eneuro

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

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.

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

Deletion of the SNORD116 non-coding RNA is associated with the development of Prader-Willi Syndrome (PWS). We and others have identified NHLH2/Nhlh2 as a putative SNORD116/Snord116 target and in this study report that Nhlh2 and Snord116 are co-expressed in forebrain neurons. Nhlh2 mRNA is found predominantly in the nucleus of Snord116+ hypothalamic neurons but is evenly distributed between the nuclear and cytoplasmic compartments in Snord116- neurons. A hypothalamic cell line co-expressing Nhlh2 and Snord116 recapitulates this nuclear partitioning pattern for Nhlh2 only when the putative Snord116 binding site is present in the 3 untranslated region of the Nhlh2 mRNA. In a PWS mouse model (Snord116del), Nhlh2 mRNA levels are unchanged in Pomc neurons but are reduced and primarily cytoplasmic in lateral hypothalamic neurons. These findings demonstrate that Snord116 regulates mRNA levels via mRNA partitioning, fundamentally altering our understanding of gene expression networks in PWS. HighlightsO_LISnord116 snoRNA and its mRNA target Nhlh2 are co-expressed in forebrain neurons. C_LIO_LINhlh2 mRNA is predominantly nuclear co-localized in hypothalamic Snord116+ neurons. C_LIO_LIA putative Snord116 binding site is within the region required for cellular partitioning. C_LIO_LINhlh2 partitioning is lost in Snord116del mice, a model of Prader-Willi Syndrome. C_LI

Fiber photometry measures neural activity in vivo from genetically encoded indicators. Recordings generate large datasets that require extensive preprocessing and robust statistical methods for meaningful interpretation. However, existing analysis tools demand programming expertise, limiting accessibility. Here we describe FPmotion, a comprehensive, user-friendly software platform for batchwise processing, integration, and statistical analysis of fiber photometry data with or without accompanying behavioral information. FPmotion performs filtering, isosbestic regression, {Delta}F/F computation, detrending, and z-scoring. It also extracts detailed peak properties at whole-file, block-level, and individual-peak resolution. When behavioral data is provided, FPmotion automatically identifies behavioral bouts, computes bout-level statistics, performs peri-event signal extraction, and supports multi-group comparisons through ANOVA- and LMEM-based statistical frameworks. All analyses generate publication-ready figures and structured CSV outputs suitable for downstream workflows. FPmotion also introduces a dedicated alignment module for peri-event signal analysis. This module applies dynamic time warping followed by barycenter averaging to realign peri-event traces while preserving the behavioral time anchor, producing cleaner and more temporally coherent peri-event motifs. We demonstrate FPmotions capabilities using the dlight1.1 sensor to measure dopamine responses from ventral striatum in behaving mice before and after amphetamine treatment. Together, FPmotion offers a fully automated framework for FP data analysis that improves interpretability and accessibility while reducing analysis time substantially.

Auditory neurons, in the midbrain and beyond, detect changes in repeating acoustic patterns. Most studies focus on mechanisms underlying such sensitivity and adaptation to regularity. However, regular sound patterns are crucial in social communication, stream-segregation and grouping in different species including humans. Thus, we address cortical selectivity to periodic or aperiodic sound sequences with multiple stimulus attributes. With single unit electrophysiology and two-photon calcium imaging in anesthetized and awake mouse auditory cortex, we observe subpopulations of neurons selective to periodicity or aperiodicity that lack generalization across period-length, frequency-content or inter-token-interval durations. Comparing results with or without inter-token-interval spiking activity, shows its profound role underlying selectivity to periodic or aperiodic sequences. The whole population average rate for periodic and aperiodic stimuli is identical but not following each period and stimulus-off-period. Hence, inter-token-interval activity, post each period increases during the sequence providing information on selectivity and a prediction like signal. HighlightsO_LIContrary to common view, subpopulations of neurons in the auditory cortex are selective to repetitive sound patterns or periodic sound sequences and another to aperiodic ones. C_LIO_LINeither population of neurons above generalizes their selectivity across properties of tokens of the sequences. C_LIO_LINeural activity during the inter-token interval plays an important role in enhancing the observed selectivity. C_LIO_LIPost period or pre-subsequent period activity builds up during the stimulus providing a prediction like signal for periodic sequences with respect to aperiodic sequences. C_LI

