Functional graph alterations in schizophrenia: a result from a global anatomic decoupling?

Journal article

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.

Authors: Cabral, J; Kringelbach, M; Deco, G

• Publication status: Published
• Journal: Pharmacopsychiatry
• Volume: 45 Suppl 1
• Issue: S 01
• Pages: S57-S64
• Publication date: 2012-05-01
• DOI: 10.1055/s-0032-1309001
• EISSN: 1439-0795
• ISSN: 0176-3679
• Language: English
• Keywords: Algorithms; Image Processing, Computer-Assisted; Data Interpretation, Statistical; Schizophrenic Psychology; Reproducibility of Results; Schizophrenia; Humans; Young Adult; Nerve Net; Cognition; Models, Neurological; Male; Female; Adult; Computer Simulation; Brain; Middle Aged; Cluster Analysis; Diffusion Tensor Imaging