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Gabriel, C. J.

Publications and source records attributed to Gabriel, C. J..

3 recordsLinked to original sources

Transformations in prefrontal ensemble activity underlying rapid threat avoidance learning

The capacity to learn cues that predict aversive outcomes, and understand how to avoid those outcomes, is critical for adaptive behavior. Naturalistic avoidance often means accessing a safe location, but whether a location is safe depends on the nature of the impending threat. These relationships must be rapidly learned if animals are to survive. The prelimbic subregion (PL) of the medial prefrontal cortex (mPFC) integrates learned associations to influence these threat avoidance strategies. Prior work has focused on the role of PL activity in avoidance behaviors that are fully established, leaving the prefrontal mechanisms that drive rapid avoidance learning poorly understood. To determine when and how these learning-related changes emerge, we recorded PL neural activity using miniscope calcium imaging as mice rapidly learned to avoid a threatening cue by accessing a safe location. Over the course of learning, we observed enhanced modulation of PL activity representing intersections of a threatening cue with safe or risky locations and movements between them. We observed rapid changes in PL population dynamics that preceded changes observable in the encoding of individual neurons. Successful avoidance could be predicted from cue-related population dynamics during early learning. Population dynamics during specific epochs of the conditioned tone period correlated with the modeled learning rates of individual animals. In contrast, changes in single-neuron encoding occurred later, once an avoidance strategy had stabilized. Together, our findings reveal the sequence of PL changes that characterize rapid threat avoidance learning.

neuroscience↗

Developmentally distinct architectures in top-down circuits

The medial prefrontal cortex (mPFC) plays a key role in learning, mood and decision making, including in how individuals respond to threats1-6. mPFC undergoes a uniquely protracted development, with changes in synapse density, cortical thickness, long-range connectivity, and neuronal encoding properties continuing into early adulthood7-21. Models suggest that before adulthood, the slow-developing mPFC cannot adequately regulate activity in faster-developing subcortical centers22,23. They propose that during development, the enhanced influence of subcortical systems underlies distinctive behavioural strategies of juveniles and adolescents and that increasing mPFC control over subcortical structures eventually allows adult behaviours to emerge. Yet it has remained unclear how a progressive strengthening of top-down control can lead to nonlinear changes in behaviour as individuals mature24,25. To address this discrepancy, here we monitored and manipulated activity in the developing brain as animals responded to threats, establishing direct causal links between frontolimbic circuit activity and the behavioural strategies of juvenile, adolescent and adult mice. Rather than a linear strengthening of mPFC synaptic connectivity progressively regulating behaviour, we uncovered multiple developmental switches in the behavioural roles of mPFC circuits targeting the basolateral amygdala (BLA) and nucleus accumbens (NAc). We show these changes are accompanied by axonal pruning coinciding with functional strengthening of synaptic connectivity in the mPFC-BLA and mPFC-NAc pathways, which mature at different rates. Our results reveal how developing mPFC circuits pass through distinct architectures that may make them optimally adapted to the demands of age-specific challenges.

neuroscience↗

BehaviorDEPOT: a tool for automated behavior classification and analysis in rodents

Quantitative descriptions of animal behavior are essential to understand the underlying neural substrates. Many behavioral analyses are performed by hand or with expensive and inflexible commercial software that often fail on animals with attached head implants, such as those used for in vivo optogenetics and calcium imaging. With the development of machine learning algorithms that can estimate animal positions across time and space, it is becoming easier for users with no prior coding experience to perform automated animal tracking in behavioral video recordings. Yet classifying discrete behaviors based on positional tracking data remains a significant challenge. To achieve this, we must start with reliable ground truth definitions of behavior, a process that is hindered by unreliable human annotations. To overcome these barriers, we developed BehaviorDEPOT (DEcoding behavior based on POsitional Tracking), a MATLAB-based application comprising six independent modules and a graphical user interface. In the Analysis Module we provide hard-coded classifiers for freezing and rearing. Optionally applied spatiotemporal filters allow users to analyze behaviors in varied experimental designs (e.g. cued tasks or optogenetic manipulations). Even inexperienced users can generate organized behavioral data arrays that can be seamlessly aligned with neurophysiological recordings for detailed analyses of the neural substrates. Four additional modules create an easy-to-use pipeline for establishing reliable ground-truth definitions of behaviors as well as custom behavioral classifiers. Finally, our Experiment Module runs fear conditioning experiments using an Arduino-based design that interfaces with commercialhardware and significantly reduces associated costs. We demonstrate the utility and flexibility of BehaviorDEPOT in widely used behavioral assays including fear conditioning, avoidance, and decision-making tasks. We also demonstrate the robustness of the BehaviorDEPOT freezing classifier across multiple camera types and in mice and rats wearing optogenetic patch cables and head-mounted Miniscopes. BehaviorDEPOT provides a simple, flexible, automated pipeline to move from pose tracking to reliably quantifying a wide variety of task-relevant behaviors.

neuroscience↗