Previously, electrophysiological differences between subpopulations of midbrain dopamine (DA) neurons were identified based on projection targets, including distinct responses to hyperpolarization and in the regularity of pacemaking. Here we explored single-compartment models of three subpopulations of DA neurons, projecting to medial shell of the nucleus accumbens (VTA-mNAcc), dorsomedial striatum (SNc-DMS) or dorsolateral striatum (SNc-DLS). We reduced the dimensionality to a phase plane consisting of membrane potential and one slow variable, either total slow potassium conductance or Kv4 channel inactivation. Nullclines are curves on which the rate of change of each variable is zero, given the value of the other variable. The voltage nullclines had three branches: upper spiking, unstable middle, and lower quiescent branch. Recruitment of Kv4 channels by the more prominent after-hyperpolarizing potential (AHP) in the DA-DMS and DA-DLS models channels stabilized pacemaking by creating a restorative moving fixed point along the quiescent branch. The slow inactivation of KV4 channels dominated and regularized the dynamics during the interspike interval; a dominant slow process may be a general mechanisn for stable regular pacemaking in a frequency range between 1-10 Hz. In contrast, the smaller AHP in VTA-mNAcc models prevented recruitment of this Kv4-mediated moving fixed point, which increased the sensitivity to synaptic inputs. On rebound from hyperpolarization the ability to produce robust ramps reverses between the DA neurons: now VTA-mNAcc projecting DA models fully recruited Kv4 channels and produced stable ramp-like pauses, whereas SNc-DLS projecting cells recruited significant regenerative inward CaV3 channels that overwhelmed Kv4 channels and produced rebound bursts. Author SummaryMidbraim dopamine (DA) neurons in the mammalian midbrain are linked to motivation, control of voluntary movement initiation, and reward-based learning. Their dysfunction is implicated in major disorders like Parkinsons disease, schizophrenia or substance use disorders. Firing patters like bursts or pauses in most DA subpopulations are thought to signal better or worse than expected outcomes. Here we use dynamic systems analysis to capture how functional diversity of DA neurons of their intrinsic properties results in differences of synaptic input integration leading to the generation of burst and pause patterns of electrical activity.

When reaching to a foveated target, peripheral vision of the hand can be used to make rapid, automatic adjustments to the ongoing reach movement, with the feedback gain being sensitive to features of the task and environment. These rapid corrective responses are also observed when gaze is directed to a stationary gaze target located away from the reach target. In everyday contexts, reaching often occurs concurrently with other visual or visuomotor tasks, such as tracking a moving target. Yet it remains unclear whether engaging in such tasks affects the use of peripheral vision for hand guidance. Here, we compare rapid visuomotor corrective responses to visual perturbations during fixation and smooth pursuit, and test whether pursuit-related and reach-related visuomotor processes operate independently or compete for shared visual resources. Participants either fixated a stationary target or tracked a moving target while reaching toward a spatially dissociated reach target. During the reach, the visual representation of the hand was perturbed, requiring rapid corrective responses. We found that neither the onset nor the gain of reach corrections was modulated by gaze-task demands. Moreover, response gains were strongly correlated across tasks, indicating consistent individual response profiles that were independent of the gaze condition. Despite modest increases in position error and decreases in gain, participants largely sustained engagement with the visual tasks during target reaching. These findings demonstrate that smooth pursuit and reach-related visuomotor processing can operate in parallel without mutual interference, suggesting a functional independence between them. NEW & NOTEWORTHYIn everyday life, reaching to an object can occur while the eyes are engaged in competing visual tasks. We show that engaging in smooth pursuit eye movements does not disrupt rapid visuomotor corrections during reaching. The onset and gain of corrective responses following perturbation were unchanged by gaze-task demands and were consistent across individuals. These findings demonstrate that pursuit and reach-related visuomotor processes can operate in parallel, supporting functional independence between these systems.

