Brain simulation augments machine-learning-based classification of dementia

Abstract

INTRODUCTIONComputational 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 Alzheimers disease. METHODSWe enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local Amyloid {beta} PET with altered excitability. We use PET and MRI data from 33 participants of Alzheimers Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for machine-learning classification. RESULTSThe 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 Alzheimers-typical spatial distribution. DISCUSSIONThe cause-and-effect implementation of local hyperexcitation caused by Amyloid {beta} can improve the machine-learning-driven classification of Alzheimers and demonstrates TVBs ability to decode information in empirical data employing connectivity-based brain simulation. RESEARCH IN CONTEXTO_LISYSTEMATIC REVIEW. Machine-learning has been proven to augment diagnostics of dementia in several ways. Imaging-based approaches enable early diagnostic predictions. However, individual projections of long-term outcome as well as differential diagnosis remain difficult, as the mechanisms behind the used classifying features often remain unclear. Mechanistic whole-brain models in synergy with powerful machine learning aim to close this gap. C_LIO_LIINTERPRETATION. Our work demonstrates that multi-scale brain simulations considering Amyloid {beta} distributions and cause-and-effect regulatory cascades reveal hidden electrophysiological processes that are not readily accessible through measurements in humans. We demonstrate that these simulation-inferred features hold the potential to improve diagnostic classification of Alzheimers disease. C_LIO_LIFUTURE DIRECTIONS. The simulation-based classification model needs to be tested for clinical usability in a larger cohort with an independent test set, either with another imaging database or a prospective study to assess its capability for long-term disease trajectories. C_LI