Unsupervised discovery of behaviorally relevant brain states in rats playing hide-and-seek

Cortex-wide characterization of decision-making neural dynamics during spatial navigation

Decision-making during freely moving behaviors involves complex interactions among many cortical and subcortical regions. However, the spatiotemporal coordination across regions to generate a decision is less understood. Using a head-mounted widefield microscope, cortex-wide calcium dynamics were recorded in mice expressing GCaMP7f as they navigated an 8-maze using two paradigms. The first was an alternating pattern that required short term memory of the previous trial to make the correct decision and the second after a rule change to a fixed path in which rewards were delivered only on the left side. Identification of cortex-wide activation states revealed differences between the two paradigms. There was a higher probability for a visual/retrosplenial cortical state during the alternating paradigm and higher probability of a secondary motor and posterior parietal state during left-only. Three state sequences (motifs) illustrated both anterior and posterior activity propagations across the cortex. The anterior propagating motifs had the highest probability around the decision and posterior propagating motifs peaked following the decision. The latter, likely reflecting internal feedback to influence future actions, were more common in the left-only paradigm. Therefore, the probabilities and sequences of cortical states differ when working memory is required versus a fixed trajectory reward paradigm.

Brain dynamics and spatiotemporal trajectories during threat processing

In the past decades, functional MRI research has investigated task processing in largely static fashion based on evoked responses during blocked and event-related designs. Despite some progress in naturalistic designs, our understanding of threat processing remains largely limited to those obtained with standard paradigms with limited dynamics. In the present paper, we applied Switching Linear Dynamical Systems to uncover the dynamics of threat processing during a continuous threat-of-shock paradigm. Importantly, unlike studies in systems neuroscience that frequently assume that systems are decoupled from external inputs, we characterized both endogenous and exogenous contributions to dynamics. First, we demonstrated that the SLDS model learned the regularities of the experimental paradigm, such that states and state transitions estimated from fMRI time series data from 85 regions of interest reflected both the proximity of the circles and their direction (approach vs. retreat). After establishing that the model captured key properties of threat-related processing, we characterized the dynamics of the states and their transitions. The results revealed that threat processing benefits from being viewed in terms of dynamic multivariate patterns whose trajectories are a combination of intrinsic and extrinsic factors that jointly determine how the brain temporally evolves during dynamic threat. Finally, we investigated the generalizability of the modeling approach. The successful application of the SLDS model, trained on one paradigm to a separate experiment illustrates the potential of this approach to capture fMRI dynamics that generalize across related but distinct threat-processing tasks. We propose that viewing threat processing through the lens of dynamical systems offers important avenues to uncover properties of the dynamics of threat that are not unveiled with standard experimental designs and analyses.

ONIX: a unified open-source platform for multimodal neural recording and perturbation during naturalistic behavior

Behavioral neuroscience faces two conflicting demands: long-duration recordings from large neural populations and unimpeded animal behavior. To meet this challenge we developed ONIX, an open-source data acquisition system with high data throughput (2 GB s-1) and low closed-loop latencies (<1 ms) that uses a 0.3-mm thin tether to minimize behavioral impact. Head position and rotation are tracked in three dimensions and used to drive active commutation without torque measurements. ONIX can acquire data from combinations of passive electrodes, Neuropixels probes, head-mounted microscopes, cameras, three-dimensional trackers and other data sources. We performed uninterrupted, long (~7 h) neural recordings in mice as they traversed complex three-dimensional terrain, and multiday sleep-tracking recordings (~55 h). ONIX enabled exploration with similar mobility as nonimplanted animals, in contrast to conventional tethered systems, which have restricted movement. By combining long recordings with full mobility, our technology will enable progress on questions that require high-quality neural recordings during ethologically grounded behaviors.