Lick microstructure is a term used in behavioural neuroscience to describe the information that can be obtained from a detailed examination of rodent drinking behaviour. Rather than simply recording total intake (volume consumed), lick microstructure examines how licks are grouped, and the spacing of these groups of licks. This type of analysis can provide important insights into why an animal is drinking, for example, whether it is influenced by taste or affected by consequences of consumption (e.g., feeling "full"). Here we present a software package, lickcalc, that allows detailed microstructural analysis of licking patterns. The software is browser-based and is hosted at https://lickcalc.uit.no or the repository can be downloaded and installed locally. Lick timestamps can be loaded from a variety of formats and different analysis and plotting options allow quality control of data and determining critical parameters for microstructural analysis number and size of lick bursts. Data can be divided into epochs for detailed examination of changes across session. Batch processing and custom configurations are supported. In this manuscript, we demonstrate use of the functions exposed by lickcalc by analysing data comparing lick patterns between mice on a protein-restricted and control (non-restricted diet). We show that lickcalc allows quality control of the data and uncovering of subtle differences in lick behaviour that are not apparent when just considering the total number of licks. This software makes microstructural analysis accessible to any researchers who wish to employ it while providing sophisticated analyses with high scientific value.

Fast-conducting A{beta} slowly adapting type I low threshold mechanoreceptors (SAI-LTMRs) and A{beta} rapidly adapting type I (RAI-) LTMRs are critical for discriminative touch from glabrous skin. Recent studies used mouse genetic loss-of-function and optogenetic gain-of-function manipulations to determine that signals from these A{beta} LTMRs are integrated subcortically to build the cortical representation of touch. However, the precise influence of each subtype on downstream responses is unclear, in part because previous manipulations lacked the capacity to reproduce physiological firing patterns in individual A{beta} LTMR subtypes. Here, we took advantage of a fast variant of channelrhodopsin, CatCh, which enabled us to generate physiological spiking rates and patterns in either subtype while monitoring responses in mouse primary somatosensory cortex (S1). Doing so revealed that propagation of steady-state signals from sustained responses of A{beta} SAI-LTMRs to neural activity (spiking and local field potential) in S1 is dependent on synchronous activation of multiple A{beta} SAI-LTMRs. Asynchronous activation of the same A{beta} SAI-LTMRs rarely produced sustained responses in S1 measured at the level of single unit spiking as well as local field potentials. This suggests that the irregular firing patterns of A{beta} SAI-LTMRs during static indentations contribute to the preponderance of transient cortical responses. By contrast, both synchronous and asynchronous activation of multiple A{beta} RAI-LTMRs resulted in robust S1 responses. Overall, the temporal patterning and synchrony of activity within A{beta} LTMR subtypes govern how well their signals propagate through the ascending somatosensory system. Key PointsO_LICatCh, a sensitive and fast channelrhodopsin variant, enables pulsed-light generation of physiological spiking patterns in A{beta} low threshold mechanoreceptor (LTMR) subtypes. C_LIO_LIThe synchrony of multiple mechanoreceptors within a subtype controls the extent to which their signals influence activity in primary somatosensory cortex. C_LIO_LIA{beta} rapidly adapting type I (RAI-) LTMRs drive stronger cortical responses than A{beta} slowly adapting type I (SAI-) LTMRs, especially when considering asynchronous activation of A{beta} LTMRs within each subtype. C_LI

In natural environments, animals must effectively maneuver around obstacles to reach goals such as food or shelter. Recent work has demonstrated that laboratory mice use vision for naturalistic behavior such as prey capture, escape, and distance estimation. However, it is unknown to what extent mice use vision relative to other senses such as touch for obstacle avoidance, a critical natural behavior. In this study we developed an obstacle avoidance task in freely moving mice to investigate how vision is used to guide paths around an obstacle obstructing a goal. We found that mice clearly use vision to avoid an obstacle, steering around the obstacle at distances where tactile information isnt available. By comparing trajectories for mice performing obstacle avoidance in the light versus the dark, we found that vision contributes to more spatially efficient trajectories and paths directed to the open edge of the obstacle. When vision is available, mice make large orienting movements towards the opening of the obstacle at about 10 cm from its edge, suggesting that mice are actively using visual information to direct these movements. Finally, by occluding one eye, we found that mice were still able to avoid obstacles with primarily monocular information. Taken together, these results demonstrate that laboratory mice use vision to avoid an obstacle, taking directed paths that are initiated by large orienting movements. In addition to demonstrating the visual behavioral capabilities of the mouse, this paradigm can serve as a foundation to study the neural circuits that mediate visually guided orienting and locomotion. HighlightsO_LIWe developed a simple obstacle avoidance task for freely moving mice that requires minimal training C_LIO_LIVision is necessary for efficient and directed paths around an obstacle C_LIO_LIMice steer around obstacles by performing directed head movements towards clear paths C_LIO_LIMice do not require binocular vision for obstacle avoidance C_LI

