Neuronal Dynamics Regulating Brain and Behavioral State Transitions.

Prolonged behavioral challenges can cause animals to switch from active to passive coping strategies to manage effort-expenditure during stress; such normally adaptive behavioral state transitions can become maladaptive in psychiatric disorders such as depression. The underlying neuronal dynamics and brainwide interactions important for passive coping have remained unclear. Here, we develop a paradigm to study these behavioral state transitions at cellular-resolution across the entire vertebrate brain. Using brainwide imaging in zebrafish, we observed that the transition to passive coping is manifested by progressive activation of neurons in the ventral (lateral) habenula. Activation of these ventral-habenula neurons suppressed downstream neurons in the serotonergic raphe nucleus and caused behavioral passivity, whereas inhibition of these neurons prevented passivity. Data-driven recurrent neural network modeling pointed to altered intra-habenula interactions as a contributory mechanism. These results demonstrate ongoing encoding of experience features in the habenula, which guides recruitment of downstream networks and imposes a passive coping behavioral strategy.

SPED Light Sheet Microscopy: Fast Mapping of Biological System Structure and Function.

The goal of understanding living nervous systems has driven interest in high-speed and large field-of-view volumetric imaging at cellular resolution. Light sheet microscopy approaches have emerged for cellular-resolution functional brain imaging in small organisms such as larval zebrafish, but remain fundamentally limited in speed. Here, we have developed SPED light sheet microscopy, which combines large volumetric field-of-view via an extended depth of field with the optical sectioning of light sheet microscopy, thereby eliminating the need to physically scan detection objectives for volumetric imaging. SPED enables scanning of thousands of volumes-per-second, limited only by camera acquisition rate, through the harnessing of optical mechanisms that normally result in unwanted spherical aberrations. We demonstrate capabilities of SPED microscopy by performing fast sub-cellular resolution imaging of CLARITY mouse brains and cellular-resolution volumetric Ca(2+) imaging of entire zebrafish nervous systems. Together, SPED light sheet methods enable high-speed cellular-resolution volumetric mapping of biological system structure and function.

Natural neural projection dynamics underlying social behavior.

Social interaction is a complex behavior essential for many species and is impaired in major neuropsychiatric disorders. Pharmacological studies have implicated certain neurotransmitter systems in social behavior, but circuit-level understanding of endogenous neural activity during social interaction is lacking. We therefore developed and applied a new methodology, termed fiber photometry, to optically record natural neural activity in genetically and connectivity-defined projections to elucidate the real-time role of specified pathways in mammalian behavior. Fiber photometry revealed that activity dynamics of a ventral tegmental area (VTA)-to-nucleus accumbens (NAc) projection could encode and predict key features of social, but not novel object, interaction. Consistent with this observation, optogenetic control of cells specifically contributing to this projection was sufficient to modulate social behavior, which was mediated by type 1 dopamine receptor signaling downstream in the NAc. Direct observation of deep projection-specific activity in this way captures a fundamental and previously inaccessible dimension of mammalian circuit dynamics.

Fast near-whole-brain imaging in adult Drosophila during responses to stimuli and behavior.

Whole-brain recordings give us a global perspective of the brain in action. In this study, we describe a method using light field microscopy to record near-whole brain calcium and voltage activity at high speed in behaving adult flies. We first obtained global activity maps for various stimuli and behaviors. Notably, we found that brain activity increased on a global scale when the fly walked but not when it groomed. This global increase with walking was particularly strong in dopamine neurons. Second, we extracted maps of spatially distinct sources of activity as well as their time series using principal component analysis and independent component analysis. The characteristic shapes in the maps matched the anatomy of subneuropil regions and, in some cases, a specific neuron type. Brain structures that responded to light and odor were consistent with previous reports, confirming the new technique's validity. We also observed previously uncharacterized behavior-related activity as well as patterns of spontaneous voltage activity.

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.

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 mm(3)) 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.

