Network model accounts for evolution of EEG functional connectivity in patients with cognitive decline

ggressive forms of dementia like Alzheimer's disease (AD) are known to cause deep structural alterations in the brain of affected subjects. The state of these alterations is nowadays deduced from invasive and/or expensive imaging techniques such as Magnetic Resonance Imaging and Positron Emission Tomography. Herein, we present a novel biophysicallyinspired multi-scale cortical network model of disease progression enabling the reconstruction of AD-related structural alterations from resting state electroencephalography (EEG). The model, based on the The Virtual Brain platform, reproduced EEG features (power spectral alterations and changes in functional connectivity) of subjects in prodromic states of AD, i.e., subjective cognitive decline (SCD) and mild cognitive impairment (MCI). In particular, the model accounts for the counter-intuitive finding that subjects with SCD display a stronger functional connectivity than both MCI subjects and healthy controls. This model paves the way for an EEG-based reconstruction of patient-specific structural alterations.