Presynaptic homeostatic plasticity (PHP) is a potent form of homeostatic plasticity that has been documented at synapses as diverse as the glutamatergic Drosophila neuromuscular junction (NMJ), cholinergic mammalian NMJ (including human), and glutamatergic synapses in the mammalian brain. Published experimental evidence in favor of PHP in adult hippocampus and cerebellum includes patch-clamp electrophysiology, presynaptic capacitance measurement, calcium imaging, optical reporters of vesicle release and correlated three-dimensional electron microscopy. These studies are grounded in newly optimized experimental protocols that differ substantively from those typically used to study activity-dependent plasticity in neonatal and juvenile slice preparations. Here, we elaborate and extend our assays and methodologies for the study of PHP in the adult mammalian brain. Our assays are designed to optimize synapse, cell and tissue health and minimize the incorporation of unintended adverse experimental conditions that may interfere with the induction and/or expression of PHP. In addition, we provide benchmark criteria for assessment of cell health, necessary for analysis of PHP and, in so doing, advance our understanding of postsynaptic conditions necessary for PHP induction in the adult brain. Our data underscore why PHP may have been previously overlooked, inclusive of a recent manuscript challenging the robust expression of PHP in the mammalian brain (Dou et al., 2026 BioRxiv [preprint]).

In vertebrate peripheral nerves, damaged axons can regrow after injury, but the outcomes of regeneration are variable and often incomplete. Schwann cells in injured nerves are important for repair, but their actions at different positions and stages of nerve repair are not well understood. We have investigated the roles of Schwann cells in a larval zebrafish nerve injury model, in which nerves are visible in living animals during development, the initial injury response, and regrowth of the transected axons. After mechanical injury, distal Schwann cells adopt a repair phenotype characterized by changes in marker expression, elongation, and ability to guide axons across the injury site. In contrast, proximal Schwann cells are not sufficient to guide axons across the injury site, and they associate with axons that regrow along aberrant paths. In erbb2 mutants lacking Schwann cells, developmental axon growth is normal, but after transection, axonal regrowth is greatly slowed and often misdirected. By examining animals with nerves partially populated by Schwann cells, we find that axons can regrow through regions devoid of Schwann cells, provided that at least one distal Schwann cell is at the injury site. Timelapse imaging reveals that distal Schwann cells extend processes toward the injury site, which contact and guide axons regrowing from the proximal nerve stump. In irf8 mutants lacking macrophages, debris from transected axons is cleared on schedule, and axonal regrowth is normal. Our studies demonstrate that Schwann cells immediately distal to the injury site have a unique and essential role in axonal regrowth. Main PointsO_LIAfter nerve transection in larval zebrafish, proximal and distal Schwann cells have distinct functions at injury site C_LIO_LIA single distal repair Schwann cell is sufficient for axonal regrowth C_LIO_LIAxonal regrowth is normal in mutants without macrophages C_LI

BackgroundHow does neuronal activity change as an animal transitions from being awake to a state of general anesthesia? Previous studies used C. elegans to investigate awake and anesthetized states, emergence from anesthesia, and to establish metrics characterizing how system-wide neuronal dynamics differ under these conditions. This study employs a new technique to image pan-neuronal activity in C. elegans continuously during induction of anesthesia with isoflurane. MethodsC. elegans worms expressing pan-neuronal nuclear RFP and cytosolic GCaMP6s were imaged with light sheet microscopy to measure single cell activity in the majority of neurons in the animals head during induction via isoflurane exposure. Stable concentrations of isoflurane were maintained throughout the experiment by measured flow vaporization of isoflurane into a specially designed gas enclosure compatible with the imaging system. Building on our previous work investigating emergence from anesthesia, we analyzed ensemble neuronal activity, spectrograms of frequency over time, and metrics of information flow between neurons. ResultsInduction of isoflurane anesthesia caused a progressive reduction in neuronal activity over the course of 40 minutes. Spectrograms indicated a loss of bulk signal power across all frequencies, notably in low frequencies too. State Decoupling and Internal Predictability were among the most useful metrics for discriminating the anesthetized state, demonstrating induction kinetics that are the inverse of emergence. However, each animal does not arrive at the anesthetized state at the same time; response times are highly individualized. ConclusionsInformation metrics of neurodynamic activity demonstrate that isoflurane induction results in a gradual increase in neuronal disconnection and disorganization. Thus, at the level of individual neuron connectivity and system dynamics, the induction of anesthesia in C. elegans nematodes is in essence the reverse of emergence. Induction however occurs more rapidly and shows marked variability between individuals. Future genetic studies will show which molecular targets define sensitivity to volatile anesthetics like isoflurane. Summary StatementIsoflurane-induced unconsciousness is a common phenomenon across species. Does the induction of anesthesia arise by distinct state transitions, or through gradual changes in system dynamics when activity is measured at the level of individual neurons?

