exploration - increasing dopamine activity decreased exploration and decreasing dopamine activity increased exploration. The modulation of exploration via beta-noradrenergic activity was mediated by sex. Computational model parameters revealed that dopamine modulation affected exploration via decision noise and norepinephrine modulation via outcome sensitivity. Together, these findings suggested that the mechanisms that govern the transition between exploration and exploitation are sensitive to changes in both catecholamine functions and revealed differential roles for NE and DA in mediating exploration. Significance StatementBoth dopamine (DA) and norepinephrine (NE) has been implicated in the decision making process. Although these two catecholamines have shared aspects of their biosynthetic pathways and projection targets, they are thought to exert many core functions via distinct neural targets and receptor subtypes. However, the computational neuroscience literature often ascribes similar roles to these catecholamines, despite the above evidence. Resolving this discrepancy is important in guiding mechanistic hypotheses emerging from computational psychiatric approaches. This study examines the role of dopamine and norepinephrine on the explore-exploit tradeoff. By testing mice, we were able to compare multiple pharmacological agents within subjects, and examine source of individual differences, allowing direct comparison between the effects of these two catecholamines in modulating decision making.

animal behavior and cognition↗

ArMo: An Articulated Mesh Approach for Mouse 3D Reconstruction

Characterizing animal behavior requires methods to distill 3D movements from video data. Though keypoint tracking has emerged as a widely used solution to this problem, it only provides a limited view of pose, reducing the body of an animal to a sparse set of experimenter-defined points. To more completely capture 3D pose, recent studies have fit 3D mesh models to subjects in image and video data. However, despite the importance of mice as a model organism in neuroscience research, these methods have not been applied to the 3D reconstruction of mouse behavior. Here, we present ArMo, an articulated mesh model of the laboratory mouse, and demonstrate its application to multi-camera recordings of head-fixed mice running on a spherical treadmill. Using an end-to-end gradient based optimization procedure, we fit the shape and pose of a dense 3D mouse model to data-derived keypoint and point cloud observations. The resulting reconstructions capture the shape of the animals surface while compactly summarizing its movements as a time series of 3D skeletal joint angles. ArMo therefore provides a novel alternative to the sparse representations of pose more commonly used in neuroscience research.

animal behavior and cognition↗

PREMISE: A database of 20 Macaca Fascicularis PET/MRI brain imaging available for research

Non-human primate (NHP) studies are unique in translational research, especially in neurosciences and neuroimaging approaches are a preferred method for scaling cross-species comparative neurosciences. In this regard, neuroimaging database development and sharing are encouraged to increase the number of subjects available to the community while limiting the number of animals used in research. We present here a simultaneous PET/MR dataset of 20 Macaca Fascicularis structured according to the Brain Imaging Data Structure (BIDS) standards. This database contains multiple MRI sequences (anatomical, diffusion and perfusion imaging notably), as well as PET perfusion and inflammation using respectively [15O]H2O and [11C]PK11195 radiotracers. We describe the pipeline method to assemble baseline data from various cohorts and qualitatively assessed all the data using signal-to-noise and contrast-to-noise ratios as well as the median of intensity. The database is stored and available through the PRIME-DE consortium repository.

scientific communication and education↗

Differentiation trajectories of the Hydra nervous system reveal transcriptional regulators of neuronal fate

Hydra vulgaris, a cnidarian with a simple nerve net, is an emerging model for developmental, regenerative, and functional neuroscience. Its genetic tractability and capacity for whole-system imaging make it well suited for studying neuron replacement, regeneration, and neural circuit function. Here, we present the most comprehensive molecular and spatial characterization of the H. vulgaris nervous system to date. Using single-cell RNA sequencing, we identified eight neuron types, each defined by distinct neuropeptide expression, and further resolved these into fifteen transcriptionally distinct subtypes with unique spatial distributions and morphologies. To investigate the gene regulatory networks underlying neuronal differentiation, we applied trajectory inference, identified key transcription factors, and performed ATAC-seq on sorted neurons to map chromatin accessibility. All datasets are available through an interactive, user-friendly web portal to support broad use by the research community. Together, these resources provide a foundation for uncovering molecular mechanisms that govern nervous system development, homeostasis, and regeneration in H. vulgaris. Summary StatementHydra vulgaris is a model for regenerative and functional neuroscience. This study identifies fifteen neuron subtypes using scRNA-seq, maps spatial distributions, explores regulatory mechanisms, and provides an accessible web portal.

