Neuronal Tissue Models (T-NEURO)

The Neuronal Tissue Models (T-NEURO) enable the dynamic modeling of EM-induced neuronal activation, inhibition, and synchronization using either complex, multi-compartmental representations of axons, neurons, and neuronal networks with varying channel dynamics, or generic models.

Contents	Sim4Life uses the NEURON solver developed at the Yale University which is ideal for studying interaction mechanisms, evaluating and optimizing neurostimulating devices, and assessing safety issues. T-NEURO now also permits to accurately and efficiently deal with neural sensing – the simulation of measurable electric signals resulting from neural activity. Embedded geometrical and dynamical representation of neurons (soma, axon, and dendritic tree) generate physiologically functionalized anatomical models. The SENN model (safety standards) and more complex models can be applied inside whole-body models. The graphics user interface (GUI) facilitates the integration of other neuronal models from commonly used databases or independently derived models. T-NEURO has been validated against published data andex vivo and in vivo measurements, and is continually advanced and validated.
Applications	MRgFUS Neurosurgery Applications: Tumor Ablation, Neuropathic Pain Treatment, Movement Disorders FUS-Based Neural Stimulation EM Neuro-Stimulation Neural sensing: Compound action potential (CAP), electroencephalogram (EEG), and electrocorticogram (ECoG) recordings Neuro-Prosthetics (retina, cochlea, vestibular, motor) High LF-EM Field Safety Assessment (e.g., MR Gradient Coils) Pacemaker Temperature Impact on Neuronal Dynamics Neuro-Motoric Incapacitation
Characteristics	Dynamic modeling of EM-induced neuronal activation inhibition & synchronization Unidirectional coupling with the EM-QS and Thermal solver Accurately and efficiently dealing with neural sensing sensing Powerful Hessian Calculator for the investigation of electrical neuro-stimulation within the complexity of the human anatomy Interface allows integration of other neuronal models from commonly used databases User-friendly import & visualization of nerve geometries from commonly used databases Determining thresholds through titration procedure Detection of neuronal spikes and their occurence times SENN model can be applied inside whole body models Novel spatially varying temperature dependence impact on the neuronal dynamics Capturing & plotting membrane dynamics over time Easily define pulse sources that correspond to gradient switching fields Applicable to heterogeneous, anisotropic dielectric environment model, including the most complex anatomical representations

Neuronal Tissue Models (T-NEURO)

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