Pathophysiology of dystonia: A neuronal model

2002 ◽  
Vol 17 (S3) ◽  
pp. S49-S62 ◽  
Author(s):  
Jerrold L. Vitek
Keyword(s):  
Neuronal Model

An Unifying Neuronal Model for Normal and Abnormal Thinking

2016 ◽  
Author(s):  
Daniele Quintella Mendes ◽  
Luís Alfredo V. Carvalho ◽  
Roseli S. Wedemann
Keyword(s):  
Neuronal Model

scholarly journals High-content image-based analysis and proteomic profiling identifies Tau phosphorylation inhibitors in a human iPSC-derived glutamatergic neuronal model of tauopathy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chialin Cheng ◽  
Surya A. Reis ◽  
Emily T. Adams ◽  
Daniel M. Fass ◽  
Steven P. Angus ◽  
...  
Keyword(s):  
High Throughput Screening ◽  
Neural Progenitor Cells ◽  
Kinase Inhibitors ◽  
Ex Vivo ◽  
Clinical Investigation ◽  
Neuronal Model ◽  
Tau Phosphorylation ◽  
Neuronal Cultures ◽  
Proteomic Profiling ◽  
Induced Pluripotent

AbstractMutations in MAPT (microtubule-associated protein tau) cause frontotemporal dementia (FTD). MAPT mutations are associated with abnormal tau phosphorylation levels and accumulation of misfolded tau protein that can propagate between neurons ultimately leading to cell death (tauopathy). Recently, a p.A152T tau variant was identified as a risk factor for FTD, Alzheimer's disease, and synucleinopathies. Here we used induced pluripotent stem cells (iPSC) from a patient carrying this p.A152T variant to create a robust, functional cellular assay system for probing pathophysiological tau accumulation and phosphorylation. Using stably transduced iPSC-derived neural progenitor cells engineered to enable inducible expression of the pro-neural transcription factor Neurogenin 2 (Ngn2), we generated disease-relevant, cortical-like glutamatergic neurons in a scalable, high-throughput screening compatible format. Utilizing automated confocal microscopy, and an advanced image-processing pipeline optimized for analysis of morphologically complex human neuronal cultures, we report quantitative, subcellular localization-specific effects of multiple kinase inhibitors on tau, including ones under clinical investigation not previously reported to affect tau phosphorylation. These results demonstrate the potential for using patient iPSC-derived ex vivo models of tauopathy as genetically accurate, disease-relevant systems to probe tau biochemistry and support the discovery of novel therapeutics for tauopathies.

scholarly journals Emergence of chimera states in a neuronal model of delayed oscillators

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Alessandra Lucchetti ◽  
Mogens H. Jensen ◽  
Mathias L. Heltberg
Keyword(s):  
Neuronal Model ◽  
Chimera States

A Neuronal Model of a Global Workspace in Effortful Cognitive Tasks

2006 ◽  
Vol 929 (1) ◽  
pp. 152-165 ◽  
Author(s):  
STANISLAS DEHAENE ◽  
MICHEL KERSZBERG ◽  
JEAN-PIERRE CHANGEUX
Keyword(s):  
Neuronal Model ◽  
Cognitive Tasks ◽  
Global Workspace

Mechanism of dynamic phase transition and synchronous stability in Hindmarsh–Rose neuronal network

2018 ◽  
Vol 32 (28) ◽  
pp. 1850308
Author(s):  
Shi-Dong Liang ◽  
Haoqi Li ◽  
Yuefan Deng
Keyword(s):  
Phase Transition ◽  
Parameter Space ◽  
Lyapunov Stability ◽  
Neuronal Network ◽  
Neuronal Model ◽  
Index Formula ◽  
Neuronal Dynamics ◽  
Dynamical Phase ◽  
Dynamic Phase ◽  
Dynamic Phase Transition

The neuronal dynamics plays an important role in understanding the neurological phenomena. We study the mechanism of the dynamic phase transition and its Lyapunov stability of a single Hindmarsh–Rose (HR) neuronal model. We propose an index [Formula: see text] to express the dynamical phase of the HR neurons. When [Formula: see text] the neuron is in the pure resting state, and when [Formula: see text] the neuron closes to the pure spiking phase, while when [Formula: see text] the neuron runs in the bursting phase. Based on this method, we investigate numerically the phase diagram of the HR neuronal model in the parameter space. We find that two mechanisms governed the HR neuronal dynamic phase transition, the phase transition and crossover transition in the different regions of the parameter space. Moreover, we analyze the equilibrium point stability of the HR neuronal model based on the Lyapunov stability method. We study the synchronous stability of the HR neuronal network based on the master stability function method and give the phase diagrams of the maximum Lyapunov exponents in the parameter space of networks. The regions of the synchronous stabilities in the parameter space depend on the couplings of the HR neurons of the membrane potential and the flux of the fast ion channel between the HR neurons. These results help to understand the HR neuronal dynamics and the synchronous stability of the HR neuronal networks.

Coherence resonance in a modified FHN neuron with autapse and phase noise

2018 ◽  
Vol 32 (30) ◽  
pp. 1850332 ◽  
Author(s):  
Xiaoqiang Liu ◽  
Xiaoli Yang
Keyword(s):  
Numerical Simulation ◽  
Phase Noise ◽  
Electromagnetic Induction ◽  
Neuronal Model ◽  
Parameter Range ◽  
Feedback Gain ◽  
Coherence Resonance ◽  
Induction Current ◽  
Significant Phenomenon

The influences of phase noise together with autapse on the resonance dynamics in a modified FitzHugh–Nagumo (FHN) neuron are investigated by numerical simulation, where the neuronal model is in the environment of electromagnetic induction. First, it is found that phase noise can induce double coherence resonances, which is further confirmed to be robust to the feedback gain of induction current. Surprisingly, by individually changing the period of phase noise and the feedback gain, a resonance-like behavior also appears. Subsequently, the significant phenomenon of autapse-induced multiple coherence resonances is discovered. Moreover, the phenomenon of multiple coherence resonances can emerge at a broad parameter range of autaptic strength and autaptic delay.

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