eneuro

Quantifying whether and when signals are modulated by autonomous or external events is ubiquitous in the field of neuroscience. Existing statistical approaches, however, are not ideally suited to do this, especially when the signals under scrutiny show temporal autocorrelations. For example, a standard approach in the analysis of calcium imaging data is to use a t-test on predetermined time-windows to quantify whether neurons respond (differently) to an event of interest. While this is attractive because of its simplicity, only average signal differences can be detected. In practice, neurons often show complex response dynamics which are missed by conventional statistical tests. More advanced methods, such as bin-wise ANOVAs, do not share this drawback, but can suffer from high false-positive rates or "ghost correlations" when applied to temporally autocorrelated data. To solve this issue, we have developed three novel statistical tests extending the original ZETA-test to other use cases: 1) a test for time-series data; 2) a two-sample test to detect differences in neural responses between two conditions; and 3) a two-sample test to detect differences in time-series data between two conditions. In addition, we have improved upon the original ZETA-test. We show that our methods have a statistical sensitivity superior to t-tests and ANOVAs and work well with temporally autocorrelated data where other approaches fail. Our methods are widely applicable and we present example applications to Neuropixels data, two-photon GCaMP imaging data, and human electrocorticogram data. Open-source code for implementations in MATLAB and Python is available on GitHub and PyPi. Significance StatementNeurophysiology involves the detection of weak signals in a noisy background. Often these signals, as well as the background, are temporally autocorrelated. This means that many statistical methods, such as the ANOVA, are inappropriate. In some cases, these statistical tests produce "ghost" signals that mislead researchers into thinking there is an effect of their experiment, when in reality there is none. We have therefore developed a new family of statistical tests, the ZETA-tests, that are not negatively impacted by temporal autocorrelations. They do not use arbitrary (hyper)parameters, like bin size, and show superior statistical sensitivity compared to other methods like t-tests and ANOVAs. We provide easy-to-use implementations and expect our methods to be useful for many neuroscientists from various disciplines.

Sensorimotor adaptation is traditionally studied in well-controlled laboratory settings with specialized equipment. However, recent public health concerns such as the COVID-19 pandemic, as well as a desire to recruit a more diverse study population, have led the motor control community to consider at-home study designs. At-home motor control experiments are still rare because of the requirement to write software that can be easily used by anyone on any platform. To this end, we developed software that runs locally on a personal computer. The software provides audiovisual instructions and measures the ability of the subject to control the cursor in the context of visuomotor perturbations. We tested the software on a group of at-home participants and asked whether the adaptation principles inferred from in-lab measurements were reproducible in the at-home setting. For example, we manipulated the perturbations to test whether there were changes in adaptation rates (savings and interference), whether adaptation was associated with multiple timescales of memory (spontaneous recovery), and whether we could selectively suppress subconscious learning (delayed feedback, perturbation variability) or explicit strategies (limited reaction time). We found remarkable similarity between in-lab and at-home behaviors across these experimental conditions. Thus, we developed a software tool that can be used by research teams with little or no programming experience to study mechanisms of adaptation in an at-home setting. SignificanceSensorimotor learning is traditionally studied in the laboratory, but recent public health emergencies have caused the community to consider at-home data collection. To accelerate this effort, we implemented a software tool that remotely tracks motor learning. Compared with previous remote data collection strategies, our software (1) generates experiments of arbitrary length that (2) run locally on a participants laptop which (3) can be modified without any programming expertise in the research laboratory. Here we show a close correspondence between behaviors captured by our tool and those observed in laboratory environments including savings, interference, spontaneous recovery, and variations in implicit and explicit learning due to changes in perturbation variance, reaction time constraints, and feedback delay. Our software and its corresponding manuals are available here: https://osf.io/e8b63/.

