Different modulation of RPS6 phosphorylation by risperidone in striatal cells sub populations: involvement of the mTOR pathway in antipsychotic-induced extrapyramidal symptoms in mice

Objective: Acute extrapyramidal symptoms (EPS) are frequent and serious adverse reactions to antipsychotic (AP) drugs. Although the proposed mechanism is an excessive blockade of dopamine D2 receptors in the striatopallidal pathway of the striatum, previous studies implicated the mTOR pathway in the susceptibility to EPS. The objective of the present study is to analyze the mTOR-mediated response to risperidone in subpopulations of striatal neurons and its relationship to risperidone-induced motor side effects. Methods: Two mouse strains (A/J and DBA/2J) with different susceptibility to developing EPS were treated with risperidone 1 mg/kg for three consecutive days. Here we monitored, by double labeling immunohistochemistry, ribosomal protein S6 (rpS6) phosphorylation (Ser235/236 and Ser244/247 sites), a marker of mTOR signaling, in the striatonigral pathway (D1-medium spiny neurons (MSNs)), the striatopallidal pathway (D2-MSNs) and striatal cholinergic interneurons. Results: We found that EPS-resistant DBA/2J mice show higher baseline levels of phosphoactivated rpS6 protein in striatal MSNs, compared with EPS-prone A/J mice. Moreover, risperidone differentially targeted rpS6 phosphorylation in direct and indirect pathway neurons in a strain-specific manner: a significant decrease in the phosphorylation of rpS6 at Ser235/236 and Ser240/244 in DRD1-MSNs EPS-resistant DBA/2J mice after; and a significant increase of phospho-Ser235/236-rpS6 in the striatopallidal pathway of the EPS-prone A/J mice in response to risperidone. Conclusions: Our results reveal the vital role of genetic background in the response to risperidone, and point to the mTOR pathway as an important factor in EPS susceptibility. Keywords: Schizophrenia, Antipsychotic, Risperidone, Extrapyramidal symptoms. mTOR pathway, Striatum, Medium spiny neuron

Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control

WOS: 000370402900001PubMed ID: 26321943In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain-machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extra-cellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.Bogazici University BAP Grants [10XD3]; Bogazici University Life Sciences and Technologies Research Center [09K120520]This research was supported by Bogazici University BAP Grants #10XD3 and Bogazici University Life Sciences and Technologies Research Center #09K120520

Mixed Electrical–Chemical Synapses in Adult Rat Hippocampus are Primarily Glutamatergic and Coupled by Connexin-36

Dendrodendritic electrical signaling via gap junctions is now an accepted feature of neuronal communication in mammalian brain, whereas axodendritic and axosomatic gap junctions have rarely been described. We present ultrastructural, immunocytochemical, and dye-coupling evidence for “mixed” (electrical/chemical) synapses on both principal cells and interneurons in adult rat hippocampus. Thin-section electron microscopic images of small gap junction-like appositions were found at mossy fiber (MF) terminals on thorny excrescences of CA3 pyramidal neurons (CA3pyr), apparently forming glutamatergic mixed synapses. Lucifer Yellow injected into weakly fixed CA3pyr was detected in MF axons that contacted four injected CA3pyr, supporting gap junction-mediated coupling between those two types of principal cells. Freeze-fracture replica immunogold labeling revealed diverse sizes and morphologies of connexin-36-containing gap junctions throughout hippocampus. Of 20 immunogold-labeled gap junctions, seven were large (328–1140 connexons), three of which were consistent with electrical synapses between interneurons; but nine were at axon terminal synapses, three of which were immediately adjacent to distinctive glutamate receptor-containing postsynaptic densities, forming mixed glutamatergic synapses. Four others were adjacent to small clusters of immunogold-labeled 10-nm E-face intramembrane particles, apparently representing extrasynaptic glutamate receptor particles. Gap junctions also were on spines in stratum lucidum, stratum oriens, dentate gyrus, and hilus, on both interneurons and unidentified neurons. In addition, one putative GABAergic mixed synapse was found in thin-section images of a CA3pyr, but none were found by immunogold labeling, suggesting the rarity of GABAergic mixed synapses. Cx36-containing gap junctions throughout hippocampus suggest the possibility of reciprocal modulation of electrical and chemical signals in diverse hippocampal neurons

Linking Visual Cortical Development to Visual Perception

Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-95-1-0657

Python Scripting in the Nengo Simulator

Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models

Neural Coordination of Distinct Motor Learning Strategies: Latent Neurofunctional Mechanisms Elucidated via Computational Modeling