developmental biology↗

New vistas for epileptogenic zone (EZ) localization and interpretation

ObjectivePost-surgical seizure freedom in drug-resistant epilepsy (DRE) patients varies from 30 to 80%, implying that in many cases the current approaches fail to fully map the epileptogenic zone (EZ). This suggests that the EZ entails a broader epileptogenic brain network (EpiNet) beyond the seizure-zone (SZ) that show seizure activity. MethodsWe first used computational modeling to identify putative complex-systems- and systems-neuroscience-driven mechanistic biomarkers for epileptogenicity. We then extracted these epileptogenicity biomarkers from stereo-EEG (SEEG) resting-state data from DRE patients and trained supervised classifiers to localize the SZ with these biomarkers against gold-standard clinical localization. To further explore the prevalence of these pathological biomarkers in an extended network outside of the clinically-identified SZ, we also used unsupervised classification. ResultsSupervised SZ-classification trained on individual features achieved accuracies of 0.6-0.7 areaunder-the-receiver-operating-characteristics curve (AUC). However, combining all criticality and synchrony features improved the AUC up to 0.85. Unsupervised classification uncovered an EpiNet-like cluster of brain regions with 51% of regions outside of SZ. Brain regions in this cluster engaged in inter-areal hypersynchrony and locally exhibited high amplitude bistability and excessive inhibition, which was strikingly similar to the high seizure-risk regime revealed by computational modeling. SignificanceThe finding that combining biomarkers improves EZ localization shows that the different mechanistic biomarkers of epileptogenicity assessed here yield synergistic information. On the other hand, the discovery of SZ-like pathophysiological brain dynamics outside of the clinically-defined EZ provides experimental localization of an extended EpiNet. Key pointsO_LIWe advanced novel complex-systems- and systems-neuroscience-driven biomarkers for epileptogenicity C_LIO_LIIncreased bistability, inhibition, and power-low scaling exponents characterized our model operating in a high seizure-risk regime and SEEG oscillations in the seizure-zone (SZ) C_LIO_LICombining all biomarkers yielded more accurate supervised SZ-classification than using any individual biomarker alone C_LIO_LIUnsupervised classification revealed more extended pathological brain networks including the SZ and many non-seizure-zone areas that were previously considered healthy C_LI

physiology↗

Global Cluster Analysis and Network Visualization in Chordoma: A Scientometric Mapping

