Target cell-type specificity of a cortico-amygdala long range synaptic connection

Ayah Khubieh
EPFL, 2021
Thèse EPFL

Résumé

An important function of the brain is to analyze sensory information, and to modulate animal behaviour according to previous experience. During processes of emotional learning, sensory percepts with a reinforcing quality, also called unconditioned stimuli (US), influence the quality of innocuous sensory stimuli. A brain structure involved in the integration of innocuous and reinforcing sensory stimuli is the lateral amygdala (LA), an input station to further amygdalar circuits. Little is known about which upstream brain regions convey US-information to the LA; however, evidence indicates that the posterior insular cortex, which processes nociceptive somatosensory information, might be a candidate. The LA microcircuit is composed of principal neurons and inhibitory interneurons; the latter play a prominent role in the control of local activity and plasticity. To investigate how the posterior insular cortex might recruit LA neurons, we used optogenetically-assisted circuit mapping in combination with genetic identification of cell types with Cre-mouse lines. Specifically, a VGluT2-Cre mouse line, producing a marker for excitatory neurons, and VIP-Cre and SOM-Cre mice as markers for two classes of inhibitory interneurons, were used. We found that VGluT2-Cre+ neurons received strong excitatory and feedforward inhibitory inputs from the posterior insular cortex. The excitatory input was sufficient to induce action potential firing, hence engaging the local circuit. VIP-Cre+ interneurons were moderately excited by posterior insular cortex input, and received strong feedforward inhibition. Both VIP-Cre+ and SOM-Cre+ interneurons also received feedforward, polysynaptic excitation, likely the result of the activity of local principal neurons. Finally, SOM-Cre+ interneurons received moderate excitatory and feedforward inhibitory input from the posterior insular cortex, and their connectivity probability was high. This study supports the notion that the posterior insular cortex can convey US-information to the LA, with the potential to strongly activate LA principal neurons. However, it suggests that a disinhibition of LA principal neurons through VIP interneurons, which has been observed in previous studies, might be shaped by additional afferents, possibly including neuromodulatory inputs. Through systematic recordings of input connections to defined neuron types in the LA, this study provides an entry point to the understanding of the synaptic connectivity rules that govern the activation of amygdalar circuits by incoming long-range cortical inputs.

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https://infoscience.epfl.ch/record/283687?ln=fr

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Ayah Khubieh
EPFL, 2021
Thèse EPFL

Résumé

Official source

https://infoscience.epfl.ch/record/283687?ln=fr

À propos de ce résultat

Concepts associés (19)

Concepts associés

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Publications associées (30)

Publications associées

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Animals are capable of evaluating sensory cues for possible threats and adapting their behaviours accordingly. Fear learning is an evolutionarily conserved behaviour crucial for animal survival, during which sensory percepts with a negative reinforcing quality, also called unconditioned stimuli (US), influence the valence of initially neutral sensory cues (conditioned stimulus, CS). The lateral amygdala (LA) is thought to integrate information about sensory cues like tones (the CS), and information about primary reinforcers like painful stimuli (the US). Nevertheless, synaptic afferents to the LA and upstream brain areas remain understudied in fear learning. The posterior insular cortex (pInsCx) has been shown to project to the LA and is known to be a multimodal sensory area which processes auditory, somatosensory-nociceptive, and interoceptive information, making it an interesting brain area with a putative role in fear learning. My PhD thesis therefore aimed to investigate a possible connection from the pInsCx to the LA, and the role of this pathway in the fear learning circuitry. I first used anatomical anterograde and retrograde tracing strategies in the mouse to study the afferents to the LA. Besides previously known inputs, I found that the granular (GI) and the dysgranular (DI) areas of the pInsCx strongly project to the anterior LA. With optogenetically-assisted circuit mapping (OACM), I found a glutamatergic monosynaptic connection from the pInsCx to LA principal neurons that, in agreement with the anatomical findings, was localized more to the anterior LA. In comparison, a well-studied auditory thalamic (MGm/PIN) input was localized more to the posterior LA. Despite this regional input specificity, ~30% LA principal neurons received projections from both areas. Thus, in the first part, using anatomical tracing and OACM, I have unambiguously shown a robust functional excitatory connection from the pInsCx (GI/DI) to the LA.In the second part of my thesis, I aimed to characterize the role of the pInsCx and the pInsCx-LA pathway in fear learning. Using archaerhodopsin-mediated optogenetic inhibition, I found that silencing of footshock-driven activity of pInsCx neurons in-vivo, partially blocks auditory-cued fear memory. These results were also supported by chemogenetic silencing of the pInsCx during the training day, which led to a modest impairment of fear retrieval a day later. Next, to investigate the role of the pInsCx-LA projection more specifically, I used a retrograde expression approach to silence LA-projectors during the footshock, which again, suppressed fear memory one day later. In further ex-vivo experiments, I found that EPSCs at the pInsCx-LA connection exhibited an increased AMPA/NMDA ratio following fear learning, indicating that plasticity occurs at this pathway during fear learning. Finally, miniature microscopic Ca2+ imaging of LA neurons combined with halorhodopsin-mediated axon inhibition, showed that the pInsCx-LA excitatory synapse contributes to transmitting footshock information to a sub-population of LA neurons. This study unravels the relationship between two serial nodes, the pInsCx and the LA, in the fear learning network. Thus, the pInsCx-LA connection is a novel pathway in auditory-cued fear memory that is strengthened with learning and that partly provides aversive information to the amygdala.