Investigation of rodent drinking behavior has provided insight into drivers of thirst, circadian rhythms, anhedonia, and drug and ethanol consumption. Traditional methods of recording fluid intake involve weighing bottles, which is cumbersome and lacks temporal resolution. Several open-source devices have been designed to improve drink monitoring, particularly for two-bottle choice tasks. However, recent designs are limited by the use of infrared photobeam sensors and incompatibility with prolonged undisturbed use in ventilated home cages. Beam-break sensors lack accuracy for bout microstructure analysis and are prone to damage from rodents. Thus, we designed LIQ HD (Lick Instance Quantifier Home cage Device) with the goal of utilizing capacitive sensors to increase accuracy and analyze lick microstructure, building a device compatible with ventilated home cages, increasing scale with prolonged undisturbed recordings, and creating a design that is easy to build and use with an intuitive touchscreen graphical user interface. The system tracks two-bottle choice licking behavior in up to 18 rodent cages, or 36 single bottles, on a minute-to-minute timescale controlled by a single Arduino microcontroller. The data are logged to a single SD card, allowing for efficient downstream analysis. With sucrose, quinine, and ethanol two-bottle choice tasks, we validated that LIQ HD has superior accuracy compared to photobeam sensors. The system measures preference over time and changes in bout microstructure, with undisturbed recordings lasting up to 7 days. All designs and software are open-source to allow other researchers to build upon the system and adapt LIQ HD to their animal home cages. Significance StatementTwo-bottle choice drinking tasks are traditionally performed by periodically weighing bottles, which is cumbersome and lacks temporal resolution. Several open-source tools have been developed to improve drink monitoring in various settings. However, no open-source devices have been designed specifically to investigate temporally precise two-bottle choice drinking behavior and bout microstructure during prolonged undisturbed tasks in mouse ventilated home cages at a large scale. Our design, LIQ HD (Lick Instance Quantifier Home cage Device), is a home cage compatible system that utilizes capacitive sensors for highly accurate lick detection during two-bottle choice tasks in up to 18 cages driven by a single Arduino microcontroller. The system is low-cost, easy to build, and controlled via touchscreen with an intuitive graphical user interface.

Paw preference, or handedness, is a widely studied behavioral trait used to assess lateralization and motor function in rodents. This study aimed to determine the consistency and reliability of three commonly used behavioral tests to rigorously assess paw preference: the Collins Test, the Staircase Test, and the Pawedness Trait Test. Thirty Sprague Dawley rats (12-48 weeks; 20 females, 10 males) were subjected to all three behavioral tests. Paw uses were recorded, and the laterality index was calculated for each test. Additional cohorts of younger rats (6-9 weeks; 45 females, 45 males) and older rats (12-48 weeks; 38 females, 45 males) were tested to assess the effects of age and sex on paw preference. ANOVA, Fleiss and pairwise Cohens Kappa were used for statistical analysis. All three tests yielded comparable measures of paw preference (ANOVA, p = 0.801). Substantial inter-test agreement was demonstrated by Fleiss kappa ({kappa} = 0.761, p = 3.93 x 10{square}12). Paw preference did not significantly vary by age or sex, and the distribution of left, right, and ambidextrous preference categories aligned with existing literature. The Collins, Staircase, and Pawedness Trait Tests provide consistent, reliable assessments of paw preference in Sprague Dawley rats. These validated behavioral assays can serve as essential tools for preclinical research, including but not limited to models of motor asymmetry observed in stroke, cerebral palsy, traumatic brain injury, and language lateralization, as well as neurodegenerative diseases. HighlightsO_LIRigorously validated paw preference in Sprague Dawley rats using three commonly used behavioral tests. C_LIO_LIDemonstrated strong inter-test agreement across Collins, Staircase, and PaTRaT ({kappa} = 0.761) C_LIO_LIShowed that paw preference remains stable across age and sex in large cohorts C_LIO_LIApplied standardized LI thresholds to enable cross-test comparability C_LI

