AbstractBackgroundComputational multi‐scale brain modeling has been found to be a useful tool for identifying potential disease mechanisms in Alzheimer's disease (AD) [1]. Importantly, structural and functional disruption patterns of AD continuum arise from a chain of degeneration effects which take place even from the preclinical phase of the disease, which characterization has the potential to enable early intervention to counteract and delay cognitive decline.MethodMagnetoencephalography (MEG), T1‐ and dw‐ magnetic resonance imaging (MRI) have been gathered from 20 healthy controls (HC) and 20 mild cognitive impaired (MCI), to characterize their resting‐state functional connectivity (rsFC), and structural connectivity (SC) profiles. Regarding MEG signal, source reconstruction was performed with linearly constrained minimum variance beamformer and the obtained neural MEG sources were anatomically parcellated according to the Desikan‐Killiany atlas. Resting state activity of the participants has been characterized considering two FC indices (i.e., PLV and AEC) among relevant areas of the Default Mode Network (DMN). Personalized simulations of the 40 subjects have been carried out using The Virtual Brain software and the matching between real and simulated rsFC is finally evaluated. For the MCIs two different degradation patterns are introduced in the model, one based on the gray matter volumes, and one that takes in account white matter integrity.ResultThe inclusion of both types of degeneration patterns in key areas of the DMN results in an increase of the matching between real and simulated rsFC for MCI patients; the inclusion of both simultaneously brought the best results.ConclusionThis study shows how the inclusion of quantifiable signatures of structural degeneration regarding gray matter volumes and white matter integrity in multi‐scale brain models reflects to a better matching between real and simulated MEG‐rsFC, when modeling MCI patients. This complements the already existing studies in the field and provides new insights towards a precise characterization of patient's MEG resting state networks during the early phases of AD. 1 ‐ L. Stefanovski et al., “Linking Molecular Pathways and Large‐Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer’s Disease,” Front Comput Neurosci, vol. 13, p. 54, 2019.