Introduction Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer’s disease.

Methods We enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local Amyloid β PET with altered excitability. We use PET and MRI data from 33 participants of Alzheimer’s Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for machine-learning classification.

Results The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the Alzheimer’s-typical spatial distribution.

Discussion The cause-and-effect implementation of local hyperexcitation caused by Amyloid β can improve the machine-learning-driven classification of Alzheimer’s and demonstrates TVB’s ability to decode information in empirical data employing connectivity-based brain simulation.

Alzheimer’s Dement. 2022;8:e12303 (2022)