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.

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.

Enhancing the performance of the light field microscope using wavefront coding.

Light field microscopy has been proposed as a new high-speed volumetric computational imaging method that enables reconstruction of 3-D volumes from captured projections of the 4-D light field. Recently, a detailed physical optics model of the light field microscope has been derived, which led to the development of a deconvolution algorithm that reconstructs 3-D volumes with high spatial resolution. However, the spatial resolution of the reconstructions has been shown to be non-uniform across depth, with some z planes showing high resolution and others, particularly at the center of the imaged volume, showing very low resolution. In this paper, we enhance the performance of the light field microscope using wavefront coding techniques. By including phase masks in the optical path of the microscope we are able to address this non-uniform resolution limitation. We have also found that superior control over the performance of the light field microscope can be achieved by using two phase masks rather than one, placed at the objective's back focal plane and at the microscope's native image plane. We present an extended optical model for our wavefront coded light field microscope and develop a performance metric based on Fisher information, which we use to choose adequate phase masks parameters. We validate our approach using both simulated data and experimental resolution measurements of a USAF 1951 resolution target; and demonstrate the utility for biological applications with in vivo volumetric calcium imaging of larval zebrafish brain.

Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample.

BACKGROUND: Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample. METHODS: We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes. RESULTS: The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation. CONCLUSIONS: Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.

Interpretable whole-brain prediction analysis with GraphNet.

Multivariate machine learning methods are increasingly used to analyze neuroimaging data, often replacing more traditional "mass univariate" techniques that fit data one voxel at a time. In the functional magnetic resonance imaging (fMRI) literature, this has led to broad application of "off-the-shelf" classification and regression methods. These generic approaches allow investigators to use ready-made algorithms to accurately decode perceptual, cognitive, or behavioral states from distributed patterns of neural activity. However, when applied to correlated whole-brain fMRI data these methods suffer from coefficient instability, are sensitive to outliers, and yield dense solutions that are hard to interpret without arbitrary thresholding. Here, we develop variants of the Graph-constrained Elastic-Net (GraphNet), a fast, whole-brain regression and classification method developed for spatially and temporally correlated data that automatically yields interpretable coefficient maps (Grosenick et al., 2009b). GraphNet methods yield sparse but structured solutions by combining structured graph constraints (based on knowledge about coefficient smoothness or connectivity) with a global sparsity-inducing prior that automatically selects important variables. Because GraphNet methods can efficiently fit regression or classification models to whole-brain, multiple time-point data sets and enhance classification accuracy relative to volume-of-interest (VOI) approaches, they eliminate the need for inherently biased VOI analyses and allow whole-brain fitting without the multiple comparison problems that plague mass univariate and roaming VOI ("searchlight") methods. As fMRI data are unlikely to be normally distributed, we (1) extend GraphNet to include robust loss functions that confer insensitivity to outliers, (2) equip them with "adaptive" penalties that asymptotically guarantee correct variable selection, and (3) develop a novel sparse structured Support Vector GraphNet classifier (SVGN). When applied to previously published data (Knutson et al., 2007), these efficient whole-brain methods significantly improved classification accuracy over previously reported VOI-based analyses on the same data (Grosenick et al., 2008; Knutson et al., 2007) while discovering task-related regions not documented in the original VOI approach. Critically, GraphNet estimates fit to the Knutson et al. (2007) data generalize well to out-of-sample data collected more than three years later on the same task but with different subjects and stimuli (Karmarkar et al., submitted for publication). By enabling robust and efficient selection of important voxels from whole-brain data taken over multiple time points (>100,000 "features"), these methods enable data-driven selection of brain areas that accurately predict single-trial behavior within and across individuals.

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.

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.

Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder.