Standing balance relies on rapid reflexes as well as longer-latency subcortical and cortical processes to generate corrective responses to postural disturbances. Electroencephalography (EEG) studies consistently identify two perturbation-evoked markers of cortical activity, the balance N1 and midfrontal theta power, associated with changes in body orientation and corrective actions. It remains unclear, however, whether these markers depend on the nervous systems active control of posture or reflect a more general evaluation of unexpected sensory input. We tested this by measuring cortical and muscle activity during support-surface perturbations while systematically manipulating whether participants actively controlled posture. In Experiment 1 (n = 10), participants experienced identical perturbations while either actively balancing or being passively moved through equivalent motion. Despite large reductions in balance-correcting muscle activity during passive trials ([~]30-60%), N1 and theta responses persisted with only modest amplitude reductions ([~]10%). In Experiment 2 (n = 16), we created passive conditions increasingly removed from balance by varying sensory feedback (footplate + whole-body vs footplate-only motion) and motor engagement (isometric contraction vs. relaxed posture). Relaxed postures markedly suppressed muscle responses, yet cortical responses persisted, showing only modest modulation with sensory feedback (larger during footplate-only rotations) and no dependence on motor engagement. Together, these results indicate that N1 and midfrontal theta are not dependent on active postural control and persist even without matching sensory feedback or motor engagement. Rather than reflecting the generation or scaling of corrective actions, they index the early detection and evaluation of unexpected sensory events, consistent with prediction error or surprise processing. Key pointsO_LIWhen standing balance is disturbed by a perturbation, the brain shows characteristic electrical responses called the balance N1 and theta activity, which are thought to contribute to balance-correcting actions. C_LIO_LIWe tested whether these cortical responses depend on actively controlling posture or instead reflect the detection of unexpected motion irrespective of balance conditions. C_LIO_LIParticipants stood in a robotic balance simulator and experienced identical perturbations while actively balancing or being passively moved, and when whole-body sensory feedback and muscle engagement were removed. C_LIO_LIThe balance N1 and theta activity persisted in conditions where participants were not controlling their movement and even when whole-body sensory feedback and motor engagement were removed, whereas balance-correcting muscle responses were strongly diminished. C_LIO_LIThis shows that cortical responses to balance perturbations are not specific to active balance control but likely represent the brains detection and evaluation of unexpected sensory events. C_LI

String-pulling in rodents (rats and mice) is a task in which animals make hand-over-hand movements to spontaneously reel in a string with or without a food reward attached to its end. The task elicits bilateral skilled hand movements for which rodents require little training. The task is suitable for phenotyping physiology and pathophysiology of sensorimotor integration in rodent models of neurological and motor disorders. Because a rodent stands in the same location and its movements are repetitive, the task lends itself to quantification of topographical and kinematic parameters for on-line tactile tracking of the string, skilled hand movements for grasping, and rhythmical bilateral forearm movements to advance the string. Here we describe a Matlab(R) based software with a graphical user interface to assist researchers in analyzing the video record of string pulling. The software allows global characterization of position and motion using optical flow estimation, descriptive statistics, principal component, and independent component analyses as well as temporal measures of Fano factor, entropy, and Higuchi fractal dimension. Based on image segmentation and object tracking heuristic algorithms, the software also allows independent tracking of the body, ears, nose, and forehands for estimation of kinematic parameters such as body length, body angle, head roll, head yaw, head pitch, movement paths and speed of hand movement. The utility of the task and that of the software is presented by describing mouse strain characteristics in string-pulling behavior of two strains of mice, C57BL/6 and Swiss Webster. Postural and skilled hand kinematic differences that characterize the strains highlight the utility of the task and assessment methods for phenotypic and neurological analysis of healthy and rodent models of diseases such as Parkinsons, Huntingtons, Alzheimers and other neurological and motor disorders. Significance statementMouse models are used to investigate the physiology and pathophysiology of motor deficits observed in human neurological conditions, for testing substances for therapeutic drug development, and to investigate the role of neural systems and their genetic basis in the expression of behavior. Behavioral tasks involving unconditioned and natural behavior can provide rich insights into motor performance in animal models and analyses can be aided by the automated processing of video data for reliable quantification and high throughput.

A prosocial response to others in distress is increasingly recognized as a natural behavior for many social species, from humans to rodents. While prosocial behavior is more frequently observed towards familiar conspecifics, even within the same social context some individuals are more prone to help than others. For instance, in a rat helping behavior test, rats can release a distressed conspecific trapped inside a restrainer by opening the restrainer door. Typically, rats are motivated to release a trapped cagemate, and consistently release the trapped rat ( openers), yet around 30% do not open the restrainer ( non-openers). To characterize the difference between these populations, behavioral and neural activity were compared between opener and non-opener rats tested with a trapped cagemate in the helping test. Behaviorally, openers showed significantly more social affiliative behavior both before and after door-opening compared to non-openers. Analysis of brain-wide neural activity based on the immediate early gene c-Fos revealed increased activity in openers in the previously identified prosocial neural network compared to non-openers. The network includes regions associated with empathy in humans (somatosensory cortex, insula, cingulate cortex and frontal cortex), and motivation and reward regions such as the nucleus accumbens. Oxytocin receptor mRNA expression levels were higher in the accumbens but not the anterior insula. Several transcription control pathways were also significantly upregulated in openers accumbens. These findings indicate that prosocial behavior may be predicted by affiliative behavior and activity in the prosocial neural network and provide targets for the investigation of causal mechanisms underlying prosocial behavior. Significance StatementProsocial behavior is observed in many social species, including rodents, yet the determinants underlying why some animals help and others do not is poorly understood. Here, we show behavioral and neural differences between prosocial and non-prosocial pairs in a rat helping behavior test, with increased social interaction and nucleus accumbens oxytocin receptor gene expression in animals that helped.

