Action representation in the mouse parieto-frontal network

Neuronal ensembles: Building blocks of neural circuits

Neuronal ensembles, defined as groups of neurons displaying recurring patterns of coordinated activity, represent an intermediate functional level between individual neurons and brain areas. Novel methods to measure and optically manipulate the activity of neuronal populations have provided evidence of ensembles in the neocortex and hippocampus. Ensembles can be activated intrinsically or in response to sensory stimuli and play a causal role in perception and behavior. Here we review ensemble phenomenology, developmental origin, biophysical and synaptic mechanisms, and potential functional roles across different brain areas and species, including humans. As modular units of neural circuits, ensembles could provide a mechanistic underpinning of fundamental brain processes, including neural coding, motor planning, decision-making, learning, and adaptability.

Rat movements reflect internal decision dynamics in an evidence accumulation task

Perceptual decision-making involves multiple cognitive processes, including accumulation of sensory evidence, planning, and executing a motor action. How these processes are intertwined is unclear; some models assume that decision-related processes precede motor execution, whereas others propose that movements reflecting on-going decision processes occur before commitment to a choice. Here we develop and apply two complementary methods to study the relationship between decision processes and the movements leading up to a choice. The first is a free response pulse-based evidence accumulation task, in which stimuli continue until choice is reported. The second is a motion-based drift diffusion model (mDDM), in which movement variables from video pose estimation constrain decision parameters on a trial-by-trial basis. We find the mDDM provides a better model fit to rats' decisions in the free response accumulation task than traditional DDM models. Interestingly, on each trial we observed a period of time, prior to choice, that was characterized by head immobility. The length of this period was positively correlated with the rats' decision bounds and stimuli presented during this period had the greatest impact on choice. Together these results support a model in which internal decision dynamics are reflected in movements and demonstrate that inclusion of movement parameters improves the performance of diffusion-to-bound decision models.

Visuomotor interactions in the mouse forebrain mediated by extrastriate cortico-cortical pathways

Introduction

The mammalian visual system can be broadly divided into two functional processing pathways: a dorsal stream supporting visually and spatially guided actions, and a ventral stream enabling object recognition. In rodents, the majority of visual signaling in the dorsal stream is transmitted to frontal motor cortices via extrastriate visual areas surrounding V1, but exactly where and to what extent V1 feeds into motor-projecting visual regions is not well known.

Methods

We employed a dual labeling strategy in male and female mice in which efferent projections from V1 were labeled anterogradely, and motor-projecting neurons in higher visual areas were labeled with retrogradely traveling adeno-associated virus (rAAV-retro) injected in M2. We characterized the labeling in both flattened and coronal sections of dorsal cortex and made high-resolution 3D reconstructions to count putative synaptic contacts in different extrastriate areas.

Results

The most pronounced colocalization V1 output and M2 input occurred in extrastriate areas AM, PM, RL and AL. Neurons in both superficial and deep layers in each project to M2, but high resolution volumetric reconstructions indicated that the majority of putative synaptic contacts from V1 onto M2-projecting neurons occurred in layer 2/3.

Discussion

These findings support the existence of a dorsal processing stream in the mouse visual system, where visual signals reach motor cortex largely via feedforward projections in anteriorly and medially located extrastriate areas.

Neural manifold analysis of brain circuit dynamics in health and disease

Recent developments in experimental neuroscience make it possible to simultaneously record the activity of thousands of neurons. However, the development of analysis approaches for such large-scale neural recordings have been slower than those applicable to single-cell experiments. One approach that has gained recent popularity is neural manifold learning. This approach takes advantage of the fact that often, even though neural datasets may be very high dimensional, the dynamics of neural activity tends to traverse a much lower-dimensional space. The topological structures formed by these low-dimensional neural subspaces are referred to as "neural manifolds", and may potentially provide insight linking neural circuit dynamics with cognitive function and behavioral performance. In this paper we review a number of linear and non-linear approaches to neural manifold learning, including principal component analysis (PCA), multi-dimensional scaling (MDS), Isomap, locally linear embedding (LLE), Laplacian eigenmaps (LEM), t-SNE, and uniform manifold approximation and projection (UMAP). We outline these methods under a common mathematical nomenclature, and compare their advantages and disadvantages with respect to their use for neural data analysis. We apply them to a number of datasets from published literature, comparing the manifolds that result from their application to hippocampal place cells, motor cortical neurons during a reaching task, and prefrontal cortical neurons during a multi-behavior task. We find that in many circumstances linear algorithms produce similar results to non-linear methods, although in particular cases where the behavioral complexity is greater, non-linear methods tend to find lower-dimensional manifolds, at the possible expense of interpretability. We demonstrate that these methods are applicable to the study of neurological disorders through simulation of a mouse model of Alzheimer's Disease, and speculate that neural manifold analysis may help us to understand the circuit-level consequences of molecular and cellular neuropathology.

Functional Connectivity of the Brain Across Rodents and Humans

Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.

Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography

Social interactions powerfully impact the brain and the body, but high-resolution descriptions of these important physical interactions and their neural correlates are lacking. Currently, most studies rely on labor-intensive methods such as manual annotation. Scalable and objective tracking methods are required to understand the neural circuits underlying social behavior. Here we describe a hardware/software system and analysis pipeline that combines 3D videography, deep learning, physical modeling, and GPU-accelerated robust optimization, with automatic analysis of neuronal receptive fields recorded in interacting mice. Our system ("3DDD Social Mouse Tracker") is capable of fully automatic multi-animal tracking with minimal errors (including in complete darkness) during complex, spontaneous social encounters, together with simultaneous electrophysiological recordings. We capture posture dynamics of multiple unmarked mice with high spatiotemporal precision (~2 mm, 60 frames/s). A statistical model that relates 3D behavior and neural activity reveals multiplexed 'social receptive fields' of neurons in barrel cortex. Our approach could be broadly useful for neurobehavioral studies of multiple animals interacting in complex low-light environments.

