Highly unstable heterogeneous representations in VIP interneurons of the anterior cingulate cortex

Cortical representations of affective pain shape empathic fear in male mice

Affect sharing, the ability to vicariously feel others' emotions, constitutes the primary component of empathy. However, the neural basis for encoding others' distress and representing shared affective experiences remains poorly understood. Here, using miniature endoscopic calcium imaging, we identify distinct and dynamic neural ensembles in the anterior cingulate cortex (ACC) that encode observational fear across both excitatory and inhibitory neurons in male mice. Notably, we discover that the population dynamics encoding vicarious freezing information are conserved in ACC pyramidal neurons and are specifically represented by affective, rather than sensory, responses to direct pain experience. Furthermore, using circuit-specific imaging and optogenetic manipulations, we demonstrate that distinct populations of ACC neurons projecting to the periaqueductal gray (PAG), but not to the basolateral amygdala (BLA), selectively convey affective pain information and regulate observational fear. Taken together, our findings highlight the critical role of ACC neural representations in shaping empathic freezing through the encoding of affective pain.

REVEALS: an open-source multi-camera GUI for rodent behavior acquisition

Deciphering the rich repertoire of mouse behavior is crucial for understanding the functions of both the healthy and diseased brain. However, the current landscape lacks effective, affordable, and accessible methods for acquiring such data, especially when employing multiple cameras simultaneously. We have developed REVEALS (Rodent Behavior Multi-Camera Laboratory Acquisition), a graphical user interface for acquiring rodent behavioral data via commonly used USB3 cameras. REVEALS allows for user-friendly control of recording from one or multiple cameras simultaneously while streamlining the data acquisition process, enabling researchers to collect and analyze large datasets efficiently. We release this software package as a stand-alone, open-source framework for researchers to use and modify according to their needs. We describe the details of the graphical user interface implementation, including the camera control software and the video recording functionality. We validate results demonstrating the graphical user interface's stability, reliability, and accuracy for capturing rodent behavior using DeepLabCut in various behavioral tasks. REVEALS can be incorporated into existing DeepLabCut, MoSeq, or other custom pipelines to analyze complex behavior. In summary, REVEALS offers an interface for collecting behavioral data from single or multiple perspectives, which, when combined with deep learning algorithms, enables the scientific community to identify and characterize complex behavioral phenotypes.

Overexpression of the schizophrenia risk gene C4 in PV cells drives sex-dependent behavioral deficits and circuit dysfunction

Fast-spiking parvalbumin (PV)-positive cells are key players in orchestrating pyramidal neuron activity, and their dysfunction is consistently observed in myriad brain diseases. To understand how immune complement pathway dysregulation in PV cells drives disease pathogenesis, we have developed a transgenic line that permits cell-type specific overexpression of the schizophrenia-associated C4 gene. We found that overexpression of mouse C4 (mC4) in PV cells causes sex-specific alterations in anxiety-like behavior and deficits in synaptic connectivity and excitability of PFC PV cells. Using a computational model, we demonstrated that these microcircuit deficits led to hyperactivity and disrupted neural communication. Finally, pan-neuronal overexpression of mC4 failed to evoke the same deficits in behavior as PV-specific mC4 overexpression, suggesting that perturbations of this neuroimmune gene in fast-spiking neurons are especially detrimental to circuits associated with anxiety-like behavior. Together, these results provide a causative link between C4 and the vulnerability of PV cells in brain disease.

Overexpression of the schizophrenia risk gene C4 in PV cells drives sex-dependent behavioral deficits and circuit dysfunction

Fast-spiking parvalbumin (PV)-positive cells are key players in orchestrating pyramidal neuron activity, and their dysfunction is consistently observed in myriad brain diseases. To understand how immune complement dysregulation - a prevalent locus of brain disease etiology - in PV cells may drive disease pathogenesis, we have developed a transgenic mouse line that permits cell-type specific overexpression of the schizophrenia-associated complement component 4 (C4) gene. We found that overexpression of mouse C4 (mC4) in PV cells causes sex-specific behavioral alterations and concomitant deficits in synaptic connectivity and excitability of PV cells of the prefrontal cortex. Using a computational network, we demonstrated that these microcircuit deficits led to hyperactivity and disrupted neural communication. Finally, pan-neuronal overexpression of mC4 failed to evoke the same deficits in behavior as PV-specific mC4 overexpression, suggesting that C4 perturbations in fast-spiking neurons are more harmful to brain function than pan-neuronal alterations. Together, these results provide a causative link between C4 and the vulnerability of PV cells in brain disease.

REVEALS: An Open Source Multi Camera GUI For Rodent Behavior Acquisition

Understanding the rich behavioral data generated by mice is essential for deciphering the function of the healthy and diseased brain. However, the current landscape lacks effective, affordable, and accessible methods for acquiring such data, especially when employing multiple cameras simultaneously. We have developed REVEALS (Rodent BEhaVior Multi-camErA Laboratory AcquiSition), a graphical user interface (GUI) written in python for acquiring rodent behavioral data via commonly used USB3 cameras. REVEALS allows for user-friendly control of recording from one or multiple cameras simultaneously while streamlining the data acquisition process, enabling researchers to collect and analyze large datasets efficiently. We release this software package as a stand-alone, open-source framework for researchers to use and modify according to their needs. We describe the details of the GUI implementation, including the camera control software and the video recording functionality. We validate results demonstrating the GUI's stability, reliability, and accuracy for capturing and analyzing rodent behavior using DeepLabCut pose estimation in both an object and social interaction assay. REVEALS can also be incorporated into other custom pipelines to analyze complex behavior, such as MoSeq. In summary, REVEALS provides an interface for collecting behavioral data from one or multiple perspectives that, combined with deep learning algorithms, will allow the scientific community to discover and characterize complex behavioral phenotypes to understand brain function better.

Using deep learning to study emotional behavior in rodent models

Quantifying emotional aspects of animal behavior (e.g., anxiety, social interactions, reward, and stress responses) is a major focus of neuroscience research. Because manual scoring of emotion-related behaviors is time-consuming and subjective, classical methods rely on easily quantified measures such as lever pressing or time spent in different zones of an apparatus (e.g., open vs. closed arms of an elevated plus maze). Recent advancements have made it easier to extract pose information from videos, and multiple approaches for extracting nuanced information about behavioral states from pose estimation data have been proposed. These include supervised, unsupervised, and self-supervised approaches, employing a variety of different model types. Representations of behavioral states derived from these methods can be correlated with recordings of neural activity to increase the scope of connections that can be drawn between the brain and behavior. In this mini review, we will discuss how deep learning techniques can be used in behavioral experiments and how different model architectures and training paradigms influence the type of representation that can be obtained.