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.

Place cells, first identified in the rat hippocampus as neurons that fire selectively at specific locations, are central to investigations of the neural underpinnings of spatial navigation. With recent work with human patients, identifying and characterizing place cells across species has become increasingly important for understanding the extent to which decades of rodent research generalize to humans and uncovering principles of spatial cognition. One challenge, however, is that detection methods differ: rodent studies often rely on spatial information (SI), whereas human studies employ analysis of variance (ANOVA) - based approaches. These methodological differences may affect the identified place cell population, which complicates how their properties are interpreted and cross-species comparisons. To address this, we systematically applied multiple detection pipelines to human and rat datasets, supported by simulations that vary place-field properties. Our analyses and simulations demonstrate that spatial information and ANOVA-based approaches are responsive to distinct place field properties: spatial information primarily reflects the contrast between peak and average firing rates, while ANOVA emphasizes consistency across trials. Across species, rodent place cells revealed a broad spectrum of spatial tuning, including strongly tuned neurons with high spatial information (SI) and high ANOVA values. In contrast, human place cells lacked this strongly tuned population and exhibited a narrower distribution of tuning scores, concentrated at the lower end of both spatial tuning metrics. Despite these differences, both species had an overlapping population of neurons with weaker yet consistent spatial tuning, which may support important functional roles such as generalization and mixed selectivity. Together, our study provides a roadmap showing how spatial tuning metrics shape place cell detection and interpretation, while underscoring the functional importance of weaker-tuned neurons in cross-species comparisons. Author SummaryPlace cells are neurons that become active in specific locations, and they play a critical role in how the brain supports navigation and memory. Place cells were first discovered in rats and later observed in humans, however, there has been a lack of direct comparisons between species using comparable approaches. Part of the difficulty doing so is that studies of rodent and human place cells have often relied on different analysis methods, making it difficult to determine if and how place-cell properties differ between species. To address this, in this study, we set out to understand how differences in place cell detection methods affect the identified place cell populations and interpretations of spatial coding across species. To do so, we compared the most prevalent detection methods used in rodent and human research side by side, applying them to datasets from both species and to simulations. We found that different methods emphasize different features of spatial responses, which changes which neurons are identified as place cells. Across species, rat recordings revealed a wide range of spatial responses, from neurons with sharply localized activity to those with broader but reliable patterns. Human recordings, by contrast, were more concentrated at weaker but consistent levels of tuning. Importantly, these weaker but consistent responses reflect an overlapping population of neurons found in both species, which may serve similar functional roles in supporting flexible spatial memory and generalization. By separating methodological effects from biological differences, we lay the groundwork for future cross-species studies for spatial coding. Materials Descriptions and Availability StatementsO_ST_ABSProject RepositoryC_ST_ABSThis project is openly available through an online project repository, which includes all the code used for data pre-processing and analysis. Project Repository: https://github.com/HSUPipeline/PlaceCellMethods DatasetThis project uses electrophysiological data collected from neurosurgical patients, as well as an open-access dataset of rat recordings from CRCNS.org: http://dx.doi.org/10.6080/K09G5JRZ The human data were collected as part of a previously published study and will be made available prior to publication [1]. A custom simulation framework was developed to evaluate place cell detection methods across species and will be released as part of the open-source SpikeTools repository prior to publication. SoftwareAll code used and developed for this project was written in the Python programming language. The code is openly available, licensed for reuse, and deposited in the project repository. Management of the dataset was conducted using the Human Single Unit (HSU) Pipeline: https://github.com/HSUPipeline Analyses of the single-neuron data were performed using the open-source SpikeTools toolbox: https://github.com/spiketools/spiketools Literature searches and related resources were organized using LISC, an open-source Python module for literature analysis. https://github.com/HSUPipeline/Literature

