Brain simulation augments machine-learning-based classification of dementia

Paul Triebkorn , Leon Stefanovski, Kiret Dhindsa, Margarita-Arimatea Diaz-Cortes, Patrik Bey, Konstantin Bülau, Roopai Pai, Anreas Spiegler, Ana Solodkin, Viktor Jirsa, Anthony R. McIntosh, Petra Ritter

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)


Alzheimer’s Diseasemachine learningmulti-scale brain simulationpositron emission tomographyThe Virtual Brain
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