Cornelis Jan Stam

This paper personalizes tDCS intervention strategies for Alzheimer's disease using individual connectomes and MEG data, showing different optimal stimulation targets compared to a general model.

The paper uses a virtual brain network model to optimize tDCS electrode placements in Alzheimer's disease, finding that right parietal anodal stimulation with contralateral supraorbital cathode improves functional network features.

This paper develops a multivariate signal processing pipeline to build high-order brain networks from fMRI data and identifies high-order hubs associated with distinct brain systems.

This study shows that multimodal multilayer network centrality in the frontoparietal network is positively associated with executive functioning, outperforming unilayer centrality.

The paper simulates individual functional connectivity using individual structural connectivity data and neural mass modeling, finding that individual simulations better match empirical data than group-averaged simulations.

The paper links functional network integration in the default mode network to cellular properties in TLE patients, showing correlations between network centrality and memory performance.

This study identifies topological phase transitions in functional brain networks using TDA, showing they occur when Euler entropy has a singularity and correlate with multidimensional topological holes.

The paper examines how functional brain networks become more segregated during childhood using magnetoencephalography and minimum spanning tree analysis.