Pupillometry provides a non-invasive window into the mind and brain, particularly as a psychophysiological readout of autonomic and cognitive processes like arousal, attention, stress, and emotional states. Pupillometry research lacks a robust, standardized framework for data preprocessing, whereas in functional magnetic resonance imaging and electroencephalography, researchers have converged on tools such as fMRIPrep, EEGLAB and MNE-Python; these tools are considered the gold standard in the field. Many established pupillometry preprocessing packages and workflows fall short of serving the goal of enhancing reproducibility, especially since most existing solutions lack designs based on Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. To promote FAIR and open science practices for pupillometry research, we developed eyeris, a complete pupillometry preprocessing suite designed to be intuitive, modular, performant, and extensible (https://github.com/shawntz/eyeris). Out-of-the-box, eyeris provides a recommended preprocessing workflow and considers signal processing best practices for tonic and phasic pupillometry. Moreover, eyeris further enables open and reproducible science workflows, as well as quality control workflows by following a well-established file management schema and generating interactive output reports for both record keeping/sharing and quality assurance of preprocessed pupil data prior to formal analysis. Taken together, eyeris provides a robust all-in-one transparent and adaptive solution for high-fidelity pupillometry preprocessing with the aim of further improving reproducibility in pupillometry research. Impact StatementPupillometry research currently lacks a standardized, integrated preprocessing framework comparable to tools widely adopted in EEG and fMRI research. We introduce eyeris, an open-source R package that fills this gap through a modular, transparent pipeline with signal processing best practices, interactive diagnostic reports for quality control, and scalable database storage. eyeris advances pupillometry methods by promoting reproducible, FAIR-compliant workflows accessible to researchers at all levels of programming expertise.

Learning motor skills requires plasticity in the primary motor cortex (M1). But the capacity for cortical circuit plasticity varies over developmental age in sensory cortex. This study assesses the normal developmental trajectory of motor learning to assess how aptitude might vary with age. We trained mice of both sexes to run on a custom accelerating rotarod at ages from postnatal day (P) 20 to P120, tracking paw position and quantifying time to fall and changes in gait pattern. While animals of all ages were able to perform better after five training sessions, performance improved most rapidly on the first training day for mice between ages P30-60, suggesting an age with heightened plasticity. Learning this task required M1, because pharmacological inactivation of M1 prevented improvement in task performance. Paw position and gait patterns changed with learning, though differently between age groups. Successful mice learned to shift their gait from hopping to walking. Notably, this shift in gait happened earlier in the trial for forelimbs in comparison to hindlimbs. Thus, motor plasticity might more readily occur in forelimbs. Changes in gait and other kinematic parameters are an additional learning metric beyond time to fall, offering insight into how mice improve performance. Overall, these results suggest mouse motor learning has a developmental trajectory. SignificancePlasticity in sensory cortex is restricted to a limited developmental window. Learning motor skills requires motor cortex plasticity, but it is unknown whether learning aptitude changes over development. Here, we define the developmental trajectory of motor learning aptitude for the accelerating rotarod task in mice, demonstrating a sensitive period for motor learning. Learning peaks at P30-P60, with mice learning to shift from hopping to walking gait to stay on the rotarod longer. Learning this task depends on M1. Further, the gait shift in forelimbs precedes hindlimbs. Knowing the peak in motor plasticity identifies the time window at which we should seek to understand the circuit basis of motor learning plasticity in cortex.

Psychophysical methods are widely used in neuroscience to investigate the quantitative relation between a physical property of the world and its perceptual representation provided by the senses. Recent studies introduced the Generalized Linear Mixed Model (GLMM) to fit the responses of multiple participants in psychophysical experiments. Another approach (two-level approach) requires fitting psychometric functions to each individual participant data using a Generalized Linear Model (GLM), and then testing the hypotheses on the multiple participants by means of a second level analysis. For either options, the implementation of the statistical analysis in R is possible and beneficial. Here, we introduce the package MixedPsy to model and fit psychometric data in R, either with two-level and GLMM approaches. The package, freely available in the CRAN repository, uses different methods for the estimation of Point of Subjective Equivalence (PSE) and Just Noticeable Difference (JND), and provides utilities for immediate visualization and plotting of the fitted results. This manuscript aims to provide researchers with a practical tutorial for implementing a complete analysis pipeline for psychophysical data using MixedPsy and other packages and basic functionalities of the R programming environment.

