Logan Grosenick

Structural and molecular interrogation of intact biological systems

Obtaining high-resolution information from a complex system, while maintaining the global perspective needed to understand system function, represents a key challenge in biology. Here we address this challenge with a method (termed CLARITY) for the transformation of intact tissue into a nanoporous hydrogel-hybridized form (crosslinked to a three-dimensional network of hydrophilic polymers) that is fully assembled but optically transparent and macromolecule-permeable. Using mouse brains, we show intact-tissue imaging of long-range projections, local circuit wiring, cellular relationships, subcellular structures, protein complexes, nucleic acids and neurotransmitters. CLARITY also enables intact-tissue in situ hybridization, immunohistochemistry with multiple rounds of staining and de-staining in non-sectioned tissue, and antibody labelling throughout the intact adult mouse brain. Finally, we show that CLARITY enables fine structural analysis of clinical samples, including non-sectioned human tissue from a neuropsychiatric-disease setting, establishing a path for the transmutation of human tissue into a stable, intact and accessible form suitable for probing structural and molecular underpinnings of physiological function and disease.

Amygdala circuitry mediating reversible and bidirectional control of anxiety

Anxiety—a sustained state of heightened apprehension in the absence of immediate threat—becomes severely debilitating in disease states. Anxiety disorders represent the most common of psychiatric diseases (28% lifetime prevalence) and contribute to the aetiology of major depression and substance abuse. Although it has been proposed that the amygdala, a brain region important for emotional processing, has a role in anxiety, the neural mechanisms that control anxiety remain unclear. Here we explore the neural circuits underlying anxiety-related behaviours by using optogenetics with two-photon microscopy, anxiety assays in freely moving mice, and electrophysiology. With the capability of optogenetics to control not only cell types but also specific connections between cells, we observed that temporally precise optogenetic stimulation of basolateral amygdala (BLA) terminals in the central nucleus of the amygdala (CeA)—achieved by viral transduction of the BLA with a codon-optimized channelrhodopsin followed by restricted illumination in the downstream CeA—exerted an acute, reversible anxiolytic effect. Conversely, selective optogenetic inhibition of the same projection with a third-generation halorhodopsin (eNpHR3.0) increased anxiety-related behaviours. Importantly, these effects were not observed with direct optogenetic control of BLA somata, possibly owing to recruitment of antagonistic downstream structures. Together, these results implicate specific BLA–CeA projections as critical circuit elements for acute anxiety control in the mammalian brain, and demonstrate the importance of optogenetically targeting defined projections, beyond simply targeting cell types, in the study of circuit function relevant to neuropsychiatric disease.

Fish can infer social rank by observation alone

Transitive inference (TI) involves using known relationships to deduce unknown ones (for example, using A > B and B > C to infer A > C), and is thus essential to logical reasoning. First described as a developmental milestone in children, TI has since been reported in nonhuman primates, rats and birds. Still, how animals acquire and represent transitive relationships and why such abilities might have evolved remain open problems. Here we show that male fish (Astatotilapia burtoni) can successfully make inferences on a hierarchy implied by pairwise fights between rival males. These fish learned the implied hierarchy vicariously (as ‘bystanders’), by watching fights between rivals arranged around them in separate tank units. Our findings show that fish use TI when trained on socially relevant stimuli, and that they can make such inferences by using indirect information alone. Further, these bystanders seem to have both spatial and featural representations related to rival abilities, which they can use to make correct inferences depending on what kind of information is available to them. Beyond extending TI to fish and experimentally demonstrating indirect TI learning in animals, these results indicate that a universal mechanism underlying TI is unlikely. Rather, animals probably use multiple domain-specific representations adapted to different social and ecological pressures that they encounter during the course of their natural lives.

Optetrode: a multichannel readout for optogenetic control in freely moving mice

Recent advances in optogenetics have improved the precision with which defined circuit elements can be controlled optically in freely moving mammals; in particular, recombinase-dependent opsin viruses, used with a growing pool of transgenic mice expressing recombinases, allow manipulation of specific cell types. However, although optogenetic control has allowed neural circuits to be manipulated in increasingly powerful ways, combining optogenetic stimulation with simultaneous multichannel electrophysiological readout of isolated units in freely moving mice remains a challenge. We designed and validated the optetrode, a device that allows for colocalized multi-tetrode electrophysiological recording and optical stimulation in freely moving mice. Optetrode manufacture employs a unique optical fiber-centric coaxial design approach that yields a lightweight (2 g), compact and robust device that is suitable for behaving mice. This low-cost device is easy to construct (2.5 h to build without specialized equipment). We found that the drive design produced stable high-quality recordings and continued to do so for at least 6 weeks following implantation. We validated the optetrode by quantifying, for the first time, the response of cells in the medial prefrontal cortex to local optical excitation and inhibition, probing multiple different genetically defined classes of cells in the mouse during open field exploration.

Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior

A way to modulate reward-seeking Which brain regions are causally involved in reward-related behavior? Ferenczi et al. combined focal, cell type-specific, optogenetic manipulations with brain imaging, behavioral testing, and in vivo electrophysiology (see the Perspective by Robbins). Stimulation of midbrain dopamine neurons increased activity in a brain region called the striatum and was correlated with reward-seeking across individual animals. However, elevated excitability of an area called the medial prefrontal cortex reduced both striatal responses to the stimulation of dopamine neurons and the behavioral drive to seek the stimulation of dopamine neurons. Finally, modulating the excitability of medial prefrontal cortex pyramidal neurons drove changes in neural circuit synchrony, as well as corresponding anhedonic behavior. These observations resemble imaging and clinical phenotypes observed in human depression, addiction, and schizophrenia. Science, this issue p. 10.1126/science.aac9698; see also p. 10.1126/science.aad9698 Optogenetic and brain imaging approaches reveal a causal brainwide dynamical mechanism for the hedonic-anhedonic transition. [Also see Perspective by Robbins] INTRODUCTION The drive to seek and experience reward is conserved across species and, in mammals, involves interactions between subcortical dopaminergic systems and limbic structures such as the striatum. Impairment of this process, observed across a number of psychiatric conditions, is the clinical symptom of anhedonia (loss of enjoyment). The neural mechanisms underlying anhedonia are unknown but could result from abnormal interactions between cortical and subcortical reward circuits. We sought to test the hypothesis that elevated medial prefrontal cortex (mPFC) excitability (a clinical feature associated with anhedonia) exerts suppressive control over the interactions between two distant subcortical regions: the dopaminergic midbrain and the striatum. RATIONALE Clinical imaging studies have detected elevated activity in the mPFC in human patients with depression, and treatment is associated with normalization of this overactivity and improvement of anhedonic symptoms. Additionally, human studies have identified areas of the brain that respond to reward anticipation and experience, and this response can be suppressed in psychiatric disease. However, the source of this reward signal and the mechanisms underlying its modulation have not been causally demonstrated. We have integrated a diverse set of chronic and acute optogenetic tools with functional magnetic resonance imaging (fMRI) to provide a bridge between the causal, cellular specificity of rodent optogenetics and the brainwide observations that characterize human neuroimaging, with the goal of locally manipulating and globally visualizing neural activity to understand the regulation of reward-seeking behavior. RESULTS We demonstrate that stimulation of midbrain dopamine neurons drives both striatal fMRI blood oxygen level–dependent (BOLD) activity and reward-seeking behavior, and we show that these are correlated across individuals. We additionally find that silencing of dopamine neurons suppresses activity in the striatum, as well as in other brain regions (such as the hypothalamus), and drives avoidance behavior. Having established this bidirectional control of reward-seeking behavior, we then tested for perturbation of this circuitry via elevation of mPFC excitability. We observed suppression of striatal responses to dopamine, as well as the behavioral drive to seek out dopamine neuron stimulation and other natural rewarding stimuli. Finally, we demonstrate that stably elevated mPFC excitability synchronizes corticolimbic BOLD and electrophysiological activity, which in turn can predict anhedonic behavior in individual animals. CONCLUSION Our findings from experiments involving local cell-specific control, simultaneously with global unbiased observation of neural activity, reveal that the mPFC exerts top-down control over midbrain dopaminergic interactions with the striatum and that, when elevated, activity in the mPFC can suppress natural reward-related behavior. Furthermore, we observe that cortical-subcortical neural dynamics work in concert to regulate reward processing. All of these findings have implications for our understanding of natural reward-related physiology and behavior, as well as the pathogenesis of anhedonia. Reward-related signaling between the dopaminergic midbrain and the striatum is under suppressive control by the mPFC. Optogenetic fMRI was used to locally manipulate and globally visualize brainwide neural activity related to reward. Habituated rats were scanned in the awake state (top photographs). We establish that striatal BOLD activity is increased by optogenetic stimulation of dopamine neurons and decreased by optogenetic neural silencing. We demonstrate that focally elevated mPFC excitability suppresses reward-seeking behavior by exerting top-down control over striatal dopamine-induced activity and drives synchrony between specific corticolimbic circuits. Motivation for reward drives adaptive behaviors, whereas impairment of reward perception and experience (anhedonia) can contribute to psychiatric diseases, including depression and schizophrenia. We sought to test the hypothesis that the medial prefrontal cortex (mPFC) controls interactions among specific subcortical regions that govern hedonic responses. By using optogenetic functional magnetic resonance imaging to locally manipulate but globally visualize neural activity in rats, we found that dopamine neuron stimulation drives striatal activity, whereas locally increased mPFC excitability reduces this striatal response and inhibits the behavioral drive for dopaminergic stimulation. This chronic mPFC overactivity also stably suppresses natural reward-motivated behaviors and induces specific new brainwide functional interactions, which predict the degree of anhedonia in individuals. These findings describe a mechanism by which mPFC modulates expression of reward-seeking behavior, by regulating the dynamical interactions between specific distant subcortical regions.