Humans perform a variety of complex hand movements to manipulate objects, requiring precise control of changing forces. Understanding the role of sensorimotor cortex and the cortical dynamics underlying these actions is crucial for developing interventions that restore dexterous hand function after injury or disease. In this study, two individuals with tetraplegia resulting from cervical spinal cord injury attempted a series of isometric grasps. Neural activity was recorded from the motor and somatosensory cortices using intracortical microelectrode arrays while participants attempted to exert a static force or to ramp force up and down. Despite their inability to execute movement, and with limited afferent input, the spiking activity in motor and somatosensory cortex was modulated with the task. Within the neural response we identified independent neural modes - distinct patterns of population-level neural activity - that were informative about both the timing and magnitude of the force. Moreover, distinct neural modes were observed during static and dynamic grasping conditions, suggesting independent control schemes for maintaining and changing forces. These modes were related to phases of the task, including the onset, offset, holding periods, as well as phases of increasing and decreasing force. These results will inform the design of intracortical brain-computer interface (iBCI) systems that can leverage these naturally occurring patterns of grasp and force control to restore dexterous hand function. Significance StatementRestoring dexterous hand function after injury remains a major challenge, partly due to an incomplete understanding of the cortical dynamics underlying grasping and force control. In this study, we investigated neural activity within the motor and somatosensory cortices of individuals with tetraplegia attempting to perform grasps to different target forces with varying temporal profiles. We identified distinct neural modes modulated during specific phases of grasp that encode force information throughout the task. These findings suggest that brain-computer interfaces could leverage these native neural modes to restore grasping and force modulation.

Kisspeptin neurons in the rostral hypothalamus are hypothesized to initiate preovulatory gonadotropin-releasing hormone (GnRH) surges by causing estradiol-dependent activation of GnRH neuron action potential firing and subsequent GnRH release. To determine if estradiol or ovarian cycle stage modulates functional connectivity in this circuit, we used optogenetics to photostimulate anteroventral-periventricular (AVPV) area kisspeptin neurons while recording electrical activity and/or evoked synaptic currents from preoptic area GnRH neurons in acutely-prepared mouse brain slices. Slices were prepared from mice in multiple hormonal states, including 2-days post ovariectomy (OVX) and OVX plus estradiol during the morning or afternoon, diestrus, proestrus and 1-week post OVX, and 6-weeks post OVX with or without 1 week of estradiol replacement. Photostimulation induced a sustained, frequency-dependent increase in GnRH neuron firing rate. This neuromodulatory-typical response was not different in diestrous vs proestrous mice but was blunted in 1-week OVX mice, suggesting ovarian steroids amplify this response. Neuromodulatory responses were infrequent in 6-week OVX mice even with 1-week of estradiol treatment. A minority of GnRH neurons exhibited a substantial and near-immediate increase in firing rate typical of fast synaptic transmission. Monosynaptic connectivity was low and stable across the hormone states tested and mediated by GABA. Interestingly, evidence of a monosynaptic connection was not a requirement for GnRH neurons to exhibit a sustained increase in firing rate, suggesting non-synaptic or volume transmission occurs in this system. Synaptic connectivity did, however, amplify the increase in firing rate observed in GnRH neurons from proestrous mice, indicating proestrous hormonal conditions can amplify this response. Significance statementOvulation is initiated by central positive feedback effects of estradiol stimulating a surge of gonadotropin-releasing hormone (GnRH) release. Estradiol feedback is conveyed to GnRH neurons by afferents expressing estrogen receptor alpha, including kisspeptin-expressing neurons in the anteroventral periventricular (AVPV) area. To determine if endocrine milieu modulates functional interactions between AVPV kisspeptin and GnRH neurons, optogenetics was used to stimulate AVPV kisspeptin neurons while recording GnRH neuron spiking activity or synaptic currents in brain slices from ovariectomized, estradiol-treated, and ovary-intact mice. Stimulation (20Hz) increased GnRH neuron firing rate in all hormone conditions. This effect was stronger during proestrus and was further increased in GnRH neurons receiving fast-synaptic transmission. A synaptic connection was not required, however, suggesting volume transmission occurs.