When neuroscientists record neural activity from the brain, they often conclude that neural responses observed during task performance are indicative of the functional role of the brain area(s) studied. In humans and nonhuman primates, it is often hard to combine recordings and causal techniques within the same experiment, leaving the possibility that the activity recorded may be epiphenomenal rather than reflecting a specific functional role. Currently, the prevalence of epiphenomenal neural activity in the cortex is unknown. To estimate the extent of such activity in primates, we chronically recorded neural activity in the prefrontal cortex of the same monkeys using the same neural implants during the performance of four different cognitive tasks. The four tasks were carefully selected such that only one of them causally depends on the brain area recorded, as demonstrated by previous double dissociation studies. Using the four most common single neuron analyses methods in the field, we found that the prevalence and strength of neural correlates were just as high across all four tasks, including for the three tasks that do not depend on this brain area. These results suggest that the probability of observing epiphenomenal activity in primate cortex is high, which can mislead investigators relying on neural recording or imaging to map brain function. One-Sentence SummaryTremblay, Testard and colleagues show that inferring a brain areas function from neural recordings alone could be misleading.

The sense of touch is not restricted to the body but can also extend to external objects. When we use a hand-held tool to contact an object, we feel the touch on the tool and not in the hand holding the tool. The ability to perceive touch on a tool actually extends along its entire surface, allowing the user to accurately localize where it is touched similarly as they would on their body. While the neural mechanisms underlying the ability to localize touch on the body have been largely investigated, those allowing to localize touch on a tool are still unknown. We aimed to fill this gap by recording the EEG signal of participants while they localized tactile stimuli on a hand-held rod. We focused on oscillatory activity in the alpha (7-14 Hz) and beta (15-30 Hz) range, as they have been previously linked to distinct spatial codes used to localize touch on the body. Beta activity reflects the mapping of touch in skin-based coordinates, whereas alpha activity reflects the mapping of touch in external space. We found that alpha activity was solely modulated by the location of tactile stimuli applied on a hand-held rod. Source reconstruction suggested that this alpha power modulation was localized in a network of fronto-parietal regions previously implicated in higher-order tactile and spatial processing. These findings are the first to implicate alpha oscillations in tool-extended sensing and suggest an important role for processing touch in external space when localizing touch on a tool.

The refinement of gross motor skills, such as locomotion, during development is conserved across vertebrate species. Our previous work demonstrated, in larval zebrafish, that dopaminergic signaling through the dopamine D2-like family of receptors, specifically the dopamine 4 receptor subtype, was necessary for the developmental transformation of behaviorally relevant locomotor activity from an immature to a mature pattern between 3- and 4-days post-fertilization. In this study, we used a complement of tools, including electrophysiology, pharmacology, in vivo calcium imaging, liquid chromatography-mass spectrometry, and quantitative reverse transcription polymerase chain reaction to characterize the functional and molecular mechanisms responsible for this dopaminergic-mediated refinement of spinal locomotor activity. The results demonstrate that the dopamine 4 receptor subtype is both present and functional in, at least, a subset of immature larvae. Further, gene expression of all D2-like receptor subtypes, levels of dopamine, and activity of diencephalic dopaminergic neurons are significantly greater in mature larvae compared to immature larvae. The integration of these results provides evidence for the developmental role of dopaminergic signaling, specifically the dopamine receptor 4 subtype, in the refinement of locomotor activity in vertebrates. Significance StatementThroughout life, all vertebrates acquire and improve gross motor skills. This is particularly evident in the locomotor system where motor output is initially coarse and becomes progressively more refined during development. Previously, we demonstrated that dopaminergic signaling was a factor in the developmental refinement of locomotor activity. However, an understanding of the molecular and functional mechanisms underlying the dopaminergic-mediated refinement of spinal locomotor activity remains elusive. This study demonstrates, in larval zebrafish, that increased expression of all D2-like dopamine receptor subtypes, levels of dopamine, and activity of diencephalic dopaminergic neurons correlate with the refinement of locomotor activity.

