Personalized Modeling for Investigating Electric Neuromodulation of Large Scale Brain Network Dynamics
Abstract
Mental, neurological, and psychiatric disorders are a leading cause of death and disability worldwide. Personalized computational modelling for investigating large-scale brain network dynamics is a powerful method for optimizing non-invasive brain stimulation therapies. To this end, we developed an automated, personalized modeling pipeline on o2S2PARC, an open, cloud-based platform for creating, sharing, and publishing computational models and data analysis workflows. The pipeline starts by creating a realistic 3D head model in Sim4Life (ZMT, Switzerland) based on automatic segmentation of structural magnetic resonance imaging (MRI) data using a convolutional neural network. Isotropic dielectric properties are assigned to each tissue using literature-derived values, while the heterogeneous, anisotropic properties of brain tissues are inferred from diffusion tensor imaging when available. The non-invasive electrical stimulation exposure is simulated in Sim4Life using an electroquasistatic solver. After extracting personalized structural connectivity (SC) matrices using Freesurfer, FSL, and MRtrix, individualized whole-brain network model based on the neural-mass approach are created using The Virtual Brain (TVB) software to investigate the neuromodulatory impact of the electromagnetic fields from the stimulator. Finally, electroencephalography (EEG) signals are predicted in a patient-specific manner based on the simulated network activity and the lead-field matrices extracted from the electromagnetic simulations. These signals can be used to validate the pipeline output. Using our pipeline, we investigated the effects of non-invasive stimulation on network dynamics by predicting the associated EEG signals and computing their corresponding power spectra. Our pipeline not only augments existing knowledge about large-scale brain dynamics in healthy and diseased subjects, but also paves the way towards optimizing stimulation parameters in a patient-specific (personalized anatomy, brain properties, networks) manner and ultimately could be used to optimize stimulation conditions (electrode placement, currents, dynamic variation over time, activity synchronization) such that brain activity is driven towards patterns associated with health. Show more
Publication status
unpublishedEvent
Organisational unit
02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.
More
Show all metadata
ETH Bibliography
yes
Altmetrics