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.

This database includes structural and functional connectivity matrices estimated from tractography and rs-fMRI time-series of each subject analyzed (18 HC, 22 MCI, 20 AD). An ad-hoc grey matter (GM) parcellation atlas has been created combining 93 cerebral (including cortical/subcortical structures) and 31 cerebellar labels. Each GM parcellation is reported as a node in the connectivity matrices. Two types of SC matrices are reported: a distance matrix containing the length of tracts connecting each pair of nodes and a weight matrix in which connections strengths (number of streamlines) are normalized by the maximum value per each subject.  The time-course of BOLD signals has been extracted for each node. To perform brain dynamics simulations or graph theory analysis in multiple functional networks a subset of nodes and connections need to be extracted from whole-brain SC and FC matrices and used as an input for TVB or GT.

All codes used for brain dynamics simulations with TheVirtualBrain are available as a Python code that can be found at  https://wiki.ebrains.eu/bin/view/Collabs/tvb-ww-tutorial/