Paw preference in rats is widely used to study hemispheric lateralization, but many individual studies are underpowered and employ inconsistent methods, leading to conflicting reports of population-level bias. We conducted a PRISMA-compliant systematic review and meta-analysis to determine whether rats consistently display paw preference at the individual and population levels, and to evaluate the influence of behavioral test type, strain, sex, and age. Studies published between 1930 and 2025 were identified through PubMed, Google Scholar, and ScienceDirect. Data were extracted on strain, age, sex, behavioral paradigm, and paw-preference classification. Random-effects models were used to estimate pooled prevalence, with subgroup analyses for key variables. Forty studies (n = 1,609 rats) met inclusion criteria. At the individual level, 84% of rats displayed consistent paw preference (95% CI: 78-89%, p < 0.0001), demonstrating robust individual-level lateralization. However, population-level analyses showed no universal directional bias, right paw use occurred in 48% of rats (95% CI: 43-54%) and left paw use in 39% (95% CI: 34-44%). Ambidextrous classification thresholds were standardized across studies to ensure comparability. Subgroup analyses indicated modest strain- and test-dependent effects, with Sprague Dawley rats tending toward balanced paw use, while other strains showed slight rightward bias. Skilled-reaching tasks produced slightly stronger asymmetry than the Collins test. Sex- and age-related differences were subtle and inconsistent. Overall, rats exhibit reliable individual-level paw preference without species-wide directional asymmetry, distinguishing them from humans. Standardized testing protocols and balanced cohort designs will enhance reproducibility and translational relevance in lateralization research.

Comparative analyses of locomotor behavior and cellular electrical properties between wild-type and mutant C. elegans are crucial for exploring the gene basis of behaviors and the underlying cellular mechanisms. Although many tools have been developed by research labs and companies, their application is often hindered by implementation difficulties or lack of features specifically suited for C. elegans. Track-A-Worm 2.0 addresses these challenges with three key components: WormTracker, SleepTracker, and Action Potential (AP) Analyzer. WormTracker accurately quantifies a comprehensive set of locomotor and body bending metrics, reliably distinguish between the ventral and dorsal sides, continuously tracks the animal using a motorized stage, and seamlessly integrates external devices, such as a light source for optogenetic stimulation. SleepTracker detects and quantifies sleep-like behavior in freely moving animals. AP Analyzer assesses the resting membrane potential, afterhyperpolarization level, and various AP properties, including threshold, amplitude, mid-peak width, rise and decay times, and maximum and minimum slopes. Importantly, it addresses the challenge of AP threshold quantification posed by the absence of a pre-upstroke inflection point. Track-A-Worm 2.0 is potentially a valuable tool for many C. elegans research labs due to its powerful functionality and ease of implementation.

Hind-limb clasping is a widely used motor assay in rodent models of neurological disease, yet its scoring remains dependent on categorical, observer-defined scales that lack sensitivity to subtle kinematic features. Here, we present an integrated pipeline combining DeepLabCut for markerless pose estimation, SimBA for automated clasping detection, and VECTR-Clasp, an open-source R package, for continuous vector-based geometric analysis of movement during tail suspension. A SimBA random forest classifier trained on DeepLabCut pose tracks achieved automated clasping detection approaching human-level performance, with output closely matching the scoring intersection of two independent raters. Beyond binary classification, VECTR-Clasp extracted continuous circular and geometric measures, including head directionality, movement amplitude, and lateral swing frequency, from the same pose estimation data, revealing previously uncharacterised microphenotypes in Cdkl5-deficient mice. Knockout animals displayed reduced snout displacement, higher directional consistency, and fewer lateral swings compared to wildtype littermates, indicative of constrained or stereotyped movement patterns present even in the absence of overt clasping. These kinematic features were undetectable using traditional categorical scoring. VECTR-Clasp is fully open-source, compatible with standard DeepLabCut outputs, and generalisable to related suspended-mouse paradigms including the tail suspension test, providing a broadly applicable framework for continuous motor phenotyping across preclinical models. MotivationQuantitative assessment of motor behaviour in rodents remains constrained by categorical scoring systems that limit sensitivity, reproducibility, and the ability to detect subtle phenotypes. We developed VECTR-Clasp to address these limitations by introducing a vector-based geometric framework that transforms standard pose estimation outputs into continuous, body-relative kinematic representations. By combining DeepLabCut for pose estimation, SimBA for automated clasping classification, and VECTR-Clasp for downstream geometric analysis, our pipeline moves beyond binary event detection to extract movement features invisible to traditional scoring. Applied to Cdkl5-deficient mice, this integrated approach reveals previously uncharacterised motor microphenotypes, demonstrating that computational behavioural analysis can uncover biologically meaningful phenotypic structure beyond what categorical scales can resolve. HighlightsO_LIDeepLabCut-SimBA pipeline automates hind-limb clasping detection at human-level accuracy C_LIO_LIVECTR-Clasp extracts continuous geometric and circular kinematics from pose estimation data C_LIO_LICdkl5-deficient mice show constrained snout trajectories and reduced lateral swinging during suspension. C_LIO_LIKinematic microphenotypes are detectable in knockout mice even in the absence of overt clasping C_LI