EPFL2022

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A path-integral approach is developed for the analysis of spike-triggered-average quantities in neurons with voltage-gated subthreshold currents. Using a linearization procedure to reduce the models to the generalized integrate-and-fire form, analytical expressions are obtained in an experimentally relevant limit of fluctuation-driven firing. The influences of voltage-gated channels as well as excitatory and inhibitory synaptic filtering are shown to affect significantly the neuronal dynamics prior to the spike. Analytical forms are given for all relevant physiological quantities, such as the mean voltage triggered to the spike, mean current flowing through voltage-gated channels and the mean excitatory and inhibitory conductance wave forms prior to a spike. The mathematical results are shown to be in good agreement with numerical simulations of the underlying non-linear conductance-based models. The method promises to provide a useful analytical tool for the prediction and interpretation of the temporal structure of spike-triggered averages measured experimentally.

2008

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Data-driven whole mouse brain modeling for multi-scale simulations

Dimitri Bruno Marie Rodarie

In this thesis, we present a data-driven iterative pipeline to generate, simulate and validate point-neuron models of the whole mouse brain. The ultimate goal is to replicate close loop experiments with a virtual body in a virtual world. This pipeline was origi-nally created by a PhD student of the Blue Brain Project and this pioneer work has yielded the first versions of the model and their simulations. My objective is to refine, extend and consolidate the previous version of the workflow and in particular to extend the repertoire of neuron types by integrating and consolidating available literature data.The pipeline has four main parts. First, we define a pair of reference atlases to create a spatial reference for every dataset that we want to integrate. Second, we create a cell atlas with estimates of the number and density of the major cell types in each brain region. Then, we build a connectivity atlas that describes the connections of each neuron of the mouse brain and their properties. Finally, we assign point-neuron parameters to each of the neuron types, and synaptic parameters to each of the connection types of our model to obtain a point-neuron network of the mouse brain. This network can be simulated to perform mouse brain experi-ments in-silico.In this thesis, we evaluate the quality of the different reference atlases, released by the Allen Institute for Brain Science and pro-vide methods to mitigate the impact of artifacts in the original images. We further extend the cell atlas with density estimates of more inhibitory neuron types. To do so, we introduce a new method to combine estimates of inhibitory neuron counts from the literature into a consistent framework. We also estimate inhibitory neuron density in regions where no literature data are avai-lable, using collected literature estimates and gene expression data from in situ hybridization image stacks. Our approach can be further extended to other cell types and provides a resource to build more detailed circuits of the mouse brain. Next, we refine the connectivity atlas, including literature findings on short-range connectivity. Additionally, we construct a database to store data collected on neuron types point-neuron parameters and synaptic parameters. With the results of the previous steps, we generate a new version of the whole mouse brain point-neuron network. We tested this new model against its previous version using three benchmark experiments. We explore the possibility to embed and connect detailed circuit reconstructions of the mouse brain to our point neuron model and co-simulate them. Finally, we constrain a 3D skeleton model of the mouse with joint constraints from the literature and attach muscle to its bones to create a musculoskeletal model of the mouse body that can be simulated in close-loop with our model to reproduce motor control experiments.

EPFL2022

Chargement

EPFL2022

Data-driven whole mouse brain modeling for multi-scale simulations

Dimitri Bruno Marie Rodarie

EPFL2022