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

BACKGROUND Mild cognitive impairment (MCI) is a clinical condition in the continuum between normal cognition and dementia. Despite numerous studies, the heterogeneity of the underlying pathophysiology prevents a precise prediction of clinical evolution. METHODS 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 (MRI) data. With this approach, the analysis was extended to a multiparametric space and brought from the group to the individual subject level. RESULTS 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). There was a high correlation (R ²  ~ 70%) between GT and TVB parameters and neuropsychological performance in multiple cognitive domains. CONCLUSIONS 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 with neuropsychological scores. This, in turn, could form the basis for a more precise MCI stratification leading, in the future, to a personalized prediction of evolution and therapeutic intervention.