Brain simulation augments machine-learning-based classification of dementia

Introduction Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning (ML) and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer's disease (AD). Methods We enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local amyloid beta (A beta) positron emission tomography (PET) with altered excitability. We use PET and magnetic resonance imaging (MRI) data from 33 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for ML 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 AD-typical spatial distribution. Discussion The cause-and-effect implementation of local hyperexcitation caused by A beta can improve the ML-driven classification of AD and demonstrates TVB's ability to decode information in empirical data using connectivity-based brain simulation.

Automated summary

Computational brain network modeling using The Virtual Brain (TVB) simulation platform, combined with machine learning and multi-modal neuroimaging, can reveal mechanisms and improve diagnostics in Alzheimer's disease (AD). This study enhances whole-brain simulation by linking local amyloid beta (Aβ) positron emission tomography (PET) with altered excitability, and demonstrates improved classification accuracy when combining empirical neuroimaging features and simulated local field potentials (LFPs).