58 research outputs found

    Genetic inhibition of neurotransmission reveals role of glutamatergic input to dopamine neurons in high-effort behavior

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    Midbrain dopamine neurons are crucial for many behavioral and cognitive functions. As the major excitatory input, glutamatergic afferents are important for control of the activity and plasticity of dopamine neurons. However, the role of glutamatergic input as a whole onto dopamine neurons remains unclear. Here we developed a mouse line in which glutamatergic inputs onto dopamine neurons are specifically impaired, and utilized this genetic model to directly test the role of glutamatergic inputs in dopamine-related functions. We found that while motor coordination and reward learning were largely unchanged, these animals showed prominent deficits in effort-related behavioral tasks. These results provide genetic evidence that glutamatergic transmission onto dopaminergic neurons underlies incentive motivation, a willingness to exert high levels of effort to obtain reinforcers, and have important implications for understanding the normal function of the midbrain dopamine system.Fil: Hutchison, M. A.. National Institutes of Health; Estados UnidosFil: Gu, X.. National Institutes of Health; Estados UnidosFil: Adrover, Martín Federico. National Institutes of Health; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; ArgentinaFil: Lee, M. R.. National Institutes of Health; Estados UnidosFil: Hnasko, T. S.. University of California at San Diego; Estados UnidosFil: Alvarez, V. A.. National Institutes of Health; Estados UnidosFil: Lu, W.. National Institutes of Health; Estados Unido

    Amygdala inputs to prefrontal cortex guide behavior amid conflicting cues of reward and punishment

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    Orchestrating appropriate behavioral responses in the face of competing signals that predict either rewards or threats in the environment is crucial for survival. The basolateral nucleus of the amygdala (BLA) and prelimbic (PL) medial prefrontal cortex have been implicated in reward-seeking and fear-related responses, but how information flows between these reciprocally connected structures to coordinate behavior is unknown. We recorded neuronal activity from the BLA and PL while rats performed a task wherein competing shock- and sucrose-predictive cues were simultaneously presented. The correlated firing primarily displayed a BLA→PL directionality during the shock-associated cue. Furthermore, BLA neurons optogenetically identified as projecting to PL more accurately predicted behavioral responses during competition than unidentified BLA neurons. Finally photostimulation of the BLA→PL projection increased freezing, whereas both chemogenetic and optogenetic inhibition reduced freezing. Therefore, the BLA→PL circuit is critical in governing the selection of behavioral responses in the face of competing signals.National Institutes of Health (U.S.) (Award 1R25-MH092912-01)National Institute of Mental Health (U.S.) (Grant R01- MH102441-01)National Institutes of Health (U.S.) (Award DP2- DK-102256-01

    Resolving the neural circuits of anxiety

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    Although anxiety disorders represent a major societal problem demanding new therapeutic targets, these efforts have languished in the absence of a mechanistic understanding of this subjective emotional state. While it is impossible to know with certainty the subjective experience of a rodent, rodent models hold promise in dissecting well-conserved limbic circuits. The application of modern approaches in neuroscience has already begun to unmask the neural circuit intricacies underlying anxiety by allowing direct examination of hypotheses drawn from existing psychological concepts. This information points toward an updated conceptual model for what neural circuit perturbations could give rise to pathological anxiety and thereby provides a roadmap for future therapeutic development.National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (NIH Director’s New Innovator Award DP2-DK-102256-01)National Institute of Mental Health (U.S.) (NIH) R01-MH102441-01)JPB Foundatio

    Unravelling the neural networks that drive different eating behaviours

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    Optogenetics

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    Geometry simplification of open-cell porous materials for elastic deformation FEA

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    Estimation of mechanical properties of porous materials is central for their medical and industrial application. However, the massive size of accurate boundary representations (B-Rep) of the foams makes the numerical estimations intractable. Even for small domain sizes, the mesh generation for finite element analysis (FEA) may not terminate. Current efforts for simulating porous materials use statistical predictions of the material structure. The simulated and actual materials present different geometry and topology, with consequences on the simulation results. To overcome these limitations, this manuscript presents a method, which (1) synthesizes an accurate truss abstraction from the raw geometry data, (2) executes efficient FEA simulations, and (3) processes nodal displacements to estimate apparent mechanical moduli of the porous material. The method addresses materials whose ligaments have circular cross-sections. The iso-surface present in the Computer Tomography (CT) scan of the porous material is used to synthesize a truss graph whose edges are truncated cones. Then, optimization and simplification methods are applied to produce a topologically and geometrically correct truss representation for the foam domain. Comparative FEA load simulations are conducted between the full B-Rep and truss representations of the material. The truss model proves to be significantly more efficient for FEA, departing from the Full B-Rep FEA by a maximum of 16% in the estimation of equivalent mechanical moduli. Geometric assessments such as porosity and Hausdorff distance confirm that the truss abstraction is a cost-effective one. Ongoing efforts concentrate on point set geometric algorithms for enforcement of standardized material testing. © 2018 Springer-Verlag London Ltd., part of Springer Natur
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