BackgroundA recent scientific and comprehensive examination of the present status and future directions in chordoma research is a subject worth exploring. This study endeavors to investigate and depict the quantitative and qualitative development, research frontiers, and future trends in chordoma using bibliometric approaches. MethodsA total of 2510 publications from the Science Citation Index Expanded of Web of Science Core Collection were obtained from 1991 to 2021 and selected for bibliometric analysis. Bibliometric and knowledge-map analysis were performed using visualized approaches such as VOS viewer and CiteSpace, along with Origin 8.0 and GraphPad prism 8 tools. ResultsThe top five countries with the highest number of publications are the United States, China, Japan, Italy, and Germany. The most impactful journal is World Neurosurgery, while the most cited journal is Journal of Neurosurgery. The five most common research fields are neuroscience neurology, surgery, oncology, pathology, and radiology nuclear medicine medical imaging. Harvard University has the highest number of publications among institutions, and Gokaslan, Ziya L. has published the most papers and has the highest total link strength. The article "Chordoma: incidence and survival patterns in the United States, 1973-1995" has the most citations. The co-occurrence analysis includes 744 keywords that were categorized into five clusters: radiotherapy study, basic and clinical trill research, diagnostic study, surgical approach research, and prognostic study. CiteSpace identified 12 clusters of keywords, with clusters 0-5, 7, and 12 being former hotspots, clusters 6 and 11 being mid-period hotspots, and cluster 8 being the current research hotspot. ConclusionsThis bibliometric analysis and visualized study of chordomas global research field in the past 30 years is the first of its kind. The findings may help academic researchers comprehend chordomas dynamic evolution and research trends. Investigating the current status and future trends in chordoma research is a valuable undertaking. Using a bibliometric approach, this study aims to quantitatively and qualitatively explore the development and frontiers of chordoma research. A total of 2510 publications were analyzed, downloaded from the Science Citation Index Expanded of Web of Science Core Collection between 1991 and 2021. Bibliometric and knowledge-map analysis were performed using visualized approaches, Origin 8.0, and GraphPad prism 8 tools. The top five countries with the most publications were the USA, China, Japan, Italy, and Germany. World Neurosurgery was the most influential journal, and the Journal of Neurosurgery was the most co-cited. The research fields focused on neuroscience, neurology, surgery, oncology, pathology, and radiology nuclear medicine medical imaging. Harvard University produced the highest number of publications, and Gokaslan, Ziya L had the most papers and total link strength. The co-occurrence analysis included 744 keywords and was classified into five clusters: radiotherapy study, basic and clinical trill research, diagnostic study, surgical approach research, and prognostic study. Additionally, 12 clusters of keywords were identified by CiteSpace, with cluster 0 to 5, cluster 7, and cluster 12 being former appeared hotspots, clusters 6 and 11 being mid-period appeared hotspots, and cluster 8 being present research hotspots. This is the first comprehensive bibliometric analysis and visualized study of the global chordoma research field in the past 30 years, providing insights into its dynamic evolution and research trends. It may be beneficial for researchers to gain a better understanding of chordoma research. Level of EvidenceLevel 3 Short SummaryThis investigation represents the inaugural comprehensive and scientific bibliometric analysis and visualization of the global chordoma research landscape over the preceding three decades. The present inquiry offers advantages to scholarly investigators seeking to gain a nuanced understanding of the dynamic development and research tendencies pertaining to chordoma.

cancer biology↗

Linked OXTR Variants Are Associated with Social Behavior Differences in Bonobos (Pan paniscus)

Single-nucleotide polymorphisms (SNPs) in forkhead box protein P2 (FOXP2), oxytocin receptor (OXTR), and arginine vasopressin receptor gene 1A (AVPR1A) have been associated with linguistic and social development in humans, as well as symptom severity in autism. Studying biobehavioral mechanisms in the species most closely related to humans can provide insights into the origins of human communication, and the impact of genetic variation on complex behavioral phenotypes. Here, we tested the hypothesis that similar genetic factors underlie social communication differences in both bonobos (Pan paniscus) and humans. We analyzed Sanger sequencing results to determine if bonobos exhibit individual variation at 10 loci across FOXP2, OXTR, and AVPR1A that have been implicated in human social development and behavior. We identified a novel variant in bonobo FOXP2, as well as three novel variants in bonobo OXTR that were 19-184 base pairs away from the target human SNPs. We also found a linked SNP combination (TGA) across the 3 novel bonobo OXTR sites at high frequency (65%) in the study population, including 6 homozygous bonobos. When comparing the combined OXTR genotypes, we found significant group differences in social behavior; bonobos with two copies of the TGA combination were more social than bonobos with one copy and bonobos with zero copies of the TGA combination. Taken together, our findings suggest that these OXTR variants may influence individual-level social behavior in bonobos and support the notion that linked genetic variants are promising biomarkers for differences in human social communication. Revision SummaryThe original manuscript version underwent peer-review at Frontiers in Behavioral Neuroscience. As a part of this process, a reviewer requested that the sequence files be included in the manuscript. While preparing these files, Dr. Hudson identified several errors in his work regarding the individual bonobo genotypes reported in the original version of the manuscript. Upon discovery of the errors, Dr. Skiba and Dr. Taglialatela conducted a new genetic analysis using the raw files and subsequently corrected the misreported information - updating the editorial team and including the raw sequences with the revised manuscript. In addition to requested edits for improvement and clarity by the peer-reviewers, this revised manuscript corrects several errors in the human and bonobo reference genomes, the SNP locations, and the individual genotypes reported in the original manuscript. Information in the figures, main text, and supplementary materials has been corrected in this revised version. Dr. Hudson and his lab member, Mr. Hansen, were unable to identify how the errors in data reporting occurred, and neither party contributed to the revised version of this manuscript. The co-author list has been adjusted to reflect contributions to the corrected analyses and revised manuscript. We are deeply grateful to the editorial team and reviewers at Frontiers in Behavioral Neuroscience for their thorough peer-review of this work - improving the overall quality of the manuscript and leading to the discovery and correction of substantial errors in the original version.

