Biology subjects

Padilla-Coreano, N.

Publications and source records attributed to Padilla-Coreano, N..

7 recordsLinked to original sources

bioRxiv2026

Topological data analysis captures complex behavioral dynamics during naturalistic social interaction between domestic ferrets

Capturing naturalistic behavioral dynamics is essential for understanding social interaction in ecologically valid settings. Existing investigations of naturalistic social interaction rely on time-aggregated analysis methods better suited for task-based experiments, which lose the complex, moment-to-moment dynamics exhibited in naturalistic settings. The emerging field of topological data analysis (TDA) provides new tools to characterize fine-grained dynamics in time-series data that cannot be captured by time-averaged methods. The present work utilizes Temporal Mapper, a recently developed TDA specifically tailored to analyzing dynamical systems. Temporal Mapper characterizes complex temporal dynamics as transition networks, where nodes are stable states and edges are transitions between states. Originally designed for human neural time series analysis, here we demonstrate the utility of Temporal Mapper to capture rich animal postural dynamics during naturalistic social interaction. We utilized an existing dataset with 12 video recording sessions of two domestic ferrets (Mustela putorius furo) during naturalistic interaction and tracked the postures of animals during social interaction. Ferrets were chosen due to their strong social-cognitive skills and rich postural dynamics for investigating social behavior via posture estimation. Temporal Mapper was then used to represent the postural dynamics as transition networks for each recording session. Here, we found that posture states are significantly smaller and more widespread during active social interaction compared to non-social activities. Additionally, the number of sequential postural states before transitioning to new behaviors is more consistent during active social interaction than non-social activities. Together, our findings suggest that social activity has a broad range of unstable postural states arranged in consistent sequences. Our method, Temporal Mapper, allows for network structure analysis of complex naturalistic data, applicable for characterizing rich dynamics in different species, scales, and paradigms.

neuroscience↗

bioRxiv2026

Mouse strain and network-level activity differences underlie social decision-making

Adaptive social behavior requires balancing self-interest with the welfare of others, a core axiom of social decision-making that determines whether actions are selfish or prosocial. Although the medial prefrontal cortex (mPFC) has been implicated in prosocial behavior, the broader cortical-subcortical networks that arbitrate between selfish and prosocial actions remain poorly understood. Moreover, most studies of social decision-making (at both the single-region and circuit levels) have focused on inbred C57BL/6 mice, leaving unclear whether similar neural mechanisms operate across genetically diverse populations. Here, we combined a social decision-making task with c-Fos mapping to examine activity across distributed cortical and subcortical regions in inbred C57BL/6 and outbred CD1 male mice during prosocial and selfish choices. We found that CD1 mice exhibited a stronger bias toward selfish behavior, whereas C57BL/6 mice were more prosocial. This behavioral divergence was associated with elevated c-Fos activity in the mPFC and nucleus accumbens core (NAcC) in CD1 mice compared with C57BL/6 mice, and mPFC activity positively correlated with selfish choice bias. At the network level, social decision-making selectively recruited coordinated activity among the distinct mPFC subregions, ventral tegmental area (VTA), and NAcC. Importantly, prosocial and selfish individuals recruited distinct prefrontal-subcortical network configurations during social decision-making. Together, these findings identify distributed cortical-subcortical network dynamics underlying social choice bias and reveal strain-dependent differences in the neural architecture supporting prosocial and selfish behavior. Significant statementSocial decisions require weighing personal benefit against the welfare of others, yet the neural circuits that bias individuals toward selfish versus prosocial choices remain poorly understood. Here, we show that two mouse strains with opposing social preferences recruit distinct cortical and subcortical network configurations during social choice, despite performing the same task. Rather than reflecting differences in single brain regions, social decision-making engaged coordinated activity across a prefrontal-striatal-midbrain circuit, with prosocial and selfish individuals recruiting different versions of this network. These findings reveal that social choice bias is encoded at the level of distributed circuit organization and that genetic background shapes how the brain implements social decisions.

neuroscience↗

bioRxiv2025

Separable Dorsal Raphe Dopamine Projections mediate the Facets of Loneliness-like state

Affiliative social connections facilitate well-being and survival in numerous species. Engaging in social interactions requires positive or negative motivational drive, elicited through coordinated activity across neural circuits. However, the identity, interconnectivity, and functional encoding of social information within these circuits remains poorly understood. Here, we focus on downstream projections of dorsal raphe nucleus (DRN) dopamine neurons (DRNDAT), which we previously implicated in social motivation alongside an aversive affective state. We show that three prominent DRNDAT projections - to the bed nucleus of the stria terminalis (BNST), central amygdala (CeA), and posterior basolateral amygdala (BLP) - play separable roles in behavior, despite substantial collateralization. Photoactivation of the DRNDAT-CeA projection promoted social behavior and photostimulation of the DRNDAT-BNST projection promoted exploratory behavior, while the DRNDAT-BLP projection supported place avoidance, suggesting a negative affective state. Downstream regions showed diverse receptor expression, poising DRNDAT neurons to act through dopamine, neuropeptide, and glutamate transmission. Furthermore, we show ex vivo that the effect of DRNDAT photostimulation on downstream neuron excitability depended on region and baseline cell properties, resulting in excitatory responses in BNST cells and diverse responses in CeA and BLP. Finally, in vivo microendoscopic cellular-resolution recordings in the CeA with DRNDAT photostimulation revealed a correlation between social behavior and neurons excited by social stimuli- suggesting that increased dopamine tone may recruit different CeA neurons to social ensembles. Collectively, these circuit features may facilitate a coordinated, but flexible, response in the presence of social stimuli that can be flexibly guided based on the internal social homeostatic need state of the individual.