People encounter AI voices daily. Existing behavioral studies suggest listeners rely on prosodic cues such as intonation and expressiveness to detect audio deepfakes, reporting that AI voices sound prosodically less rich than human voices. To test whether prosodic processing drives deepfake discrimination in the neural time course of voice processing, we recorded electroencephalographic (EEG) data while participants listened to human and AI-generated speakers producing utterances in confident vs. doubtful prosody (tone of voice), with attention directed toward memorizing speaker names. We used voice cloning to control for speaker identity confounds between human and AI voices. Multivariate pattern analysis revealed that neural discrimination of human vs. AI voices emerged rapidly regardless of prosody (confident: 176 ms; doubtful: 134 ms), substantially preceding prosody discrimination (confident vs. doubtful within human voices: 2066 ms; within AI voices: 1366 ms). Acoustic analysis confirmed that prosodic distinctions became classifiable only at utterance offset (90% normalized duration), converging with neural evidence that prosody requires near-complete temporal integration. This temporal dissociation between rapid voice source discrimination and late-emerging prosody decoding suggests that prosody plays a smaller role in audio deepfake detection than listeners retrospectively report. Representational similarity analysis further revealed that spectral envelope features (mel-frequency cepstral coefficients; MFCC), rather than the visually salient high-frequency energy differences, drove neural human-AI discrimination, with MFCCs earliest independent contribution (228 ms) closely following the MVPA decoding onset (134-176 ms). Future studies may manipulate specific acoustic components to establish the causal sources of this rapid and sustained neural discrimination. Significance StatementPeople encounter AI voices daily, in phone calls, navigation apps, supermarket checkouts, and subway announcements. Using electroencephalography, we show that the human brain automatically and rapidly distinguishes everyday AI voices from human speech, even without conscious attention to voice source. Although people may attribute this ability to AI voices sounding monotone or prosodically unnatural, the brain relies on subtler acoustic signatures, enabling discrimination before prosodic information becomes available. Attempts to identify the specific acoustic features driving this neural detection were inconclusive, pointing to the need for future causal investigations. We encourage engineers and policymakers to ensure AI voices remain perceptually detectable, as increasingly humanlike AI voices could cognitively disadvantage the general public if they become indistinguishable from human speech.

A central question in neuroscience is how neural processing generates or encodes behavior. Caenorhabditis elegans is well suited to addressing this question, given its compact nervous system and near-complete structural connectome. Despite this, findings from previous studies remain inconclusive. While some have shown that the connectome can robustly encode specific behaviors such as locomotion, others report that functional connectivity can be reconfigured across behaviors. We aim to understand the relationship between structural connectivity, functional connectivity and biological behavior in silico by using an experimentally motivated computational model leveraging the structural connectome. Stimulation of specific neurons in the model induces oscillatory neural responses, enabling us to infer neuronal functional connectivity. Functional connectivity is found to be stronger among some neurons, allowing us to identify functional communities. We find that electrical synapses play a critical role in determining functional communities, and the resulting mesoscale functional architecture is predominantly gap junctionally assortative. Furthermore, comparison with behavioral circuits shows that locomotion circuits are largely segregated into distinct functional communities while other circuits are more distributed across multiple functional communities. We also observe that stimulation of neurons belonging to these distributed circuits elicits a more synchronized neuronal response compared to stimulation of neurons within the more segregated circuits. This is consistent with the presence of behavioral patterns that originate in one circuit and terminate in another (e.g., chemosensation leading to locomotion), such that stimulation of one circuit can activate the other and eventually result in a synchronized response. We also find a large repertoire of chimera-like synchronization patterns upon stimulation of certain behavioral circuits (chemosensation, mechanosensation) indicating high dynamical flexibility. Overall, our results demonstrate that while certain behaviors are governed by functionally segregated circuits, others emerge from the synchronization of multiple functional communities, which are, to begin with, influenced by the underlying structural connectivity. Author summaryAnimals constantly transform sensory inputs into actions, but it is still unclear how this mapping from neural activity to behavior is implemented in a real nervous system. Caenorhabditis elegans offers a unique testbed for this question because its entire wiring diagram is nearly completely mapped. Yet, previous works have reached mixed conclusions about how well this anatomical circuit diagram predicts actual patterns of activity and behavior. Here, we use a biologically inspired computational model of the C. elegans nervous system to bridge this gap between structure, function, and behavior. By virtually stimulating individual neurons and observing the resulting network-wide oscillations, we infer how strongly different pairs and groups of neurons interact in functional terms. We then use network analysis tools to identify groups of neurons that tend to co-activate, and relate these functional communities to known behavioral circuits for locomotion and sensory processing. We find that gap junctions play a key role in shaping functional communities, and that locomotion-related neurons are more functionally segregated than neurons involved in other behaviors, which are more functionally distributed. Our results suggest that some behaviors rely on specialized, functionally isolated circuits, whereas others emerge from the coordinated activity of multiple functional communities.