evolutionary biology↗

Magnetoelectric Nanodiscs Enable Wireless Transgene-Free Neuromodulation

Deep-brain stimulation (DBS) with implanted electrodes revolutionized treatment of movement disorders and empowered neuroscience studies. Identifying less invasive alternatives to DBS may further extend its clinical and research applications. Nanomaterial-mediated transduction of magnetic fields into electric potentials offers an alternative to invasive DBS. Here, we synthesize magnetoelectric nanodiscs (MENDs) with a core-double shell Fe3O4-CoFe2O4-BaTiO3 architecture with efficient magnetoelectric coupling. We find robust responses to magnetic field stimulation in neurons decorated with MENDs at a density of 1 {micro}g/mm2 despite individual-particle potentials below the neuronal excitation threshold. We propose a model for repetitive subthreshold depolarization, which combined with cable theory, corroborates our findings in vitro and informs magnetoelectric stimulation in vivo. MENDs injected into the ventral tegmental area of genetically intact mice at concentrations of 1 mg/mL enable remote control of reward behavior, setting the stage for mechanistic optimization of magnetoelectric neuromodulation and inspiring its future applications in fundamental and translational neuroscience.

bioengineering↗

MarmoPose: A Deep Learning-Based System for Real-time Multi-Marmoset 3D Pose Trackingp

The ability to track positions and poses (body parts) of multiple monkeys in a 3D space in real time is highly desired by non-human primate (NHP) researchers in behavioral and systems neuroscience because it allows both analyzing social behaviors among multiple NHPs and performing close-loop experiments (e.g., delivering sensory or optogenetics stimulation during a particular behavior). While a number of animal pose tracking systems have been reported, nearly all published work lacks the real-time analysis capacity. Existing methods for tracking freely moving animals have been developed primarily for rodents which typically move on a 2D space. In contrast, NHPs roam in a 3D space and move at a much faster speed than rodents. We have designed a real-time 3D pose tracking system (MarmoPose) based on deep learning to capture and quantify social behaviors in natural environment of a highly social NHP species, the common marmosets (Callithrix jacchus) which has risen to be an important NHP model in neuroscience research in recent years. This system has minimum hardware requirement and can accurately track the 3D poses (16 body locations) of multiple marmosets freely roaming in their homecage. It employs a marmoset skeleton model to optimize the 3D poses and estimate invisible body locations. Furthermore, it achieves high inference speed and provides an online processing module for real-time closed-loop experimental control based on the 3D poses of marmosets. While this system is optimized for marmosets, it can also be adapted for other large animal species in a typical housing environment with minimal modifications.

animal behavior and cognition↗

Revealing non-trivial information structures in aneural biological tissues via functional connectivity