The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood. Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that predicted individual differences in ASD behaviors and were stable in cross-validation. Clustering along these three dimensions revealed four reproducible ASD subgroups with distinct functional connectivity alterations in ASD-related networks and clinical symptom profiles that were reproducible in an independent sample. By integrating neuroimaging data with normative gene expression data from two independent transcriptomic atlases, we found that within each subgroup, ASD-related functional connectivity was explained by regional differences in the expression of distinct ASD-related gene sets. These gene sets were differentially associated with distinct molecular signaling pathways involving immune and synapse function, G-protein-coupled receptor signaling, protein synthesis and other processes. Collectively, our findings delineate atypical connectivity patterns underlying different forms of ASD that implicate distinct molecular signaling mechanisms.

Bidirectional Regulation of Motor Circuits Using Magnetogenetic Gene Therapy.

Regulating the activity of discrete neuronal populations in living mammals after delivery of modified ion channels can be used to map functional circuits and potentially treat neurological diseases. Here we report a novel suite of magnetogenetic tools, based on a single anti-ferritin nanobody-TRPV1 receptor fusion protein, which regulated neuronal activity in motor circuits when exposed to magnetic fields. AAV-mediated delivery of a cre-dependent nanobody-TRPV1 calcium channel into the striatum of adenosine 2a (A2a) receptor-cre driver mice led to restricted expression within D2 neurons, resulting in motor freezing when placed in a 3T MRI or adjacent to a transcranial magnetic stimulation (TMS) device. Functional imaging and fiber photometry both confirmed focal activation of the target region in response to the magnetic fields. Expression of the same construct in the striatum of wild-type mice along with a second injection of an AAVretro expressing cre into the globus pallidus led to similar circuit specificity and motor responses. Finally, a mutation was generated to gate chloride and inhibit neuronal activity. Expression of this variant in subthalamic nucleus (STN) projection neurons in PitX2-cre parkinsonian mice resulted in reduced local c-fos expression and a corresponding improvement in motor rotational behavior during magnetic field exposure. These data demonstrate that AAV delivery of magnetogenetic constructs can bidirectionally regulate activity of specific neuronal circuits non-invasively in vivo using clinically available devices for both preclinical analysis of circuit effects on behavior and potential human clinical translation.

Regulation of social interaction in mice by a frontostriatal circuit modulated by established hierarchical relationships.

Social hierarchies exert a powerful influence on behavior, but the neurobiological mechanisms that detect and regulate hierarchical interactions are not well understood, especially at the level of neural circuits. Here, we use fiber photometry and chemogenetic tools to record and manipulate the activity of nucleus accumbens-projecting cells in the ventromedial prefrontal cortex (vmPFC-NAcSh) during tube test social competitions. We show that vmPFC-NAcSh projections signal learned hierarchical relationships, and are selectively recruited by subordinate mice when they initiate effortful social dominance behavior during encounters with a dominant competitor from an established hierarchy. After repeated bouts of social defeat stress, this circuit is preferentially activated during social interactions initiated by stress resilient individuals, and plays a necessary role in supporting social approach behavior in subordinated mice. These results define a necessary role for vmPFC-NAcSh cells in the adaptive regulation of social interaction behavior based on prior hierarchical interactions.

Expansion of a frontostriatal salience network in individuals with depression.

Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers. 1 Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals, 2-5 but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable trait-like brain network topology that may confer risk for depression and mood-state dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

Functional and Optogenetic Approaches to Discovering Stable Subtype-Specific Circuit Mechanisms in Depression.

