BrainGlobe Atlas API: a common interface for neuroanatomical atlases

Summary: Neuroscientists routinely perform experiments aimed at recording or manipulating neural activity, uncovering physiological processes underlying brain function or elucidating aspects of brain anatomy. Understanding how the brain generates behaviour ultimately depends on merging the results of these experiments into a unified picture of brain anatomy and function. Brain atlases are crucial in this endeavour: by outlining the organization of brain regions they provide a reference upon which our understanding of brain function can be anchored. More recently, digital high-resolution 3d atlases have been produced for several model organisms providing an invaluable resource for the research community. Effective use of these atlases depends on the availability of an application programming interface (API) that enables researchers to develop software to access and query atlas data. However, while some atlases come with an API, these are generally specific for individual atlases, and this hinders the development and adoption of open-source neuroanatomy software. The BrainGlobe atlas API (BG-Atlas API) overcomes this problem by providing a common interface for programmers to download and process data across a variety of model organisms. By adopting the BG-Atlas API, software can then be developed agnostic to the atlas, increasing adoption and interoperability of packages in neuroscience and enabling direct integration of different experimental modalities and even comparisons across model organisms

Visualizing anatomically registered data with Brainrender

Three-dimensional (3D) digital brain atlases and high-throughput brain wide imaging techniques generate large multidimensional datasets that can be registered to a common reference frame. Generating insights from such datasets depends critically on visualization and interactive data exploration, but this a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. Here, we present brainrender: an open-source Python package for interactive visualization of multidimensional datasets registered to brain atlases. Brainrender facilitates the creation of complex renderings with different data types in the same visualization and enables seamless use of different atlas sources. High-quality visualizations can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data

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

Novel Approaches to Monitor and Manipulate Single Neurons In Vivo

The complexity of the vertebrate brain poses an enormous challenge to experimental neuroscience. One way of dealing with this complexity has been to investigate different aspects of brain function in widely different preparations, each best suited to address a particular question. Accordingly, cellular questions are typically addressed with intracellular recordings in in vitro preparations such as brain slices or neuronal cultures, whereas network behavior and sensory or motor response properties are analyzed in vivo, often with extracellular recordings. This division of labor has proved to be an experimentally effective strategy. However, although there seems to be no limit to the wealth of data that can be generated in this way, integrating results derived in different preparations comes with its own set of challenges. The enormous difficulties encountered when one attempts to link cellular phenomena such as synaptic plasticity to systems properties such as spatial memory (Martin et al., 2000) have shown us that close collaboration between molecular−cellular and systems neuroscience is required (Tonegawa et al., 2003) and that we need more convergence of experimental techniques to analyze the cellular basis of neural function under more natural conditions. Studying neurons under naturalistic conditions is, however, easier said than done. A return to in vivo preparations will only be successful if we are able to solve the technical problems that led previous researchers to abandon the study of intact brains in the first place. Thus, studying neurons at the cellular level in vertebrate brains is today first and foremost a technological challenge. Here we highlight recent efforts to improve our ability to analyze functions of single neurons in vivo. Given th

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

The lateral septum mediates kinship behavior in the rat

Kinship behavior in rodents has been documented in the laboratory setting but the neural mechanisms that mediate kinship behavior are not known. Here, the authors show that the lateral septum has a key role in organizing mammalian kinship behavior

Encoding Odorant Identity by Spiking Packets of Rate-Invariant Neurons in Awake Mice

Background: How do neural networks encode sensory information? Following sensory stimulation, neural coding is commonly assumed to be based on neurons changing their firing rate. In contrast, both theoretical works and experiments in several sensory systems showed that neurons could encode information as coordinated cell assemblies by adjusting their spike timing and without changing their firing rate. Nevertheless, in the olfactory system, there is little experimental evidence supporting such model. Methodology/Principal Findings: To study these issues, we implanted tetrodes in the olfactory bulb of awake mice to record the odorant-evoked activity of mitral/tufted (M/T) cells. We showed that following odorant presentation, most M/T neurons do not significantly change their firing rate over a breathing cycle but rather respond to odorant stimulation by redistributing their firing activity within respiratory cycles. In addition, we showed that sensory information can be encoded by cell assemblies composed of such neurons, thus supporting the idea that coordinated populations of globally rateinvariant neurons could be efficiently used to convey information about the odorant identity. We showed that different coding schemes can convey high amount of odorant information for specific read-out time window. Finally we showed that the optimal readout time window corresponds to the duration of gamma oscillations cycles. Conclusion: We propose that odorant can be encoded by population of cells that exhibit fine temporal tuning of spiking activity while displaying weak or no firing rate change. These cell assemblies may transfer sensory information in spikin

