BrainTACO: an explorable multi-scale multi-modal brain transcriptomic and connectivity data resource.

Exploring the relationships between genes and brain circuitry can be accelerated by joint analysis of heterogeneous datasets from 3D imaging data, anatomical data, as well as brain networks at varying scales, resolutions, and modalities. Generating an integrated view, beyond the individual resources' original purpose, requires the fusion of these data to a common space, and a visualization that bridges the gap across scales. However, despite ever expanding datasets, few platforms for integration and exploration of this heterogeneous data exist. To this end, we present the BrainTACO (Brain Transcriptomic And Connectivity Data) resource, a selection of heterogeneous, and multi-scale neurobiological data spatially mapped onto a common, hierarchical reference space, combined via a holistic data integration scheme. To access BrainTACO, we extended BrainTrawler, a web-based visual analytics framework for spatial neurobiological data, with comparative visualizations of multiple resources. This enables gene expression dissection of brain networks with, to the best of our knowledge, an unprecedented coverage and allows for the identification of potential genetic drivers of connectivity in both mice and humans that may contribute to the discovery of dysconnectivity phenotypes. Hence, BrainTACO reduces the need for time-consuming manual data aggregation often required for computational analyses in script-based toolboxes, and supports neuroscientists by directly leveraging the data instead of preparing it.

An active inference perspective for the amygdala complex.

The amygdala is a heterogeneous network of subcortical nuclei with central importance in cognitive and clinical neuroscience. Various experimental designs in human psychology and animal model research have mapped multiple conceptual frameworks (e.g., valence/salience and decision making) to ever more refined amygdala circuitry. However, these predominantly bottom up-driven accounts often rely on interpretations tailored to a specific phenomenon, thus preventing comprehensive and integrative theories. We argue here that an active inference model of amygdala function could unify these fractionated approaches into an overarching framework for clearer empirical predictions and mechanistic interpretations. This framework embeds top-down predictive models, informed by prior knowledge and belief updating, within a dynamical system distributed across amygdala circuits in which self-regulation is implemented by continuously tracking environmental and homeostatic demands.

Towards correlative archaeology of the human mind.

Retracing human cognitive origins started out at the systems level with the top-down interpretation of archaeological records spanning from man-made artifacts to endocasts of ancient skulls. With emerging evolutionary genetics and organoid technologies, it is now possible to deconstruct evolutionary processes on a molecular/cellular level from the bottom-up by functionally testing archaic alleles in experimental models. The current challenge is to complement these approaches with novel strategies that allow a holistic reconstruction of evolutionary patterns across human cognitive domains. We argue that computational neuroarcheology can provide such a critical mesoscale framework at the brain network-level, linking molecular/cellular (bottom-up) to systems (top-down) level data for the correlative archeology of the human mind.

Molecular archaeology of human cognitive traits.

The brains and minds of our human ancestors remain inaccessible for experimental exploration. Therefore, we reconstructed human cognitive evolution by projecting nonsynonymous/synonymous rate ratios (ω values) in mammalian phylogeny onto the anatomically modern human (AMH) brain. This atlas retraces human neurogenetic selection and allows imputation of ancestral evolution in task-related functional networks (FNs). Adaptive evolution (high ω values) is associated with excitatory neurons and synaptic function. It shifted from FNs for motor control in anthropoid ancestry (60-41 mya) to attention in ancient hominoids (26-19 mya) and hominids (19-7.4 mya). Selection in FNs for language emerged with an early hominin ancestor (7.4-1.7 mya) and was later accompanied by adaptive evolution in FNs for strategic thinking during recent (0.8 mya-present) speciation of AMHs. This pattern mirrors increasingly complex cognitive demands and suggests that co-selection for language alongside strategic thinking may have separated AMHs from their archaic Denisovan and Neanderthal relatives.

Dissociation of impulsive traits by subthalamic metabotropic glutamate receptor 4.

