Subject-specific features of excitation/inhibition profiles in neurodegenerative diseases

Brain pathologies are based on microscopic changes in neurons and synapses that reverberate into large scale networks altering brain dynamics and functional states. An important yet unresolved issue concerns the impact of patients excitation/inhibition (E/I) profiles on neurodegenerative diseases including Alzheimer’s disease (AD), Frontotemporal Dementia (FTD) and Amyotrophic Lateral Sclerosis (ALS). In this work we used a simulation platform, The Virtual Brain (TVB), to simulate brain dynamics in healthy controls (HC) and in AD, FTD and ALS patients. The brain connectome and functional connectivity were extracted from 3T-MRI scans and the TVB nodes were represented by a Wong-Wang neural mass model endowing an explicit representation of the E/I balance. The integration of cerebro-cerebellar loops improved the correlation between experimental and simulated functional connectivity, and hence TVB predictive power, in all pathological conditions. The TVB biophysical parameters differed between clinical phenotypes, predicting higher global coupling and inhibition in AD and stronger NMDA receptor-dependent excitation in ALS. These physio-pathological parameters allowed an advanced analysis of patients’ state.

This database includes structural and functional connectivity matrices estimated from tractography and rs-fMRI time-series of each subject analyzed (15 HC, 15 AD, 15 FTD, 15 ALS). 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 and the experimental FC matrix (expFC) is computed as the Pearson’s correlation coefficient (PCC) of the time-courses between each pair of brain regions.