To compensate for self-generated movement-induced postural disturbances, the brain generates anticipatory postural adjustments (APA), ensuring smooth, coordinated actions. APA development continues into late adolescence, yet the specific pathways and mechanisms that remain immature in children are poorly understood. We studied APA mechanisms in 24 children (7-12 years old) using magnetoencephalography (MEG) while they performed the naturalistic bimanual load-lifting task (BLLT). In the BLLT, participants lift a load placed on one forearm with the contralateral hand while keeping the postural forearm horizontal, as if lifting a glass from a tray. To counteract forearm deflection caused by unloading, the brain generates APAs, which involve anticipatory inhibition of the postural Biceps brachii. We found that stronger anticipatory Biceps brachii inhibition was associated with reduced excitability, as indexed by high-gamma (90-130 Hz) suppression, and increased high-beta power (19-29 Hz) in the contralateral Supplementary Motor Area (SMA). Analysis of transient beta events revealed two functionally distinct burst types: (1) 19-24 Hz bursts: time-locked to immediate high-gamma suppression correlated with 26-28 Hz beta power; predicted stronger muscle inhibition and received directed input from middle frontal cortex and precentral gyrus; (2) 24-29 Hz bursts: linked to delayed ([~]100 ms) high-gamma suppression correlated with 8 Hz alpha power; predicted earlier and prolonged muscle inhibition and better forearm stabilization, but did not show directional influence from other regions. Results on anticipatory inhibition-related beta bursts replicated mechanisms reported in adults, suggesting that the efferent pathways and transient inhibitory processes underlying APA may already be mature in children. In contrast, higher-frequency beta bursts revealed a child-specific, complementary APA mechanism that may compensate for imprecise anticipatory inhibition. These results reveal two oscillatory mechanisms supporting APA in children and indicate that beta bursts may reflect both immediate cortical inhibition linked to muscle control and indirect alpha-mediated inhibition likely compensating for forearm instability.

Understanding neuron subclasses and their functional consequences can contribute to understanding brain circuits. A scheme long used to classify GABAergic neurons in the neocortex is based on expression of three calcium-binding proteins (CBPs): parvalbumin (PV), calbindin D-28K (CB), and calretinin (CR). Because CB and CR are frequently co-expressed by individual neurons in rodents, this scheme has been replaced by one based on PV and two signaling peptides: somatostatin (SST) and vasoactive intestinal peptide (VIP). In macaques, however, CBPs are generally not co-expressed, and so their use has persisted despite suggestions that the underlying populations are not, in fact, entirely GABAergic. We set out to quantitatively evaluate CBPs as a classification scheme for GABAergic neurons in early and mid-level visual regions in macaque cortex. Combining immunohistochemistry and in situ hybridization, we find that up to half of neurons expressing CBPs are likely not GABAergic. Furthermore, contrary to what has been previously suggested, the GABAergic subpopulations cannot be distinguished based on staining intensity. Thus, the CBP-based classification scheme is not valid, at least as it has traditionally been used. Instead, we find support for co-labeling CB and CR neurons with SST and VIP, an approach that can identify GABAergic subpopulations within the CBP classes; or simply adopting the PV/SST/VIP scheme. We discuss the functional implications of expressing these various cell type markers, and how consideration of marker functions can support proper selection of a classification scheme for a given experimental purpose.