The Virtual-Environment-Foraging Task enables rapid training and single-trial metrics of attention in head-fixed mice

What does the mean mean? A simple test for neuroscience

Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions.Behavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses. We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of Data set 1, both assumptions were largely fulfilled. In contrast, the less restrictive paradigm of Data set 2 met neither assumption. Simulations predict that the larger diversity of neuronal response preferences, rather than higher cross-trial reliability, drives the better performance of Data set 1. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context.

Advancing preclinical chronic stress models to promote therapeutic discovery for human stress disorders

There is an urgent need to develop more effective treatments for stress-related illnesses, which include depression, post-traumatic stress disorder, and anxiety. We view animal models as playing an essential role in this effort, but to date, such approaches have generally not succeeded in developing therapeutics with new mechanisms of action. This is partly due to the complexity of the brain and its disorders, but also to inherent difficulties in modeling human disorders in rodents and to the incorrect use of animal models: namely, trying to recapitulate a human syndrome in a rodent which is likely not possible as opposed to using animals to understand underlying mechanisms and evaluating potential therapeutic paths. Recent transcriptomic research has established the ability of several different chronic stress procedures in rodents to recapitulate large portions of the molecular pathology seen in postmortem brain tissue of individuals with depression. These findings provide crucial validation for the clear relevance of rodent stress models to better understand the pathophysiology of human stress disorders and help guide therapeutic discovery. In this review, we first discuss the current limitations of preclinical chronic stress models as well as traditional behavioral phenotyping approaches. We then explore opportunities to dramatically enhance the translational use of rodent stress models through the application of new experimental technologies. The goal of this review is to promote the synthesis of these novel approaches in rodents with human cell-based approaches and ultimately with early-phase proof-of-concept studies in humans to develop more effective treatments for human stress disorders.

Distributed context-dependent choice information in mouse posterior cortex

Choice information appears in multi-area brain networks mixed with sensory, motor, and cognitive variables. In the posterior cortex-traditionally implicated in decision computations-the presence, strength, and area specificity of choice signals are highly variable, limiting a cohesive understanding of their computational significance. Examining the mesoscale activity in the mouse posterior cortex during a visual task, we found that choice signals defined a decision variable in a low-dimensional embedding space with a prominent contribution along the ventral visual stream. Their subspace was near-orthogonal to concurrently represented sensory and motor-related activations, with modulations by task difficulty and by the animals' attention state. A recurrent neural network trained with animals' choices revealed an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. Our results demonstrated an independent, multi-area decision variable in the posterior cortex, controlled by task features and cognitive demands, possibly linked to contextual inference computations in dynamic animal-environment interactions.

Refinements to rodent head fixation and fluid/food control for neuroscience

The use of head fixation in mice is increasingly common in research, its use having initially been restricted to the field of sensory neuroscience. Head restraint has often been combined with fluid control, rather than food restriction, to motivate behaviour, but this too is now in use for both restrained and non-restrained animals. Despite this, there is little guidance on how best to employ these techniques to optimise both scientific outcomes and animal welfare. This article summarises current practices and provides recommendations to improve animal wellbeing and data quality, based on a survey of the community, literature reviews, and the expert opinion and practical experience of an international working group convened by the UK's National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs). Topics covered include head fixation surgery and post-operative care, habituation to restraint, and the use of fluid/food control to motivate performance. We also discuss some recent developments that may offer alternative ways to collect data from large numbers of behavioural trials without the need for restraint. The aim is to provide support for researchers at all levels, animal care staff, and ethics committees to refine procedures and practices in line with the refinement principle of the 3Rs.

Reconstruction of ovine axonal cytoarchitecture enables more accurate models of brain biomechanics

There is an increased need and focus to understand how local brain microstructure affects the transport of drug molecules directly administered to the brain tissue, for example in convection-enhanced delivery procedures. This study reports a systematic attempt to characterize the cytoarchitecture of commissural, long association and projection fibres, namely the corpus callosum, the fornix and the corona radiata, with the specific aim to map different regions of the tissue and provide essential information for the development of accurate models of brain biomechanics. Ovine samples are imaged using scanning electron microscopy combined with focused ion beam milling to generate 3D volume reconstructions of the tissue at subcellular spatial resolution. Focus is placed on the characteristic cytological feature of the white matter: the axons and their alignment in the tissue. For each tract, a 3D reconstruction of relatively large volumes, including a significant number of axons, is performed and outer axonal ellipticity, outer axonal cross-sectional area and their relative perimeter are measured. The study of well-resolved microstructural features provides useful insight into the fibrous organization of the tissue, whose micromechanical behaviour is that of a composite material presenting elliptical tortuous tubular axonal structures embedded in the extra-cellular matrix. Drug flow can be captured through microstructurally-based models using 3D volumes, either reconstructed directly from images or generated in silico using parameters extracted from the database of images, leading to a workflow to enable physically-accurate simulations of drug delivery to the targeted tissue.

Imaging and reconstruction of sheep brain axonal cytoarchitecture provides insight for brain biomechanics models that simulate drug delivery and other biological processes governed by interstitial fluid flow and transport.