A central challenge in the progression of a variety of open questions in biology, such as morphogenesis, wound healing, and development, is learning from empirical data how information is integrated to support tissue-level function and behavior. Information-theoretic approaches provide a quantitative framework for extracting patterns from data, but so far have been predominantly applied to neuronal systems at the tissue-level. Here, we demonstrate how time series of Ca2+ dynamics can be used to identify the structure and information dynamics of other biological tissues. To this end, we expressed the calcium reporter GCaMP6s in an organoid system of explanted amphibian epidermis derived from the African clawed frog Xenopus laevis, and imaged calcium activity pre- and post- a puncture injury, for six replicate organoids. We constructed functional connectivity networks by computing mutual information between cells from time series derived using medical imaging techniques to track intracellular Ca2+. We analyzed network properties including degree distribution, spatial embedding, and modular structure. We find organoid networks exhibit more connectivity than null models, with high degree hubs and mesoscale community structure with spatial clustering. Utilizing functional connectivity networks, we show the tissue retains non-random features after injury, displays long range correlations and structure, and non-trivial clustering that is not necessarily spatially dependent. Our results suggest increased integration after injury, possible cellular coordination in response to injury, and some type of generative structure of the anatomy. While we study Ca2+ in Xenopus epidermal cells, our computational approach and analyses highlight how methods developed to analyze functional connectivity in neuronal tissues can be generalized to any tissue and fluorescent signal type. Our framework therefore provides a bridge between neuroscience and more basal modes of information processing. Author summaryA central challenge in understanding several diverse processes in biology, including morphogenesis, wound healing, and development, is learning from empirical data how information is integrated to support tissue-level function and behavior. Significant progress in understanding information integration has occurred in neuroscience via the use of observable live calcium reporters throughout neural tissues. However, these same techniques have seen limited use in the non-neural tissues of multicellular organisms despite similarities in tissue communication. Here we utilize methods designed for neural tissues and modify them to work on any tissue type, demonstrating how non-neural tissues also contain non-random and potentially meaningful structures to be gleaned from information theoretic approaches. In the case of epidermal tissue derived from developing amphibians, we find non-trivial informational structure over greater spatial and temporal scales than those found in neural tissue. This hints at how more exploration into information structures within these tissue types could provide a deeper understanding into information processing within living systems beyond the nervous system.

systems biology↗

Novel and optimized mouse behavior enabled by fully autonomous HABITS: Home-cage Assisted Behavioral Innovation and Testing System

Mice are among the most prevalent animal models used in neuroscience, benefiting from the extensive physiological, imaging and genetic tools available to study their brain. However, the development of novel and optimized behavioral paradigms for mice has been laborious and inconsistent, impeding the investigation of complex cognitions. Here, we present a home-cage assisted mouse behavioral innovation and testing system (HABITS), enabling free-moving mice to learn challenging cognitive behaviors in their home-cage without any human involvement. Supported by the general programming framework, we have not only replicated established paradigms in current neuroscience research but also developed novel paradigms previously unexplored in mice, resulting in more than 300 mice demonstrated in various cognition functions. Most significantly, HABITS incorporates a machine-teaching algorithm, which comprehensively optimized the presentation of stimuli and modalities for trials, leading to more efficient training and higher-quality behavioral outcomes. To our knowledge, this is the first instance where mouse behavior has been systematically optimized by an algorithmic approach. Altogether, our results open a new avenue for mouse behavioral innovation and optimization, which directly facilitates investigation of neural circuits for novel cognitions with mice.

animal behavior and cognition↗

Agarose Stamped Device: A simple but effective fixation method for Zebrafish Larvae through Customizable 3D Printed Molds for Danio rerio (Zebrafish)

Precise immobilization of zebrafish larvae is critical for high-resolution neural imaging and behavioral studies, yet conventional methods are often labor-intensive, low-throughput, and can induce stress. To address these challenges, we introduce the Agarose Stamped Device (ASD), a novel platform enabling efficient and gentle immobilization of zebrafish larvae for high-resolution imaging and behavioral assays. By employing a stamping approach to mold an array of consistent agarose wells, the ASD enables rapid, parallel, and reproducible positioning of larvae in a standardized orientation while minimizing stress and preserving viability. We validated ASD performance by comparing larval survival, heart rate, and imaging stability to conventional agarose embedding, finding no adverse effects on larval physiology and significantly enhanced imaging throughput and data consistency. Demonstrations in neuroscience and behavioral experiments underscore the devices versatility for diverse in vivo studies. The ASD offers a simple, cost-effective, and powerful tool for advancing zebrafish-based neuroscience and behavioral research.

bioengineering↗

Recurrent Neural Network Exploration Strategies During Reinforcement Learning Depend on Network Capacity