Targeting cells with single vectors using multiple-feature Boolean logic

Precisely defining the roles of specific cell types is an intriguing frontier in the study of intact biological systems and has stimulated the rapid development of genetically encoded tools for observation and control. However, targeting these tools with adequate specificity remains challenging: most cell types are best defined by the intersection of two or more features such as active promoter elements, location and connectivity. Here we have combined engineered introns with specific recombinases to achieve expression of genetically encoded tools that is conditional upon multiple cell-type features, using Boolean logical operations all governed by a single versatile vector. We used this approach to target intersectionally specified populations of inhibitory interneurons in mammalian hippocampus and neurons of the ventral tegmental area defined by both genetic and wiring properties. This flexible and modular approach may expand the application of genetically encoded interventional and observational tools for intact-systems biology.

Wirelessly powered, fully internal optogenetics for brain, spinal and peripheral circuits in mice

To enable sophisticated optogenetic manipulation of neural circuits throughout the nervous system with limited disruption of animal behavior, light-delivery systems beyond fiber optic tethering and large, head-mounted wireless receivers are desirable. We report the development of an easy-to-construct, implantable wireless optogenetic device. Our smallest version (20 mg, 10 mm3) is two orders of magnitude smaller than previously reported wireless optogenetic systems, allowing the entire device to be implanted subcutaneously. With a radio-frequency (RF) power source and controller, this implant produces sufficient light power for optogenetic stimulation with minimal tissue heating (<1 °C). We show how three adaptations of the implant allow for untethered optogenetic control throughout the nervous system (brain, spinal cord and peripheral nerve endings) of behaving mice. This technology opens the door for optogenetic experiments in which animals are able to behave naturally with optogenetic manipulation of both central and peripheral targets.

Closed-Loop and Activity-Guided Optogenetic Control

Advances in optical manipulation and observation of neural activity have set the stage for widespread implementation of closed-loop and activity-guided optical control of neural circuit dynamics. Closing the loop optogenetically (i.e., basing optogenetic stimulation on simultaneously observed dynamics in a principled way) is a powerful strategy for causal investigation of neural circuitry. In particular, observing and feeding back the effects of circuit interventions on physiologically relevant timescales is valuable for directly testing whether inferred models of dynamics, connectivity, and causation are accurate in vivo. Here we highlight technical and theoretical foundations as well as recent advances and opportunities in this area, and we review in detail the known caveats and limitations of optogenetic experimentation in the context of addressing these challenges with closed-loop optogenetic control in behaving animals.

Wave optics theory and 3-D deconvolution for the light field microscope

Light field microscopy is a new technique for high-speed volumetric imaging of weakly scattering or fluorescent specimens. It employs an array of microlenses to trade off spatial resolution against angular resolution, thereby allowing a 4-D light field to be captured using a single photographic exposure without the need for scanning. The recorded light field can then be used to computationally reconstruct a full volume. In this paper, we present an optical model for light field microscopy based on wave optics, instead of previously reported ray optics models. We also present a 3-D deconvolution method for light field microscopy that is able to reconstruct volumes at higher spatial resolution, and with better optical sectioning, than previously reported. To accomplish this, we take advantage of the dense spatio-angular sampling provided by a microlens array at axial positions away from the native object plane. This dense sampling permits us to decode aliasing present in the light field to reconstruct high-frequency information. We formulate our method as an inverse problem for reconstructing the 3-D volume, which we solve using a GPU-accelerated iterative algorithm. Theoretical limits on the depth-dependent lateral resolution of the reconstructed volumes are derived. We show that these limits are in good agreement with experimental results on a standard USAF 1951 resolution target. Finally, we present 3-D reconstructions of pollen grains that demonstrate the improvements in fidelity made possible by our method.

Interpretable Classifiers for fMRI Improve Prediction of Purchases

Despite growing interest in applying machine learning to neuroimaging analyses, few studies have gone beyond classifying sensory input to directly predicting behavioral output. With spatial resolution on the order of millimeters and temporal resolution on the order of seconds, functional magnetic resonance imaging (fMRI) is a promising technology for such applications. However, fMRI datas low signal-to-noise ratio, high dimensionality, and extensive spatiotemporal correlations present formidable analytic challenges. Here, we apply different machine-learning algorithms to previously acquired data to examine the ability of fMRI activation in three regions—the nucleus accumbens (NAcc), medial prefrontal cortex (MPFC), and insula—to predict purchasing. Our goal was to improve spatiotemporal interpretability as well as classification accuracy. To this end, sparse penalized discriminant analysis (SPDA) enabled automatic selection of correlated variables, yielding interpretable models that generalized well to new data. Relative to logistic regression, linear discriminant analysis, and linear support vector machines, SPDA not only increased interpretability but also improved classification accuracy. SPDA promises to allow more precise inferences about when specific brain regions contribute to purchasing decisions. More broadly, this approach provides a general framework for using neuroimaging data to build interpretable models, including those that predict choice.