Catch me if you can: free-living mice show a highly flexible dodging behaviour suggestive of intentional tactical deception

Intentional tactical deception, the employment of a tactic to intentionally deceive another animal, is a complex behaviour based on higher-order cognition, that has rarely been documented outside of primates and corvids. New laboratory-to-field assays, however, provide the opportunity to investigate such behaviour among free-living mice. In the present study, we placed laboratory-style test chambers with a single entrance near a forest outside Warsaw, where we observed the social interactions of two territorial murids, black-striped and yellow-necked mice, under food competition for seven months. Notably, among the social interactions, we video-recorded 21 instances of deceptive pursuer evasion. In the most obvious cases, an individual inside the chamber, to avoid an incoming mouse, hid by the chamber opening (the only means to enter or exit), paused until the pursuer entered and passed by, and then exploited the distraction of the back-turned pursuer by fleeing through the opening in a direction opposite to the one the pursuer came from. This deceptive dodging is the first evidence of a behaviour suggestive of intentional tactical deception among mice. As such, this deceptive behaviour may be of interest not only for rodent psychology but also, more generally, for the fields of non-human intentionality and theory of mind.

Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network

Distributed hypothalamic-midbrain neural circuits orchestrate complex behavioral responses during social interactions. How population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include continuous-state linear dynamical system (LDS) and discrete-state hidden semi-Markov model (HSMM). We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively. Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states. Overall, these analysis approaches provide an unbiased strategy to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks.

A unified open-source platform for multimodal neural recording and perturbation during naturalistic behavior

Behavioral neuroscience faces two conflicting demands: long-duration recordings from large neural populations and unimpeded animal behavior. To meet this challenge, we developed ONIX, an open-source data acquisition system with high data throughput (2GB/sec) and low closed-loop latencies (<1ms) that uses a novel 0.3 mm thin tether to minimize behavioral impact. Head position and rotation are tracked in 3D and used to drive active commutation without torque measurements. ONIX can acquire from combinations of passive electrodes, Neuropixels probes, head-mounted microscopes, cameras, 3D-trackers, and other data sources. We used ONIX to perform uninterrupted, long (~7 hours) neural recordings in mice as they traversed complex 3-dimensional terrain. ONIX allowed exploration with similar mobility as non-implanted animals, in contrast to conventional tethered systems which restricted movement. By combining long recordings with full mobility, our technology will enable new progress on questions that require high-quality neural recordings during ethologically grounded behaviors.

Play fighting and the development of the social brain: The rat's tale

The benefits gained by young animals engaging in play fighting have been a subject of conjecture for over a hundred years. Progress in understanding the behavioral development of play fighting and the underlying neurobiology of laboratory rats has produced a coherent model that sheds light on this matter. Depriving rats of typical peer-peer play experience during the juvenile period leads to adults with socio-cognitive deficiencies and these are correlated with physiological and anatomical changes to the neurons of the prefrontal cortex, especially the medial prefrontal cortex. Detailed analysis of juvenile peer play has shown that using the abilities needed to ensure that play fighting is reciprocal is critical for attaining these benefits. Therefore, unlike that which was posited by many earlier hypotheses, play fighting does not train specific motor actions, but rather, improves a skill set that can be applied in many different social and non-social contexts. There are still gaps in the rat model that need to be understood, but the model is well-enough developed to provide a framework for broader comparative studies of mammals from diverse lineages that engage in play fighting.

Mechanistic modeling as an explanatory tool for clinical treatment of chronic catatonia

Mathematical modeling of neural systems is an effective means to integrate complex information about the brain into a numerical tool that can help explain observations. However, the use of neural models to inform clinical decisions has been limited. In this study, we use a simple model of brain circuitry, the Wilson-Cowan model, to predict changes in a clinical measure for catatonia, the Bush-Francis Catatonia Rating Scale, for use in clinical treatment of schizophrenia. This computational tool can then be used to better understand mechanisms of action for pharmaceutical treatments, and to fine-tune dosage in individual cases. We present the conditions of clinical care for a residential patient cohort, and describe methods for synthesizing data to demonstrated the functioning of the model. We then show that the model can be used to explain effect sizes of treatments and estimate outcomes for combinations of medications. We conclude with a demonstration of how this model could be personalized for individual patients to inform ongoing treatment protocols.