Artificial neural networks constitute simplified computational models of neural circuits that might help understand how the biological brain solves and represents complex tasks. Previous research revealed that recurrent neural networks (RNNs) with 48 hidden units show human-level performance in restless four-armed bandit tasks but differ from humans with respect to the task strategy employed. Here we systematically examined the impact of network capacity (no. of hidden units) on computational mechanisms and performance. Computational modeling was applied to investigate and compare network behavior between capacity levels as well as between RNNs and human learners. Using a task frequently employed in human cognitive neuroscience work as well as in animal systems neuroscience work, we show that high-capacity networks displayed increased directed exploration and attenuated random exploration relative to low-capacity networks. RNNs with 576 hidden units approached "human-like" exploration strategies, but the overall switch rate and the level of perseveration still deviated from human learners. In the context of the resource-rational framework, which posits a trade-off between reward and policy complexity, human learners may devote more resources to solving the task, albeit without performance benefits over RNNs. Taken together, this work reveals the importance of network capacity on exploration strategies during reinforcement learning and therefore contributes to the goal of building neural networks that behave "human-like" to possibly gain insights into computational mechanisms in human brains.

animal behavior and cognition↗

Plastic-elastomer heterostructure for robust flexible brain-computer interfaces

Electronics for neural signal recording must be robust across multiple and deep brain regions while preserving tissue-level flexibility to ensure stable tracking over months or years. However, existing electronics cannot simultaneously achieve robustness and tissue-level flexibility, limiting their potential for customizable and scalable neuroscience research and clinical applications. Here, we introduce FlexiSoft, an electronic platform based on a plastic-elastomer heterostructure that uniquely integrates mechanical robustness and tissue-level flexibility. Compared to conventional flexible electronics of similar thickness, the FlexiSoft platform demonstrates an order-of- magnitude improvement in both mechanical robustness (critical energy release rate) and flexibility (flexural rigidity). Leveraging these mechanical advantages, we developed FlexiSoft probe for robust implantation, demonstrated by its ability to withstand repeated insertion and removal, as well as to reach centimeter-scale depths comparable to those in the human brain. The platform enables long-term recording from the same neurons across the hippocampus (HPC) and primary motor cortex (M1) during a months-long motor learning task, thereby revealing long-term dynamic changes in neuronal firing patterns. Additionally, FlexiSofts unique robustness and flexibility enable curved implantation routes, opening new directions of customizable implantation pathways. In summary, we present FlexiSoft as a novel, robust, and tissue-level flexible heterostructure electronics platform that advances flexible brain-computer interfaces (BCIs) with strong translational potential for neuroscience and clinical applications.

bioengineering↗

Exploring Holography in Neuro-Vascular Dynamics

The holonomic brain theory--originally formulated to account for the need of non-local memory encoding in cognitive systems--could gain new theoretical traction when integrated with holographic principles from physics, most notably the AdS/CFT correspondence. Recent findings in neuroscience suggest that conformal field theories (CFTs), emerging at critical points across spatiotem-poral scales in neural dynamics, are essential for brain function. Concurrently, black-brane geometries, long studied in gravitational physics, can find unexpected analogues in the interplay of active matter dynamics and the brains neuroanatomical organization. Motivated by these parallels, we posit a generalized holographic framework and interrogate its validity through the fluid/gravity duality--a correspondence linking hydrodynamic equations to gravitational spacetime metrics. In this work, we explore the holographic principles at the Navier-Stokes regime, demonstrating that holography can model key neurophysiological mechanisms: cerebral autoregulation (the brains hemodynamic self-stabilization) and neurovascular coupling (the dynamic neuron-bloodflow interplay). This work bridges holography, active matter physics, and neuroscience, proposing a unified framework to decode the brains multiscale organization, its resilience to perturbations, and its computational capabilities. By grounding neurovascular physiology in gravitational duals, we open pathways to reinterpret brain function through the lens of emergent spacetime geometry.

biophysics↗

Plug-and-Play automated behavioral tracking of zebrafish larvae with DeepLabCut and SLEAP: pre-trained networks and datasets of annotated poses

