1IT’IS Foundation, Zürich, Switzerland 2Swiss Federal Institute of Technology (ETH Zurich), Zürich, Switzerland 3Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva and Sion, Switzerland 4Clinical Neuroscience, University Medical School of Geneva, Geneva, Switzerland
*Email: karimi@itis.swiss
Introduction
Non-invasive brain stimulation (NIBS) offers promising therapeutic avenues for a range of neurological conditions. However, inter-subject response variability remains an important challenge, often limiting its widespread clinical adoption. Here, we present a computational pipeline designed to optimize NIBS by harnessing personalized brain network dynamics modeling, towards enhancing both effictivity and predictability of therapeutic outcomes.
Methods We developed a comprehensive pipeline on the o2S2PARC platform (see Fig. 1). The pipeline utilizes MRI and diffusion-weighted imaging (DWI) data to construct detailed head models (>40 distinct tissue types) through AI segmentation, perform electromagnetic (EM) simulations to determine exposure-induced electric fields and personalized lead field matrices, and predict the impact of diverse stimulation conditions on brain network dynamics using personalized neural mass models (NMMs; derived from DWI structural connectivity data; simulated using the The Virtual Brain (TVB) [1] framework). The brain network modeling combined with the personalized lead fields permit to synthetize virtual EEG signals that can be compared with measurable data. Results Using the developed pipeline, we implemented a temporal interference stimulation planning (TIP) tool for optimizing electrode locations for temporal interference stimulation (TIS, a recently introduced transcranial electric stimulation method capable of targeted stimulation at depth). Demonstration applications of our pipeline predicted shifts in EEG spectral responses following transcranial alternating current stimulation (tACS) in accordance with theoretical and empirical data. Additionally, our simulations revealed dynamic fluctuations of inter-hemispheric synchronization in accordance with experimental observations. These results underscore our pipeline's potential in modeling real-world brain responses to NIBS [3].
Discussion We established a fully automated computational pipeline for personalized NIBS modeling and the optimization of dynamic brain network response predictions. This pipeline underscores the shift from generic exposure-targeting approaches to a personalized, impact-driven (network dynamics) approach, towards improving the efficacy and precision of NIBS therapies. Current research focuses on the continuous inference of improved model parameters based on measurement feedback and model-predictive control. This works lays the groundwork for adaptive and effective brain dynamics modulation for the treatment of complex neurological disorders, marking a significant advance in the personalized medicine landscape [3].
Figure 1. Figure 1: Schematic representation of the developed pipeline on the o2S2PARC platform Acknowledgements -- References 1.https://doi.org/10.1016/j.neuroimage.2015.01.002 2.https://doi.org/10.1109/TNSRE.2012.2200046 3.https://doi.org/10.1088/1741-2552/adb88f