Behavioral strategies require gating of premature responses to optimize outcomes. Several brain areas control impulsive actions, but the neuronal basis of natural variation in impulsivity between individuals remains largely unknown. Here, by combining a Go/No-Go behavioral assay with resting-state (rs) functional MRI in mice, we identified the subthalamic nucleus (STN), a known gate for motor control in the basal ganglia, as a major hotspot for trait impulsivity. In vivo recorded STN neural activity encoded impulsive action as a separable state from basic motor control, characterized by decoupled STN/substantia nigra pars reticulata (SNr) mesoscale networks. Optogenetic modulation of STN activity bidirectionally controlled impulsive behavior. Pharmacological and genetic manipulations showed that these impulsive actions are modulated by metabotropic glutamate receptor 4 (mGlu4) function in STN and its coupling to SNr in a behavioral trait-dependent manner, and independently of general motor function. In conclusion, STN circuitry multiplexes motor control and trait impulsivity, which are molecularly dissociated by mGlu4. This provides a potential mechanism for the genetic modulation of impulsive behavior, a clinically relevant predictor for developing psychiatric disorders associated with impulsivity.

Dorsal Bed Nucleus of the Stria Terminalis-Subcortical Output Circuits Encode Positive Bias in Pavlovian Fear and Reward.

Opposite emotions like fear and reward states often utilize the same brain regions. The bed nucleus of the stria terminalis (BNST) comprises one hub for processing fear and reward processes. However, it remains unknown how dorsal BNST (dBNST) circuits process these antagonistic behaviors. Here, we exploited a combined Pavlovian fear and reward conditioning task that exposed mice to conditioned tone stimuli (CS)s, either paired with sucrose delivery or footshock unconditioned stimuli (US). Pharmacological inactivation identified the dorsal BNST as a crucial element for both fear and reward behavior. Deep brain calcium imaging revealed opposite roles of two distinct dBNST neuronal output pathways to the periaqueductal gray (PAG) or paraventricular hypothalamus (PVH). dBNST neural activity profiles differentially process valence and Pavlovian behavior components: dBNST-PAG neurons encode fear CS, whereas dBNST-PVH neurons encode reward responding. Optogenetic activation of BNST-PVH neurons increased reward seeking, whereas dBNST-PAG neurons attenuated freezing. Thus, dBNST-PVH or dBNST-PAG circuitry encodes oppositely valenced fear and reward states, while simultaneously triggering an overall positive affective response bias (increased reward seeking while reducing fear responses). We speculate that this mechanism amplifies reward responding and suppresses fear responses linked to BNST dysfunction in stress and addictive behaviors.

Central amygdala circuitry modulates nociceptive processing through differential hierarchical interaction with affective network dynamics.

The central amygdala (CE) emerges as a critical node for affective processing. However, how CE local circuitry interacts with brain wide affective states is yet uncharted. Using basic nociception as proxy, we find that gene expression suggests diverging roles of the two major CE neuronal populations, protein kinase C δ-expressing (PKCδ+) and somatostatin-expressing (SST+) cells. Optogenetic (o)fMRI demonstrates that PKCδ+/SST+ circuits engage specific separable functional subnetworks to modulate global brain dynamics by a differential bottom-up vs. top-down hierarchical mesoscale mechanism. This diverging modulation impacts on nocifensive behavior and may underly CE control of affective processing.

Rapid nucleus-scale reorganization of chromatin in neurons enables transcriptional adaptation for memory consolidation.

The interphase nucleus is functionally organized in active and repressed territories defining the transcriptional status of the cell. However, it remains poorly understood how the nuclear architecture of neurons adapts in response to behaviorally relevant stimuli that trigger fast alterations in gene expression patterns. Imaging of fluorescently tagged nucleosomes revealed that pharmacological manipulation of neuronal activity in vitro and auditory cued fear conditioning in vivo induce nucleus-scale restructuring of chromatin within minutes. Furthermore, the acquisition of auditory fear memory is impaired after infusion of a drug into auditory cortex which blocks chromatin reorganization in vitro. We propose that active chromatin movements at the nucleus scale act together with local gene-specific modifications to enable transcriptional adaptations at fast time scales. Introducing a transgenic mouse line for photolabeling of histones, we extend the realm of systems available for imaging of chromatin dynamics to living animals.

Central amygdala circuit dynamics underlying the benzodiazepine anxiolytic effect.

