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During rest, the brain exhibits slow hemodynamic fluctuations (<0.1 Hz) that are correlated across spatially segregated brain regions, defining functional networks. Resting-state functional networks of people with schizophrenia were found to have graph properties that differ from those of control subjects. Namely, functional graphs from patients exhibit reduced small-worldness, increased hierarchy, lower clustering, improved efficiency and greater robustness. Notably, most of these parameters correlate with patients' cognitive performance.To test if a brain-wide coupling deficit could be at the origin of such network reorganization, we use a model of resting-state activity where the coupling strength can be manipulated. For a range of coupling values, the simulated functional graphs obtained were characterized using graph theory.For a coupling range, simulated graphs shared properties of healthy resting-state functional graphs. On decreasing the coupling strength, the resultant functional graphs exhibited a topological reorganization, in the same way as described in schizophrenia.This work shows how complex functional graph alterations reported in schizophrenia can be accounted for by a decrease in the structural coupling strength. These results are corroborated by reports of lower white matter density in schizophrenia.

Original publication




Journal article



Publication Date



45 Suppl 1


S57 - S64


Adult, Algorithms, Brain, Cluster Analysis, Cognition, Computer Simulation, Data Interpretation, Statistical, Diffusion Tensor Imaging, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Models, Neurological, Nerve Net, Reproducibility of Results, Schizophrenia, Schizophrenic Psychology, Young Adult