Regulation of Spike Timing-Dependent Plasticity of Olfactory Inputs in Mitral Cells in the Rat Olfactory Bulb

The recent history of activity input onto granule cells (GCs) in the main olfactory bulb can affect the strength of lateral inhibition, which functions to generate contrast enhancement. However, at the plasticity level, it is unknown whether and how the prior modification of lateral inhibition modulates the subsequent induction of long-lasting changes of the excitatory olfactory nerve (ON) inputs to mitral cells (MCs). Here we found that the repetitive stimulation of two distinct excitatory inputs to the GCs induced a persistent modification of lateral inhibition in MCs in opposing directions. This bidirectional modification of inhibitory inputs differentially regulated the subsequent synaptic plasticity of the excitatory ON inputs to the MCs, which was induced by the repetitive pairing of excitatory postsynaptic potentials (EPSPs) with postsynaptic bursts. The regulation of spike timing-dependent plasticity (STDP) was achieved by the regulation of the inter-spike-interval (ISI) of the postsynaptic bursts. This novel form of inhibition-dependent regulation of plasticity may contribute to the encoding or processing of olfactory information in the olfactory bulb

Explicit-Duration Hidden Markov Model Inference of UP-DOWN States from Continuous Signals

Neocortical neurons show UP-DOWN state (UDS) oscillations under a variety of conditions. These UDS have been extensively studied because of the insight they can yield into the functioning of cortical networks, and their proposed role in putative memory formation. A key element in these studies is determining the precise duration and timing of the UDS. These states are typically determined from the membrane potential of one or a small number of cells, which is often not sufficient to reliably estimate the state of an ensemble of neocortical neurons. The local field potential (LFP) provides an attractive method for determining the state of a patch of cortex with high spatio-temporal resolution; however current methods for inferring UDS from LFP signals lack the robustness and flexibility to be applicable when UDS properties may vary substantially within and across experiments. Here we present an explicit-duration hidden Markov model (EDHMM) framework that is sufficiently general to allow statistically principled inference of UDS from different types of signals (membrane potential, LFP, EEG), combinations of signals (e.g., multichannel LFP recordings) and signal features over long recordings where substantial non-stationarities are present. Using cortical LFPs recorded from urethane-anesthetized mice, we demonstrate that the proposed method allows robust inference of UDS. To illustrate the flexibility of the algorithm we show that it performs well on EEG recordings as well. We then validate these results using simultaneous recordings of the LFP and membrane potential (MP) of nearby cortical neurons, showing that our method offers significant improvements over standard methods. These results could be useful for determining functional connectivity of different brain regions, as well as understanding network dynamics

Hyperpolarization-activated and cyclic nucleotide-gated channels are differentially expressed in juxtaglomerular cells in the olfactory bulb of mice

In the olfactory bulb, input from olfactory receptor neurons is processed by neuronal networks before it is relayed to higher brain regions. In many neurons, hyperpolarization-activated and cyclic nucleotide-gated (HCN) channels generate and control oscillations of the membrane potential. Oscillations also appear crucial for information processing in the olfactory bulb. Four channel isoforms exist (HCN1–HCN4) that can form homo- or heteromers. Here, we describe the expression pattern of HCN isoforms in the olfactory bulb of mice by using a novel and comprehensive set of antibodies against all four isoforms. HCN isoforms are abundantly expressed in the olfactory bulb. HCN channels can be detected in most cell populations identified by commonly used marker antibodies. The combination of staining with marker and HCN antibodies has revealed at least 17 different staining patterns in juxtaglomerular cells. Furthermore, HCN isoforms give rise to an unexpected wealth of co-expression patterns but are rarely expressed in the same combination and at the same level in two given cell populations. Therefore, heteromeric HCN channels may exist in several cell populations in vivo. Our results suggest that HCN channels play an important role in olfactory information processing. The staining patterns are consistent with the possibility that both homomeric and heteromeric HCN channels are involved in oscillations of the membrane potential of juxtaglomerular cells