Benzodiazepines (BZDs) have been a standard treatment for anxiety disorders for decades, but the neuronal circuit interactions mediating their anxiolytic effect remain largely unknown. Here, we find that systemic BZDs modulate central amygdala (CEA) microcircuit activity to gate amygdala output. Combining connectome data with immediate early gene (IEG) activation maps, we identified the CEA as a primary site for diazepam (DZP) anxiolytic action. Deep brain calcium imaging revealed that brain-wide DZP interactions shifted neuronal activity in CEA microcircuits. Chemogenetic silencing showed that PKCδ+/SST- neurons in the lateral CEA (CEAl) are necessary and sufficient to induce the DZP anxiolytic effect. We propose that BZDs block the relay of aversive signals through the CEA, in part by local binding to CEAl SST+/PKCδ- neurons and reshaping intra-CEA circuit dynamics. This work delineates a strategy to identify biomedically relevant circuit interactions of clinical drugs and highlights the critical role for CEA circuitry in the pathophysiology of anxiety.

The amygdala instructs insular feedback for affective learning.

Affective responses depend on assigning value to environmental predictors of threat or reward. Neuroanatomically, this affective value is encoded at both cortical and subcortical levels. However, the purpose of this distributed representation across functional hierarchies remains unclear. Using fMRI in mice, we mapped a discrete cortico-limbic loop between insular cortex (IC), central amygdala (CE), and nucleus basalis of Meynert (NBM), which decomposes the affective value of a conditioned stimulus (CS) into its salience and valence components. In IC, learning integrated unconditioned stimulus (US)-evoked bodily states into CS valence. In turn, CS salience in the CE recruited these CS representations bottom-up via the cholinergic NBM. This way, the CE incorporated interoceptive feedback from IC to improve discrimination of CS valence. Consequently, opto-/chemogenetic uncoupling of hierarchical information flow disrupted affective learning and conditioned responding. Dysfunctional interactions in the IC↔CE/NBM network may underlie intolerance to uncertainty, observed in autism and related psychiatric conditions.

Identification of ALK in Thinness.

There is considerable inter-individual variability in susceptibility to weight gain despite an equally obesogenic environment in large parts of the world. Whereas many studies have focused on identifying the genetic susceptibility to obesity, we performed a GWAS on metabolically healthy thin individuals (lowest 6th percentile of the population-wide BMI spectrum) in a uniquely phenotyped Estonian cohort. We discovered anaplastic lymphoma kinase (ALK) as a candidate thinness gene. In Drosophila, RNAi mediated knockdown of Alk led to decreased triglyceride levels. In mice, genetic deletion of Alk resulted in thin animals with marked resistance to diet- and leptin-mutation-induced obesity. Mechanistically, we found that ALK expression in hypothalamic neurons controls energy expenditure via sympathetic control of adipose tissue lipolysis. Our genetic and mechanistic experiments identify ALK as a thinness gene, which is involved in the resistance to weight gain.

A Data Structure for Real-Time Aggregation Queries of Big Brain Networks.

Recent advances in neuro-imaging allowed big brain-initiatives and consortia to create vast resources of brain data that can be mined by researchers for their individual projects. Exploring the relationship between genes, brain circuitry, and behavior is one of the key elements of neuroscience research. This requires fusion of spatial connectivity data at varying scales, such as whole brain correlated gene expression, structural and functional connectivity. With ever-increasing resolution, these tend to exceed the past state-of-the art in size and complexity by several orders of magnitude. Since current analytical workflows in neuroscience involve time-consuming manual data-aggregation, incorporating efficient techniques for handling big connectivity data is a necessity. We propose a novel data structure enabling the interactive exploration of heterogeneous neurobiological connectivity data with billions of edges. Based on this data structure we realized Aggregation Queries, i.e. the aggregated connectivity from, to or between brain areas allows experts to compare the multimodal networks residing at different scales, or levels of hierarchically organized anatomical atlases. Executed on-demand on volumetric gene expression and connectivity data, they allow an interactive dissection of networks in real-time and based on their spatial context. The data structure is optimized in order to be accessible directly from the hard disk, since connectivity of large-scale networks typically exceeds the memory size of current consumer level PCs. This allows experts to embed and explore their own experimental data in the framework of public data resources without the need for their own large-scale infrastructure. Our data structure outperforms state-of-the-art graph engines in retrieving connectivity of arbitrary user defined local brain areas. We demonstrate the feasibility of our approach by analyzing fear-related functional neuroanatomy in mice. Further, we show its versatility by comparing multimodal brain networks linked to autism. Importantly, we achieve cross-species congruence in retrieving human psychiatric traits networks, which facilitates the selection of neural substrates to be further studied in mouse models.