Neurons of rat motor cortex become active during both grasping execution and grasping observation

In non-human primates, a subset of frontoparietal neurons (mirror neurons) respond both when an individual executes an action and when it observes another individual performing a similar action.^1-8 Mirror neurons constitute an observation and execution matching system likely involved in others' actions processing^3,5,9 and in a large set of complex cognitive functions.^10,11 Here, we show that the forelimb motor cortex of rats contains neurons presenting mirror properties analogous to those observed in macaques. We provide this evidence by event-related potentials acquired by microelectrocorticography and intracortical single-neuron activity, recorded from the same cortical region during grasping execution and observation. Mirror responses are highly specific, because grasping-related neurons do not respond to the observation of either grooming actions or graspable food alone. These results demonstrate that mirror neurons are present already in species phylogenetically distant from primates, suggesting for them a fundamental, albeit basic, role not necessarily related to higher cognitive functions. Moreover, because murine models have long been valued for their superior experimental accessibility and rapid life cycle, the present finding opens an avenue to new empirical studies tackling questions such as the innate or acquired origin of sensorimotor representations and the effects of social and environmental deprivation on sensorimotor development and recovery.

Parietal maps of visual signals for bodily action planning

The posterior parietal cortex (PPC) has long been understood as a high-level integrative station for computing motor commands for the body based on sensory (i.e., mostly tactile and visual) input from the outside world. In the last decade, accumulating evidence has shown that the parietal areas not only extract the pragmatic features of manipulable objects, but also subserve sensorimotor processing of others’ actions. A paradigmatic case is that of the anterior intraparietal area (AIP), which encodes the identity of observed manipulative actions that afford potential motor actions the observer could perform in response to them. On these bases, we propose an AIP manipulative action-based template of the general planning functions of the PPC and review existing evidence supporting the extension of this model to other PPC regions and to a wider set of actions: defensive and locomotor actions. In our model, a hallmark of PPC functioning is the processing of information about the physical and social world to encode potential bodily actions appropriate for the current context. We further extend the model to actions performed with man-made objects (e.g., tools) and artifacts, because they become integral parts of the subject’s body schema and motor repertoire. Finally, we conclude that existing evidence supports a generally conserved neural circuitry that transforms integrated sensory signals into the variety of bodily actions that primates are capable of preparing and performing to interact with their physical and social world.

Secondary motor cortex: Broadcasting and biasing animal's decisions through long-range circuits

Medial secondary motor cortex (MOs or M2) constitutes the dorsal aspect of the rodent medial frontal cortex. We previously proposed that the function of MOs is to link antecedent conditions, including sensory stimuli and prior choices, to impending actions. In this review, we focus on the long-range pathways between MOs and other cortical and subcortical regions. We highlight three circuits: (1) connections with visual and auditory cortices that are essential for predictive coding of perceptual inputs; (2) connections with motor cortex and brainstem that are responsible for top-down, context-dependent modulation of movements; (3) connections with retrosplenial cortex, orbitofrontal cortex, and basal ganglia that facilitate reward-based learning. Together, these long-range circuits allow MOs to broadcast choice signals for feedback and to bias decision-making processes.

Reconsidering the Border between the Visual and Posterior Parietal Cortex of Mice

The posterior parietal cortex (PPC) contributes to multisensory and sensory-motor integration, as well as spatial navigation. Based on primate studies, the PPC is composed of several subdivisions with differing connection patterns, including areas that exhibit retinotopy. In mice the composition of the PPC is still under debate. We propose a revised anatomical delineation in which we classify the higher order visual areas rostrolateral area (RL), anteromedial area (AM), and Medio-Medial-Anterior cortex (MMA) as subregions of the mouse PPC. Retrograde and anterograde tracing revealed connectivity, characteristic for primate PPC, with sensory, retrosplenial, orbitofrontal, cingulate and motor cortex, as well as with several thalamic nuclei and the superior colliculus in the mouse. Regarding cortical input, RL receives major input from the somatosensory barrel field, while AM receives more input from the trunk, whereas MMA receives strong inputs from retrosplenial, cingulate, and orbitofrontal cortices. These input differences suggest that each posterior PPC subregion may have a distinct function. Summarized, we put forward a refined cortical map, including a mouse PPC that contains at least 6 subregions, RL, AM, MMA and PtP, MPta, LPta/A. These anatomical results set the stage for a more detailed understanding about the role that the PPC and its subdivisions play in multisensory integration-based behavior in mice.

Play, but not observing play, engages rat medial prefrontal cortex

Rats have elaborate cognitive capacities for playing Hide & Seek. Playing Hide & Seek strongly engages medial prefrontal cortex and the activity of prefrontal cortex neurons reflects the structure of the game. We wondered if prefrontal neurons would also show a mirroring of play-related neural activity. Specifically, we asked how does the activity in the rat medial prefrontal cortex differ when the animal plays itself versus when it observes others playing. Consistent with our previous work, when the animal plays itself we observed medial prefrontal cortex activity that was sharply locked to game events. Observing play, however, did not lead to a comparable activation of rat medial prefrontal cortex. Firing rates during observing play were lower than during real play. The modulation of responses in medial prefrontal cortex by game events was strong during playing Hide & Seek, but weak during observing Hide & Seek. We conclude the rat prefrontal cortex does not mirror play events under our experimental conditions.