Alterations in topological and dynamical parameters correlate with disease biomarkers and neuropsychological scores in prodromic stages of dementia

: Mild cognitive impairment (MCI) is a clinical condition at the very beginning of dementia continuum whose heterogeneity prevents a precise prediction of clinical evolution. In this work, in a cohort composed of MCI, healthy controls (HC), and Alzheimer's disease (AD) patients, graph theory (GT) was combined with virtual brain modelling (TVB) to extract the information on network topology and dynamics embedded in magnetic resonance imaging data. With this approach, the analysis was extended to a multiparametric space and brought from the group to the subject-specific level. The comparison of network properties in HC, MCI, and AD revealed a profound reshaping of brain connectivity, which mainly affected the default mode, limbic, attention, and somatosensory networks. Interestingly, positivity to AD biomarkers (Aβ and τ) in MCI correlated with network topology, while a TVB parameter (i.e., recurrent excitation) correlated with reduced global cognition (MMSE score). The combination of GT and TVB parameters was superior to the individual techniques alone in providing a subject-specific phenotype of MCI sensitive to molecular biomarkers and correlated (R2 ~ 70%) with neuropsychological scores. This, in turn, could form the basis for a more precise stratification in prodromic dementia leading, in future, to a personalized prediction of evolution and therapeutic intervention.