Stress peptides sensitize fear circuitry to promote passive coping.

Survival relies on optimizing behavioral responses through experience. Animals often react to acute stress by switching to passive behavioral responses when coping with environmental challenge. Despite recent advances in dissecting mammalian circuitry for Pavlovian fear, the neuronal basis underlying this form of non-Pavlovian anxiety-related behavioral plasticity remains poorly understood. Here, we report that aversive experience recruits the posterior paraventricular thalamus (PVT) and corticotropin-releasing hormone (CRH) and sensitizes a Pavlovian fear circuit to promote passive responding. Site-specific lesions and optogenetic manipulations reveal that PVT-to-central amygdala (CE) projections activate anxiogenic neuronal populations in the CE that release local CRH in response to acute stress. CRH potentiates basolateral (BLA)-CE connectivity and antagonizes inhibitory gating of CE output, a mechanism linked to Pavlovian fear, to facilitate the switch from active to passive behavior. Thus, PVT-amygdala fear circuitry uses inhibitory gating in the CE as a shared dynamic motif, but relies on different cellular mechanisms (postsynaptic long-term potentiation vs. presynaptic facilitation), to multiplex active/passive response bias in Pavlovian and non-Pavlovian behavioral plasticity. These results establish a framework promoting stress-induced passive responding, which might contribute to passive emotional coping seen in human fear- and anxiety-related disorders.

Spectral recording of gene expression history by fluorescent timer protein.

Monitoring spatio-temporal patterns of gene expression by fluorescent proteins requires longitudinal observation, which is often difficult to implement. Here, we fuse a fluorescent timer (FT) protein with an immediate early gene (IEG) promoter to track live gene expression in single cells. This results in a stimulus- and time-dependent spectral shift from blue to red for subsequent monitoring with fluorescence activated cell sorting (FACS) and live cell imaging. This spectral shift enables imputing the time point of activity post-hoc to dissociate early and late responders from a single snapshot in time. Thus, we provide a tool for tracking stimulus-driven IEG expression and demonstrate proof of concept exploiting promoter::FT fusions, adding new dimensions to experiments that require reconstructing spatio-temporal patterns of gene expression in cells, tissues or living organisms.

Tinbergen's challenge for the neuroscience of behavior.

Nobel laureate Nikolaas Tinbergen provided clear criteria for declaring a neuroscience problem solved, criteria which despite the passage of more than 50 years and vastly expanded neuroscience tool kits remain applicable today. Tinbergen said for neuroscientists to claim that a behavior is understood, they must correspondingly understand its (i) development and its (ii) mechanisms and its (iii) function and its (iv) evolution. Now, all four of these domains represent hotbeds of current experimental work, each using arrays of new techniques which overlap only partly. Thus, as new methodologies come online, from single-nerve-cell RNA sequencing, for example, to smart FISH, large-scale calcium imaging from cortex and deep brain structures, computational ethology, and so on, one person, however smart, cannot master everything. Our response to the likely "fracturing" of neuroscience recognizes the value of ever larger consortia. This response suggests new kinds of problems for (i) funding and (ii) the fair distribution of credit, especially for younger scientists.

A role for miR-132 in learned safety.

Learned safety is a fear inhibitory mechanism, which regulates fear responses, promotes episodes of safety and generates positive affective states. Despite its potential as experimental model for several psychiatric illnesses, including post-traumatic stress disorder and depression, the molecular mechanisms of learned safety remain poorly understood, We here investigated the molecular mediators of learned safety, focusing on the characterization of miRNA expression in the basolateral amygdala (BLA). Comparing levels of 22 miRNAs in learned safety and learned fear trained mice, six safety-related miRNAs, including three members of the miR-132/-212 family, were identified. A gain-of-function approach based upon in-vivo transfection of a specific miRNA mimic, and miR-132/212 knock-out mice as loss-of-function tool were used in order to determine the relevance of miR-132 for learned safety at the behavioral and the neuronal functional levels. Using a designated bioinformatic approach, PTEN and GAT1 were identified as potential novel miR-132 target genes and further experimentally validated. We here firstly provide evidence for a regulation of amygdala miRNA expression in learned safety and propose miR-132 as signature molecule to be considered in future preclinical and translational approaches testing the transdiagnostic relevance of learned safety as intermediate phenotype in fear and stress-related disorders.

