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.

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.

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.

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.

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.

Activity of aminotransferases as a marker of liver injury in home parenteral nutrition patients.

Aim of the study: Parenteral nutrition associated liver disease (PNALD) is a frequently reported complication of long-term parenteral nutrition. Early diagnosis and treatment of PNALD can help prevent end-stage liver disease. The aim of the study was to evaluate the activity of aminotransferases as a marker of liver dysfunction in patients receiving home parenteral nutrition under the care of a reference center. Material and methods: A comprehensive analysis of patients' medical records from a 9-year period (December 2012 - December 2021) was conducted and the following parameters were evaluated: parenteral nutrition mixture composition, total plasma bilirubin, activity of the liver enzymes alanine aminotransferase (ALT) and aspartate aminotransferase (AST), standardized time factor prothrombin (international normalized ratio [INR] factor) and serum albumin. The analysis covered 630,537 days of parenteral nutrition. The study included 251 patients (140 women and 111 men) included in the Home Parenteral Nutrition Program. Results: PNALD was diagnosed in 11 parenteral fed patients, which gives the frequency of 8.3%/9 years of treatment. Two deaths were classified as cause of death related to liver disease but not related to PNALD. None of the patients included in the analysis developed end-stage liver failure. Conclusions: The above analysis shows that individual selection of the composition of the mixture for intravenous nutrition significantly reduces the risk of PNALD and may prevent liver failure in this context.

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.

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.

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.

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.

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.