BACKGROUND: Previously, we identified four depression subtypes defined by distinct functional connectivity alterations in depression-related brain networks, which in turn predicted clinical symptoms and treatment response. Optogenetic functional magnetic resonance imaging offers a promising approach for testing how dysfunction in specific circuits gives rise to subtype-specific, depression-related behaviors. However, this approach assumes that there are robust, reproducible correlations between functional connectivity and depressive symptoms-an assumption that was not extensively tested in previous work. METHODS: First, we comprehensively reevaluated the stability of canonical correlations between functional connectivity and symptoms (N = 220 subjects) using optimized approaches for large-scale statistical hypothesis testing, and we validated methods for improving estimation of latent variables driving brain-behavior correlations. Having confirmed this necessary condition, we reviewed recent advances in optogenetic functional magnetic resonance imaging and illustrated one approach to formulating hypotheses regarding latent subtype-specific circuit mechanisms and testing them in animal models. RESULTS: Correlations between connectivity features and clinical symptoms were robustly significant, and canonical correlation analysis solutions tested repeatedly on held-out data generalized. However, they were sensitive to data quality, preprocessing, and clinical heterogeneity, which can reduce effect sizes. Generalization could be markedly improved by adding L2 regularization, which decreased estimator variance, increased canonical correlations in left-out data, and stabilized feature selection. These improvements were useful for identifying candidate circuits for optogenetic interrogation in animal models. CONCLUSIONS: Multiview, latent-variable approaches such as canonical correlation analysis offer a conceptually useful framework for discovering stable patient subtypes by synthesizing multiple clinical and functional measures. Optogenetic functional magnetic resonance imaging holds promise for testing hypotheses regarding latent, subtype-specific mechanisms driving depressive symptoms and behaviors.

Coordinated Reductions in Excitatory Input to the Nucleus Accumbens Underlie Food Consumption.

Reward-seeking behavior is regulated by a diverse collection of inputs to the nucleus accumbens (NAc). The information encoded in each excitatory afferent to the NAc is unknown, in part because it is unclear when these pathways are active in relation to behavior. Here we compare the activity profiles of amygdala, hippocampal, and thalamic inputs to the NAc shell in mice performing a cued reward-seeking task using GCaMP-based fiber photometry. We find that the rostral and caudal ends of the NAc shell are innervated by distinct but intermingled populations of forebrain neurons that exhibit divergent feeding-related activity. In the rostral NAc shell, a coordinated network-wide reduction in excitatory drive correlates with feeding, and reduced input from individual pathways is sufficient to promote it. Overall, the data suggest that pathway-specific input activity at a population level may vary more across the NAc than between pathways.

Expression, structure, function, and evolution of gonadotropin-releasing hormone (GnRH) receptors GnRH-R1SHS and GnRH-R2PEY in the teleost, Astatotilapia burtoni.

Multiple GnRH receptors are known to exist in nonmammalian species, but it is uncertain which receptor type regulates reproduction via the hypothalamic-pituitary-gonadal axis. The teleost fish, Astatotilapia burtoni, is useful for identifying the GnRH receptor responsible for reproduction, because only territorial males reproduce. We have cloned a second GnRH receptor in A. burtoni, GnRH-R1(SHS) (SHS is a peptide motif in extracellular loop 3), which is up-regulated in pituitaries of territorial males. We have shown that GnRH-R1(SHS) is expressed in many tissues and specifically colocalizes with LH in the pituitary. In A. burtoni brain, mRNA levels of both GnRH-R1(SHS) and a previously identified receptor, GnRH-R2(PEY), are highly correlated with mRNA levels of all three GnRH ligands. Despite its likely role in reproduction, we found that GnRH-R1(SHS) has the highest affinity for GnRH2 in vitro and low responsivity to GnRH1. Our phylogenetic analysis shows that GnRH-R1(SHS) is less closely related to mammalian reproductive GnRH receptors than GnRH-R2(PEY). We correlated vertebrate GnRH receptor amino acid sequences with receptor function and tissue distribution in many species and found that GnRH receptor sequences predict ligand responsiveness but not colocalization with pituitary gonadotropes. Based on sequence analysis, tissue localization, and physiological response we propose that the GnRH-R1(SHS) receptor controls reproduction in teleosts, including A. burtoni. We propose a GnRH receptor classification based on gene sequence that correlates with ligand selectivity but not with reproductive control. Our results suggest that different duplicated GnRH receptor genes have been selected to regulate reproduction in different vertebrate lineages.