Dorsal tegmental dopamine neurons gate associative learning of fear.

Functional neuroanatomy of Pavlovian fear has identified neuronal circuits and synapses associating conditioned stimuli with aversive events. Hebbian plasticity within these networks requires additional reinforcement to store particularly salient experiences into long-term memory. Here we have identified a circuit that reciprocally connects the ventral periaqueductal gray and dorsal raphe region with the central amygdala and that gates fear learning. We found that ventral periaqueductal gray and dorsal raphe dopaminergic (vPdRD) neurons encode a positive prediction error in response to unpredicted shocks and may reshape intra-amygdala connectivity via a dopamine-dependent form of long-term potentiation. Negative feedback from the central amygdala to vPdRD neurons might limit reinforcement to events that have not been predicted. These findings add a new module to the midbrain dopaminergic circuit architecture underlying associative reinforcement learning and identify vPdRD neurons as a critical component of Pavlovian fear conditioning. We propose that dysregulation of vPdRD neuronal activity may contribute to fear-related psychiatric disorders.

Predicting functional neuroanatomical maps from fusing brain networks with genetic information.

Functional neuroanatomical maps provide a mesoscale reference framework for studies from molecular to systems neuroscience and psychiatry. The underlying structure-function relationships are typically derived from functional manipulations or imaging approaches. Although highly informative, these are experimentally costly. The increasing amount of publicly available brain and genetic data offers a rich source that could be mined to address this problem computationally. Here, we developed an algorithm that fuses gene expression and connectivity data with functional genetic meta data and exploits cumulative effects to derive neuroanatomical maps related to multi-genic functions. We validated the approach by using public available mouse and human data. The generated neuroanatomical maps recapture known functional anatomical annotations from literature and functional MRI data. When applied to multi-genic meta data from mouse quantitative trait loci (QTL) studies and human neuropsychiatric databases, this method predicted known functional maps underlying behavioral or psychiatric traits. Taken together, genetically weighted connectivity analysis (GWCA) allows for high throughput functional exploration of brain anatomy in silico. It maps functional genetic associations onto brain circuitry for refining functional neuroanatomy, or identifying trait-associated brain circuitry, from genetic data.

Virtual reality for freely moving animals.

Standard animal behavior paradigms incompletely mimic nature and thus limit our understanding of behavior and brain function. Virtual reality (VR) can help, but it poses challenges. Typical VR systems require movement restrictions but disrupt sensorimotor experience, causing neuronal and behavioral alterations. We report the development of FreemoVR, a VR system for freely moving animals. We validate immersive VR for mice, flies, and zebrafish. FreemoVR allows instant, disruption-free environmental reconfigurations and interactions between real organisms and computer-controlled agents. Using the FreemoVR platform, we established a height-aversion assay in mice and studied visuomotor effects in Drosophila and zebrafish. Furthermore, by photorealistically mimicking zebrafish we discovered that effective social influence depends on a prospective leader balancing its internally preferred directional choice with social interaction. FreemoVR technology facilitates detailed investigations into neural function and behavior through the precise manipulation of sensorimotor feedback loops in unrestrained animals.

Optimizing and extending light-sculpting microscopy for fast functional imaging in neuroscience.

A number of questions in system biology such as understanding how dynamics of neuronal networks are related to brain function require the ability to capture the functional dynamics of large cellular populations at high speed. Recently, this has driven the development of a number of parallel and high speed imaging techniques such as light-sculpting microscopy, which has been used to capture neuronal dynamics at the whole brain and single cell level in small model organisms. However, the broader applicability of light-sculpting microcopy is limited by the size of volumes for which high speed imaging can be obtained and scattering in brain tissue. Here, we present strategies for optimizing the present tradeoffs in light-sculpting microscopy. Various scanning modalities in light-sculpting microscopy are theoretically and experimentally evaluated, and strategies to maximize the obtainable volume speeds, and depth penetration in brain tissue using different laser systems are provided. Design-choices, important parameters and their trade-offs are experimentally demonstrated by performing calcium-imaging in acute mouse-brain slices. We further show that synchronization of line-scanning techniques with rolling-shutter read-out of the camera can reduce scattering effects and enhance image contrast at depth.