Stimulation of the central nervous system (CNS) via electromagnetic (EM) fields and acoustic pressure waves (mostly low intensity focused ultrasound - LIFUS) is used for treatment of a variety of medical disorders (e.g., stroke, movement disorders, pain, depression). Targeted stimulation is required to ensure efficacy and to avoid stimulation of non-targeted neural tissue. Computational tools and models are becoming increasingly important in this regard to inform treatment planning, the development and optimization of stimulation devices, treatment efficacy and safety assessment, and to provide an improved understanding for the underlying physical and physiological mechanisms. They offer a high degree of control, facilitate the exploration of large parameter spaces, and provide dense information, while avoiding ethical issues. Importantly, in particular for use in clinical treatments, computational modelling must be accurate and reliable, with known parameter and prediction uncertainties.
The aims of this thesis are: (i) to develop and validate a comprehensive biophysical modelling framework, based on the Sim4Life computational life sciences platform, for the reliable simulation and optimization of electric and ultrasonic neurostimulation, (ii) to apply this framework to advance innovative therapeutic approaches (in the domains of bioelectronic medicine and neuroprosthetics), and (iii) to support precision medicine through personalized, image-based modelling.
Based on a review of the current state-of-the-art in the domain of EM and FUS neurostimulation modelling, requirements and knowledge gaps were identified. To close these gaps, important extensions of Sim4Life were realized, including the implementation of a multi-GPU-accelerated solver for acoustic propagation and of support for medical image-derived tissue heterogeneous anisotropy in the EM and LIFUS simulations. This allowed to establish unprecedentedly realistic multi-scale modelling of physical exposure and induced electrophysiological responses.
Verification of the modelling framework with analytical and experimental benchmarks was complemented by experimental ex and in vivo validation in three application areas: (i) retinal prosthetics (joint work with the FDA), (ii) spinal-cord stimulation (SCS) for the treatment of paraplegics (as part of the RESTORE project), and (iii) transcranial ultrasound sonication (tcFUS; with the Hvidovre Hospital, Denmark).
(i) Retinal Prosthetics: In addition to providing the necessary confidence in the modelling framework, the retinal stimulation study also provided a) unique morphologically-detailed electrophysiological ganglion cell models, b) insights into three distinct ganglion cell stimulation mechanisms, and c) requirements for regulatory-grade neurostimulation modelling (including an uncertainty assessment).
(ii) SCS: As part of the RESTORE project, personalized EM-neural-modelling-based optimization was used to redesign the SCS implant and to allow for patient-tailored treatments (device placement and stimulation parameters), yielding vastly superior stimulation selectivity than achieved by clinical experts. Successful restoration of locomotion to paraplegics could be achieved.
(iii) tcFUS: Computational modelling is recognized as a promising solution to address the difficulty of focusing tcFUS across the highly heterogeneous structure of the skull, but reliable predictions have proven elusive. Careful computational and experimental studies on requirements and pitfalls of acoustic transducer and image-based skull modelling were therefore performed. The importance of properly characterizing and modelling the internal structure and physics of the transducer was demonstrated. Standing-wave effects in the skull were found to impact transcranial sonication efficiency to a larger extent than previously believed. The need to use computed tomography (CT)-based modelling to account for inter-subject variability was demonstrated. It was shown that not only the heterogeneity of the skull, but also its structure has an important impact, and that CT-based mapping requires imaging-parameter-dependent calibration, which prevents simple translation between clinical sites. All these investigations were experimentally validated (using explanted skulls and 3D-printed obstacles) and complemented by extensive sensitivity and uncertainty analyses.
In addition to these validation studies, simulation studies on deep brain stimulation (DBS) and SCS modelling were performed. The results not only provided mechanistic understanding, but also gave rise to a novel, activating-function-based multi-contact stimulation optimization approach that combines the benefits of physiological impact-driven optimization with those of rapid physics-based optimization. Using this approach, superior stimulation selectivity and targeting could be achieved.
The confluence of the developed multi-physics and physiological modelling across a wide range of spatial scales was further illustrated in an application example: transcranial LIFUS neuromodulation. A neural-mass model of cortical activity was extended with a model of LIFUS-induced, membrane-cavitation-mediated modulation of excitatory pyramidal neurons and inhibitory interneurons, resulting in testable predictions of induced changes in the electroencephalogram (EEG).
Finally, the limitations of this work are discussed and new research directions are suggested