The habenula (Hb), which contains medial and lateral subdivisions (MHb and LHb, respectively), has high intensity mu opioid binding and receptor (MOR) expression, yet the details of MOR localization across these regions remains debated. MHb and LHb participate in largely non-overlapping neural circuits, therefore accurately resolving MOR expression across MHb and LHb is critical for understanding how MOR ligands impact behaviors. Here we utilized in situ hybridization (ISH) and immunocytochemistry (ICC) to systematically map Oprm1 mRNA and MOR protein throughout the habenular complex. We studied both rat and mouse tissue to evaluate conserved expression across two common research species. Importantly, we found mRNA and protein in both the MHb and LHb in both. In rat, 39 {+/-} 3% (MHb) and 21 {+/-} 4% (LHb) of cells expressed Oprm1. These proportions were greater in mouse: 57 {+/-} 1% (MHb) and 32 {+/-} 4% (LHb). Within each species, Oprm1 labeling density per positive cell was greater in MHb compared to LHb (p < 0.0001 for rat and mouse). The highest intensity labeling was localized along the lateral edge of the MHb for both methods. ICC showed MOR localized to fibers and somata in MHb and LHb. In LHb, MOR labeling was most dense in intermediate sections along the anterior-posterior (AP) axis. In rats we also observed higher density labeling in dorsal LHb at intermediate AP levels and medial LHb more posteriorly. These results indicate that both MHb and LHb can contribute to MOR mediated actions through their respective circuits. Key PointsO_LIMu opioid receptor mRNA and protein is expressed in both the medial and lateral habenulae in rat and mouse. C_LIO_LIIn the medial habenula, most mu opioid receptor mRNA and protein was detected along its lateral border. C_LIO_LIAcross samples, Oprm1+ cells in the MHb contained more mRNA puncta per cell compared to lateral habenula cells. C_LI

Amyloid precursor protein (APP) is a type I transmembrane protein that undergoes proteolytic processing to generate amyloid-{beta}, the main component of amyloid plaques found in brains with Alzheimers disease. The proteolytic processing of APP also generates soluble APP alpha (sAPP) which can modulate synaptic transmission and neurite outgrowth through the {gamma}-aminobutyric acid type B receptor (GABABR). Whether GABABR mediates functions of sAPP in other neural cell types such as glia remains unknown. sAPP binds the R1a subunit isoform of GABABR1 which contains two sushi domains absent in R1b. It is unclear whether both GABABR1 isoforms are expressed equally across brain cell types. We determined relative RNA levels of the GABABR1a and 1b isoforms in oligodendrocytes, microglia, endothelial cells, astrocytes, and neurons in adult mice using two approaches. We developed a GABABR1 isoform-specific RNAseq analysis workflow to probe a publicly available dataset. We also isolated five cell types from a single mouse brain and performed RT-qPCR. We show that the GABABR1a and 1b isoforms are differentially expressed among cell types. GABABR1a expression was highest in oligodendrocytes and GABABR1b expression was highest in astrocytes, suggesting that sAPP-mediated GABABR signaling may be most prominent in oligodendrocytes. We also confirmed that APP is expressed in all five cell types and showed that APP RNA levels are highest in oligodendrocytes. Together, our findings uncover cell type-specific expression of GABABR isoforms and highlight oligodendrocytes as a principal cell type for GABABR1a-mediated APP signaling, providing a foundation for future mechanistic studies.