Tonically active GABAergic neurons in the dorsal periaqueductal gray control instinctive escape in mice.

Escape behavior is a set of locomotor actions that move an animal away from threat. While these actions can be stereotyped, it is advantageous for survival that they are flexible.1,2,3 For example, escape probability depends on predation risk and competing motivations,4,5,6,7,8,9,10,11 and flight to safety requires continuous adjustments of trajectory and must terminate at the appropriate place and time.12,13,14,15,16 This degree of flexibility suggests that modulatory components, like inhibitory networks, act on the neural circuits controlling instinctive escape.17,18,19,20,21,22 In mice, the decision to escape from imminent threats is implemented by a feedforward circuit in the midbrain, where excitatory vesicular glutamate transporter 2-positive (VGluT2+) neurons in the dorsal periaqueductal gray (dPAG) compute escape initiation and escape vigor.23,24,25 Here we tested the hypothesis that local GABAergic neurons within the dPAG control escape behavior by setting the excitability of the dPAG escape network. Using in vitro patch-clamp and in vivo neural activity recordings, we found that vesicular GABA transporter-positive (VGAT+) dPAG neurons fire action potentials tonically in the absence of synaptic inputs and are a major source of inhibition to VGluT2+ dPAG neurons. Activity in VGAT+ dPAG cells transiently decreases at escape onset and increases during escape, peaking at escape termination. Optogenetically increasing or decreasing VGAT+ dPAG activity changes the probability of escape when the stimulation is delivered at threat onset and the duration of escape when delivered after escape initiation. We conclude that the activity of tonically firing VGAT+ dPAG neurons sets a threshold for escape initiation and controls the execution of the flight action.

Threat history controls flexible escape behavior in mice.

In many instances, external sensory-evoked neuronal activity is used by the brain to select the most appropriate behavioral response. Predator-avoidance behaviors such as freezing and escape1,2 are of particular interest since these stimulus-evoked responses are behavioral manifestations of a decision-making process that is fundamental to survival.3,4 Over the lifespan of an individual, however, the threat value of agents in the environment is believed to undergo constant revision,5 and in some cases, repeated avoidance of certain stimuli may no longer be an optimal behavioral strategy.6 To begin to study this type of adaptive control of decision-making, we devised an experimental paradigm to probe the properties of threat escape in the laboratory mouse Mus musculus. First, we found that while robust escape to visual looming stimuli can be observed after 2 days of social isolation, mice can also rapidly learn that such stimuli are non-threatening. This learned suppression of escape (LSE) is extremely robust and can persist for weeks and is not a generalized adaptation, since flight responses to novel live prey and auditory threat stimuli in the same environmental context were maintained. We also show that LSE cannot be explained by trial number or a simple form of stimulus desensitization since it is dependent on threat-escape history. We propose that the action selection process mediating escape behavior is constantly updated by recent threat history and that LSE can be used as a robust model system to understand the neurophysiological mechanisms underlying experience-dependent decision-making.

Accurate determination of marker location within whole-brain microscopy images.

High-resolution whole-brain microscopy provides a means for post hoc determination of the location of implanted devices and labelled cell populations that are necessary to interpret in vivo experiments designed to understand brain function. Here we have developed two plugins (brainreg and brainreg-segment) for the Python-based image viewer napari, to accurately map any object in a common coordinate space. We analysed the position of dye-labelled electrode tracks and two-photon imaged cell populations expressing fluorescent proteins. The precise location of probes and cells were physiologically interrogated and revealed accurate segmentation with near-cellular resolution.

Multisensory coding of angular head velocity in the retrosplenial cortex.

To successfully navigate the environment, animals depend on their ability to continuously track their heading direction and speed. Neurons that encode angular head velocity (AHV) are fundamental to this process, yet the contribution of various motion signals to AHV coding in the cortex remains elusive. By performing chronic single-unit recordings in the retrosplenial cortex (RSP) of the mouse and tracking the activity of individual AHV cells between freely moving and head-restrained conditions, we find that vestibular inputs dominate AHV signaling. Moreover, the addition of visual inputs onto these neurons increases the gain and signal-to-noise ratio of their tuning during active exploration. Psychophysical experiments and neural decoding further reveal that vestibular-visual integration increases the perceptual accuracy of angular self-motion and the fidelity of its representation by RSP ensembles. We conclude that while cortical AHV coding requires vestibular input, where possible, it also uses vision to optimize heading estimation during navigation.

A Circuit for Integration of Head- and Visual-Motion Signals in Layer 6 of Mouse Primary Visual Cortex.

To interpret visual-motion events, the underlying computation must involve internal reference to the motion status of the observer's head. We show here that layer 6 (L6) principal neurons in mouse primary visual cortex (V1) receive a diffuse, vestibular-mediated synaptic input that signals the angular velocity of horizontal rotation. Behavioral and theoretical experiments indicate that these inputs, distributed over a network of 100 L6 neurons, provide both a reliable estimate and, therefore, physiological separation of head-velocity signals. During head rotation in the presence of visual stimuli, L6 neurons exhibit postsynaptic responses that approximate the arithmetic sum of the vestibular and visual-motion response. Functional input mapping reveals that these internal motion signals arrive into L6 via a direct projection from the retrosplenial cortex. We therefore propose that visual-motion processing in V1 L6 is multisensory and contextually dependent on the motion status of the animal's head.

SamuROI, a Python-Based Software Tool for Visualization and Analysis of Dynamic Time Series Imaging at Multiple Spatial Scales.

The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data. SamuROI simplifies exploratory analysis and visualization of image series of fluorescence changes in complex structures over time and is readily applicable at different spatial scales. In this paper, we show the utility of SamuROI in Ca2+-imaging based applications at three spatial scales: the micro-scale (i.e., sub-cellular compartments including cell bodies, dendrites and spines); the meso-scale, (i.e., whole cell and population imaging with single-cell resolution); and the macro-scale (i.e., imaging of changes in bulk fluorescence in large brain areas, without cellular resolution). The software described here provides a graphical user interface for intuitive data exploration and region of interest (ROI) management that can be used interactively within Jupyter Notebook: a publicly available interactive Python platform that allows simple integration of our software with existing tools for automated ROI generation and post-processing, as well as custom analysis pipelines. SamuROI software, source code and installation instructions are publicly available on GitHub and documentation is available online. SamuROI reduces the energy barrier for manual exploration and semi-automated analysis of spatially complex Ca2+ imaging datasets, particularly when these have been acquired at different spatial scales.