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. 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.

Embedded geometrical and dynamical representation of neurons (soma, axon, and dendritic tree) generate physiologically functionalized anatomical models. (coming soon)

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 and
ex vivo and in vivo measurements, and is continually advanced and validated.


Application Areas

MRgFUS Neurosurgery Applications: Tumor Ablation, Neuropathic Pain Treatment, Movement Disorders
FUS-Based Neural Stimulation



Neuro-Prosthetics (retina, cochlea, vestibular, motor)
EM Neuro-Stimulation
Neuro-Motoric Incapacitation



High LF-EM Field Safety Assessment (e.g., MR Gradient Coils)
Pacemaker
Temperature Impact on Neuronal Dynamics


Key Features

Dynamic modeling of EM-induced neuronal activation inhibition & synchronization
Unidirectional coupling with the EM-QS and Thermal solver
SENN model can be applied inside whole body models



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



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 field