Building multi-scale models for optimizing brain stimulation in Alzheimer's disease

Overview & Synopsis

Non-invasive brain stimulation offers a promising alternative for treating neurological disorders like Alzheimer’s disease or depressive disorder. Yet, optimization to individual clinical cases remains hindered by inefficient trial-and-error methods. The prior knowledge about individual brain areas role in affecting the whole brain dynamics remains less understood given the complex inter-area connectivity. To address this, we leverage Arbor, an HPC-optimized library, to transition from empirical testing to high-fidelity digital twins by co-simulating it with other simulators. Our framework achieves exascale-ready neural modeling by integrating single-neuron biophysics with whole-brain dynamics. Key technical milestones include the implementation of scalable spike transmission, the integration of structural plasticity within the simulation kernel, and the development of a multi-scale co-simulation bridge between Arbor and The Virtual Brain (TVB). By combining morphologically detailed biophysical neurons with large-scale connectivity, this project aims to establish a scalable computational platform for investigating the long-term effects of stimulation parameters on complex neural architectures