PT  - JOURNAL ARTICLE
AU  - Paul Triebkorn
AU  - Leon Stefanovski
AU  - Kiret Dhindsa
AU  - Margarita-Arimatea Diaz-Cortes
AU  - Patrik Bey
AU  - Konstantin Bülau
AU  - Roopa Pai
AU  - Andreas Spiegler
AU  - Ana Solodkin
AU  - Viktor Jirsa
AU  - Anthony Randal McIntosh
AU  - Petra Ritter
AU  - for the Alzheimer’s Disease Neuroimaging Initiative
TI  - Multi-scale brain simulation with integrated positron emission tomography yields hidden local field potential activity that augments machine learning classification of Alzheimer’s disease
AID  - 10.1101/2021.02.27.433161
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.02.27.433161
4099  - http://biorxiv.org/content/early/2021/03/01/2021.02.27.433161.short
4100  - http://biorxiv.org/content/early/2021/03/01/2021.02.27.433161.full
AB  - Introduction While the prevalence of neurodegenerative diseases and dementia increases, our knowledge of the underlying pathomechanisms and related diagnostic biomarkers, outcome predictors, or therapeutic targets remains limited. In this article, we show how computational multi-scale brain network modeling using The Virtual Brain (TVB) simulation platform supports revealing potential disease mechanisms and can lead to improved diagnostics.Methods TVB allows standardized large-scale structural connectivity (SC)-based modeling and simulation of whole-brain dynamics. We combine TVB with a cause-and-effect model for amyloid-beta, and machine-learning classification with support vector machines and random forests. The amyloid-beta burden as quantified from individual AV-45 PET scans informs parameters of local excitation/inhibition balance. We use magnetic resonance imaging (MRI), positron emission tomography (PET, specifically Amyloid-beta (Abeta) binding tracer AV-45-PET, and Tau-protein (Tau) binding AV-1451-PET) from 33 participants of Alzheimer’s Disease Neuroimaging Initiative study 3 (ADNI3). The frequency compositions of simulated local field potentials (LFP) are under investigation for their potential to classify individuals between Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI), and healthy controls (HC) using support vector machines and random forest classifiers.Results The combination of empirical features (subcortical volumetry, AV-45- and AV-1451-PET standard uptake value ratio, SUVR per region) and simulated features (mean LFP frequency per brain region) significantly outperformed the classification accuracy of empirical data alone by about 10% in the accuracy index of weighted F1-score (empirical 64.34% vs. combined 74.28%). There was no significant difference between empirical and simulated features alone. The features with the highest feature importance showed high biological plausibility with respect to the AD-typical spatial distribution of the features. This was demonstrated for all feature types, e.g., increased importance indices for the left entorhinal cortex as the most important Tau-feature, the left dorsal temporopolar cortex for Abeta, the right thalamus for LFP frequency, and the right putamen for volume.Discussion In summary, here we suggest a strategy and provide proof of concept for TVB-inferred mechanistic biomarkers that are direct indicators of pathogenic processes in neurodegenerative disease. We show how the cause-and-effect implementation of local hyperexcitation caused by Abeta can improve the machine-learning-driven classification of AD. This proves TVBs ability to decode information in empirical data by means of SC-based brain simulation.Competing Interest StatementThe authors have declared no competing interest.