neuroscience↗

bioRxiv2023

Mind the gap: A systematic review and meta-analysisof how social memory is studied

Social recognition is crucial for survival in social species, and necessary for group living, selective reproduction, pair bonding, and dominance hierarchies. Mice and rats are the most commonly used animal models in social memory research, however current paradigms do not account for the complex social dynamics they exhibit in the wild. To assess the range of social memories being studied, we conducted a systematic analysis of neuroscience articles testing the social memory of mice and rats published within the past two decades and analyzed their methods. Our results show that despite these rodents rich social memory capabilities, the majority of social recognition papers explore short-term memories and short-term familiarity levels with minimal exposure between subject and familiar stimuli - a narrow type of social memory. We have identified several key areas currently understudied or underrepresented: kin relationships, mates, social ranks, sex variabilities, and the effects of aging. Additionally, reporting on social stimulus variables such as housing history, strain, and age, is limited, which may impede reproducibility. Overall, our data highlight large gaps in the diversity of social memories studied and the effects social variables have on social memory mechanisms.

neuroscience↗

bioRxiv2022

Disentangled multi-subject and social behavioral representations through a constrained subspace variational autoencoder (CS-VAE)

Effectively modeling and quantifying behavior is essential for our understanding of the brain. Modeling behavior in naturalistic settings in social and multi-subject tasks remains a significant challenge. Modeling the behavior of different subjects performing the same task requires partitioning the behavioral data into features that are common across subjects, and others that are distinct to each subject. Modeling social interactions between multiple individuals in a freely-moving setting requires disentangling effects due to the individual as compared to social investigations. To achieve flexible disentanglement of behavior into interpretable latent variables with individual and across-subject or social components, we build on a semi-supervised approach to partition the behavioral subspace, and propose a novel regularization based on the Cauchy-Schwarz divergence to the model. Our model, known as the constrained subspace variational autoencoder (CS-VAE), successfully models distinct features of the behavioral videos across subjects, as well as continuously varying differences in social behavior. Our approach vastly facilitates the analysis of the resulting latent variables in downstream tasks such as uncovering disentangled behavioral motifs, the efficient decoding of a novel subjects behavior, and provides an understanding of how similarly different animals perform innate behaviors.

neuroscience↗

bioRxiv2020

AlphaTracker: A Multi-Animal Tracking and Behavioral Analysis Tool

The advancement of behavioral analysis in neuroscience has been aided by the development of computational tools1,2. Specifically, computer vision algorithms have emerged as a powerful tool to elevate behavioral research3,4. Yet fully automatic analysis of social behavior remains challenging in two ways. First, existing tools to track and analyze behavior often focus on single animals, not multiple, interacting animals. Second, many available tools are not developed for novice users and require programming experience to run. Here, we unveil a computer vision pipeline called AlphaTracker, which requires minimal hardware requirements and produces reliable tracking of multiple unmarked animals. An easy-to-use user interface further enables manual inspection and curation of results. We demonstrate the practical, real-time advantages of AlphaTracker through the study of multiple, socially-interacting mice.

animal behavior and cognition↗

bioRxiv2017

Selective Inhibitory Control Of Pyramidal Neuron Ensembles And Cortical Subnetworks By Chandelier Cells

The neocortex comprises multiple information processing streams mediated by subsets of glutamatergic pyramidal cells (PCs) that receive diverse inputs and project to distinct targets. How GABAergic interneurons regulate the segregation and communication among intermingled PC subsets that contribute to separate brain networks remains unclear. Here we demonstrate that a subset of GABAergic chandelier cells (ChCs) in the prelimbic cortex (PL), which innervate PCs at spike initiation site, selectively control PCs projecting to the basolateral amygdala (BLAPC) compared to those projecting to contralateral cortex (ccPC). These ChCs in turn receive preferential input from local and contralateral CCPCs as opposed to BLAPCs and BLA neurons (the PL-BLA network). Accordingly, optogenetic activation of ChCs rapidly suppresses BLAPCs and BLA activity in freely behaving mice. Thus, the exquisite connectivity of ChCs not only mediates directional inhibition between local PC ensembles but may also shape communication hierarchies between global networks.

neuroscience↗