Shared and specialized coding across posterior cortical areas for dynamic navigation decisions

Animals adaptively integrate sensation, planning, and action to navigate toward goal locations in ever-changing environments, but the functional organization of cortex supporting these processes remains unclear. We characterized encoding in approximately 90,000 neurons across the mouse posterior cortex during a virtual navigation task with rule switching. The encoding of task and behavioral variables was highly distributed across cortical areas but differed in magnitude, resulting in three spatial gradients for visual cue, spatial position plus dynamics of choice formation, and locomotion, with peaks respectively in visual, retrosplenial, and parietal cortices. Surprisingly, the conjunctive encoding of these variables in single neurons was similar throughout the posterior cortex, creating high-dimensional representations in all areas instead of revealing computations specialized for each area. We propose that, for guiding navigation decisions, the posterior cortex operates in parallel rather than hierarchically, and collectively generates a state representation of the behavior and environment, with each area specialized in handling distinct information modalities.

Attention separates sensory and motor signals in the mouse visual cortex

Visually guided behaviors depend on the activity of cortical networks receiving visual inputs and transforming these signals to guide appropriate actions. However, non-retinal inputs, carrying motor signals as well as cognitive and attentional modulatory signals, also activate these cortical regions. How these networks integrate coincident signals ensuring reliable visual behaviors is poorly understood. In this study, we observe neural responses in the dorsal-parietal cortex of mice during a visual discrimination task driven by visual stimuli and movements. We find that visual and motor signals interact according to two mechanisms: divisive normalization and separation of responses. Interactions are contextually modulated by the animal's state of sustained attention, which amplifies visual and motor signals and increases their discriminability in a low-dimensional space of neural activations. These findings reveal computational principles operating in dorsal-parietal networks that enable separation of incoming signals for reliable visually guided behaviors during interactions with the environment.

High-precision coding in visual cortex

Individual neurons in visual cortex provide the brain with unreliable estimates of visual features. It is not known whether the single-neuron variability is correlated across large neural populations, thus impairing the global encoding of stimuli. We recorded simultaneously from up to 50,000 neurons in mouse primary visual cortex (V1) and in higher order visual areas and measured stimulus discrimination thresholds of 0.35° and 0.37°, respectively, in an orientation decoding task. These neural thresholds were almost 100 times smaller than the behavioral discrimination thresholds reported in mice. This discrepancy could not be explained by stimulus properties or arousal states. Furthermore, behavioral variability during a sensory discrimination task could not be explained by neural variability in V1. Instead, behavior-related neural activity arose dynamically across a network of non-sensory brain areas. These results imply that perceptual discrimination in mice is limited by downstream decoders, not by neural noise in sensory representations.

A Visual Two-Choice Rule-Switch Task for Head-Fixed Mice

Cognitive flexibility is the innate ability of the brain to change mental processes and to modify behavioral responses according to an ever-changing environment. As our brain has a limited capacity to process the information of our surroundings in any given moment, it uses sets as a strategy to aid neural processing systems. With assessing the capability of shifting between task sets, it is possible to test cognitive flexibility and executive functions. The most widely used neuropsychological task for the evaluation of these functions in humans is the Wisconsin Card Sorting Test (WCST), which requires the subject to alter response strategies and use previously irrelevant information to solve a problem. The test has proven clinical relevance, as poor performance has been reported in multiple neuropsychiatric conditions. Although, similar tasks have been used in pre-clinical rodent research, many are limited because of their manual-based testing procedures and their hardware attenuates neuronal recordings. We developed a two-choice rule-switch task whereby head-fixed C57BL/6 mice had to choose correctly one of the two virtual objects presented to retrieve a small water reward. The animals learnt to discriminate the visual cues and they successfully switched their strategies according to the related rules. We show that reaching successful performance after the rule changes required more trials in this task and that animals took more time to execute decisions when the two rules were in conflict. We used optogenetics to inhibit temporarily the medial prefrontal cortex (mPFC) during reward delivery and consumption, which significantly increased the number of trials needed to perform the second rule successfully (i.e., succeed in switching between rules), compared to control experiments. Furthermore, by assessing two types of error animals made after the rule switch, we show that interfering with the positive feedback integration, but leaving the negative feedback processing intact, does not influence the initial disengagement from the first rule, but impedes the maintenance of the newly acquired response set. These findings support the role of prefrontal networks in mice for cognitive flexibility, which is impaired during numerous neuropsychiatric diseases, such as schizophrenia and depression.

The Virtual-Environment-Foraging Task enables rapid training and single-trial metrics of rule acquisition and reversal in head-fixed mice

Behavioural flexibility is an essential survival skill, yet our understanding of its neuronal substrates is still limited. While mouse research offers unique tools to dissect the neuronal circuits involved, the measurement of flexible behaviour in mice often suffers from long training times, poor experimental control, and temporally imprecise binary (hit/miss) performance readouts. Here we present a virtual-environment task for mice that tackles these limitations. It offers fast training of vision-based rule reversals (~100 trials per reversal) with full stimulus control and continuous behavioural readouts. By generating multiple non-binary performance metrics per trial, it provides single-trial estimates not only of response accuracy and speed, but also of underlying processes like choice certainty and alertness (discussed in detail in a companion paper). Based on these metrics, we show that mice can predict new task rules long before they are able to execute them, and that this delay varies across animals. We also provide and validate single-trial estimates of whether an error was committed with or without awareness of the task rule. By tracking in unprecedented detail the cognitive dynamics underlying flexible behaviour, this task enables new investigations into the neuronal interactions that shape behavioural flexibility moment by moment.