Zebrafish are an important model system in behavioral neuroscience due to their rapid development and suite of distinct, innate behaviors. Quantifying many of these larval behaviors requires detailed tracking of eye and tail kinematics, which in turn demands imaging at high spatial and temporal resolution, ideally using semi or fully automated tracking methods for throughput efficiency. However, creating and validating accurate tracking models is time-consuming and labor intensive, with many research groups duplicating efforts on similar images. With the goal of developing a useful community resource, we trained pose estimation models using a diverse array of video parameters and a 15-keypoint pose model. We deliver an annotated dataset of free-swimming and head-embedded behavioral videos of larval zebrafish, along with four pose estimation networks from DeepLabCut and SLEAP (two variants of each). We also evaluated model performance across varying imaging conditions to guide users in optimizing their imaging setups. This resource will allow other researchers to skip the tedious and laborious training steps for setting up behavioral analyses, guide model selection for specific research needs, and provide ground truth data for benchmarking new tracking methods. SIGNIFICANCE STATEMENTLarval zebrafish are an emerging model in systems neuroscience, offering unique advantages for linking brain activity to behavior. However, detailed behavioral tracking, essential for such studies, requires time- and labor-intensive annotation and model training. To eliminate this bottleneck, here we provide a high-quality, annotated dataset of zebrafish behaviors for both free-swimming and head-embedded preparations alongside four pre-trained pose estimation models using DeepLabCut and SLEAP. We benchmark models performance across diverse imaging conditions to guide optimal setup choices. This community resource will allow researchers to bypass the most time-consuming stages of data annotation and training, enabling immediate behavioral analysis. By removing this key hurdle, this work will accelerate project initiation, support reproducibility, and provide a foundation for future tracking method development.

animal behavior and cognition↗

Effect of fluid control on the affective state of laboratory macaques

Fluid control protocols are widely used in neuroscience to motivate laboratory macaques to engage with behavioural tasks. Despite strong evidence that the physiology of the animals is not compromised by such protocols, fluid control remains controversial due to its potential impact on the psychological well-being of the animals. To address this concern, we investigated the effect of fluid control on the affective state of 23 socially-housed adult macaques (10 females) engaged in neuroscience experiments. The protocol involved up to five consecutive days of fluid control per week, followed by a minimum of two days with unrestricted fluid access. The affective state of the animals was primarily assessed by quantifying the frequency of pharmacologically-validated behavioural indicators of high-arousal negative affect (self-scratching, body shaking, self-grooming). The analysis was subsequently extended to validated behavioural indicators of low-arousal negative affect (Inactive not alert) and other behaviours suspected of indicate high-arousal negative affect but lacking proper validation (pacing, yawning). In total, 700 hours of video footage spanning up to seven years of intermittent fluid control per animal were analysed. Despite this extensive dataset, the study found no significant impact of fluid control on average, or any evidence of habituation or sensitization over the years on any of the affective state indicators. Additional results indicate that these null results are not due to a lack of sensitivity, supporting the view that fluid control, as implemented in this study, does not have an adverse impact on the psychological well-being of laboratory macaques. We argue that macaque welfare will be best served by focusing future refinement on other procedures.

animal behavior and cognition↗

EvoPRAISE: computationally guided directed evolution for rational AAV capsid engineering

Advances in neuroscience and gene therapy would be greatly accelerated by adeno-associated virus (AAV) vectors capable of crossing the blood-brain barrier (BBB). Unfortunately, capsids evolved in mice often fail to function in other species, limiting applications in non-model animals such as hibernators. To overcome this limitation, we developed Evolutionary Optimization combined with APPRAISE (EvoPRAISE), a framework that integrates structure-based peptide-protein affinity prediction with directed evolution to design peptide binders for membrane proteins. Using EvoPRAISE, we designed peptide binders that interact with Ly6E, a BBB-associated membrane protein expressed in the Syrian hamster brain. By inserting these peptides into surface-exposed regions of the AAV9 capsid, we developed AAV capsid variants capable of crossing the BBB following systemic administration in Syrian hamsters. Furthermore, this framework enabled the design of AAV capsids displaying peptide binders targeting the human BBB. This rational approach reduces the need for large-scale animal in vivo selection procedures and facilitates the efficient development of receptor-targeted AAV capsids for both neuroscience studies in non-model species and translational gene delivery to the human brain.

bioengineering↗