In this dissertation, a neurofunctional theory of learning is presented as an extension of functional analysis. This new theory clarifies the distinction— via applied quantitative analysis— between functionally intrinsic (essential) mechanistic structures and irrelevant structural details. This thesis is supported by a review of the relevant literature to provide historical context and sufficient scientific background. Further, the scope of this thesis is elucidated by two questions that are posed from a neurofunctional perspective— (1) how can specialized neuromorphology contribute to the functional dynamics of neural learning processes? (2) Can large-scale neurofunctional pathways emerge via inter-network communication between disparate neural circuits? These questions motivate the specific aims of this dissertation. Each aim is addressed by posing a relevant hypothesis, which is then tested via a neurocomputational experiment. In each experiment, computational techniques are leveraged to elucidate specific mechanisms that underlie neurofunctional learning processes. For instance, the role of specialized neuromorphology is investigated via the development of a computational model that replicates the neurophysiological mechanisms that underlie cholinergic interneurons’ regulation of dopamine in the striatum during reinforcement learning. Another research direction focuses on the emergence of large-scale neurofunctional pathways that connect the cerebellum and basal ganglia— this study also involves the construction of a neurocomputational model. The results of each study illustrate the capability of neurocomputational models to replicate functional learning dynamics of human subjects during a variety of motor adaptation tasks. Finally, the significance— and some potential applications— of neurofunctional theory are discussed

The mechanisms underlying synaptic transmission at the layer 4 of sensory cortical areas

Die Neuronen in Schicht vier (L4) des cerebralen Kortex spielen eine wichtige Rolle bei der Signalübertragung vom Thalamus zu anderen Kortexbereichen. Das Verständnis der grundlegenden Eigenschaften der synaptischen Übertragung zwischen Neuronen in L4 ermöglicht es uns, ein klarerers Bild davon zu erhalten, wie die neuronalen Netzwerke in L4 kooperieren um sensorische Informationen zu verarbeiten. In dieser Studie haben wir für exzitatorische synaptische Verbindungen innerhalb der L4 des visuellen Kortexes (V1) sowie des somatosensorischen Kortexes (S1) der Maus Parameter untersucht, die die synaptische Stärke beeinflussen, wie quantale Größe (q), die Größe des schnell freisetzbaren Vesikelvorrats (N) und die Freisetzungswahrscheinlichkeit (Pr) Während unter physiologischen Bedingungen in V1-Synapsen nur ein Vesikel pro Freisetzungszone freigesetzt wird, wurde bei S1-Synapsen multivesikuläre Freisetzung (MVR) beobachtet. Darüber hinaus konnten wir eine Sättigung der postsynaptischen Rezeptoren bei S1-Synapsen feststellen. Die anderen gemessenen synaptischen Eigenschaften sind in beiden Kortexregionen ähnlich. Experimente mit Dynamic Clamp deuten darauf hin, dass die niedrigere Freisetzungswahrscheinlichkeit sowie die multivesikuläre Freisetzung bei S1-Synapsen dazu führen, dass weniger synaptische Erregungen genügen, um ein Aktionspotential in der postsynaptischen Zelle auszulösen. Zusätzlich dazu tragen der langsamere Abfall des synaptischen Stroms und die intrinsischen Membraneigenschaften der postsynaptischen Zelle zur verlässlichen Signalübertragung zwischen S1-Neuronen bei.Neurons in layer 4 (L4) of the cortex play an important role in transferring signals from thalamus to other layers of the cortex. Understanding the fundamental properties of synaptic transmission between L4 neurons helps us to gain a clear picture of how the neuronal network in L4 co-operates to process sensory information. In the present study, we have determined the underlying parameters that govern synaptic strength such as quantal size (q), size of readily releasable vesicle pool (N) and release probability (Pr) of excitatory synaptic connections within L4 of the visual cortex (V1) and the somatosensory cortex (S1) in mice. While only a single vesicle is released per release site under physiological conditions at V1 synapses, multivesicular release (MVR) is observed at S1 synapses. In addition, we observed a saturation of postsynaptic receptors at S1 synapses. Other synaptic properties are similar in both cortices. Dynamic clamp experiments suggest that higher Pr and MVR at S1 synapses lower the requirement of the number of synaptic inputs to generate postsynaptic action potentials. In addition, the slower decay of synaptic current and the intrinsic membrane properties of the postsynaptic neuron also contribute to the reliable transmission between S1 neurons