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May 3D Implantation of the Nerve-Neural Interface Model in its Anatomical Surroundings #1118

Closed elisabettai closed 4 months ago

elisabettai commented 1 year ago

Description: 3D implantation of the nerve-neural interface model in its anatomical surroundings: An EM-electrophysiological simulation-variant featuring 3D implantation of the nerve-neural interface model in its anatomical surroundings will be performed to assess the impact of the environment (e.g., current short-circuiting through a nearby large vessel).

Deliverable: 3D implantation of nerve-neural interface model in its anatomical surroundings available; will be reported on in the reporting milestone due in Q4

Acceptance criteria: Sim4Life simulation on o²S²PARC

Out of scope: -

Deadline: Q3

wrike Y7-MS 11.2.1.6: 3D Implantation of the Nerve-Neural Interface Model in its Anatomical Surroundings

elisabettai commented 4 months ago

Y7Q3 Status update (06/10/2024): Completed

Implementation: The Yoon-Sun anatomical model belonging to the IT’IS Virtual Population has been neuro-functionalized with an image-based multi-fascicular model of the vagus nerve (VN), based on an already segmented VN histology image provided by the Grill team and available in the SPARC Portal (https://sparc.science/datasets/234). A model of a bipolar cuff electrode with parameterized geometry has been applied to the nerve. The nerve is created by extruding the segmented VN cross section image along the trajectory of the left VN within the Yoon-Sun model, using Python scripts automating the complete pipeline (discretization of the labeled image, alignment of the discretized nerve model with the VN trajectory, extrusion, insertion of the simplified cuff electrode). Python scripts were created to generate an unstructured mesh on the neuro-functionalized anatomical body including the cuff electrode using the Octree meshing engine. Electromagnetic simulations were set up and executed to calculate the electric (E-)field and current density field distributions within the VN and the surrounding anatomy. All the tissues were assigned with isotropic conductivity apart from the fascicles, where anisotropic conductivity is assigned with larger conductivity along the direction of the fascicles. Python scripts were created to automate the creation of axon trajectories within the fascicles in view of the electrophysiological simulations, to create or load user-defined distributions of fiber diameters and to assign user defined electrophysiological models of either myelinated and unmyelinated fibers. Electromagnetic simulations were executed and exposure-related quantities (E-field and current density distributions) were extracted. Electrophysiology simulations were executed to predict threshold for nerve recruitment.