4.7 Article

Anatomical insights into disrupted small-world networks in schizophrenia

Journal

NEUROIMAGE
Volume 59, Issue 2, Pages 1085-1093

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2011.09.035

Keywords

Anatomical connectivity; Diffusion tensor imaging; Graph theory; Small-world; Schizophrenia

Funding

  1. National Key Basic Research and Development Program (973) [2011CB707800]
  2. Natural Science Foundation of China [30730035, 30900487]
  3. Ministry of Economic Affairs [98-EC-17-A-19-S2-0103]
  4. National Health Research Institute [NHRI-EX98-9813EC]
  5. National Science Council [98-2517-S-004-001-MY3, 98-2627-B-010-008]
  6. Ministry of Education
  7. MRI Core Laboratory, NYMU

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Schizophrenia is characterized by lowered efficiency in distributed information processing, as indicated by research that identified a disrupted small-world functional network. However, whether the dysconnection manifested by the disrupted small-world functional network is reflected in underlying anatomical disruption in schizophrenia remains unresolved. This study examined the topological properties of human brain anatomical networks derived from diffusion tensor imaging in patients with schizophrenia and in healthy controls. We constructed the weighted brain anatomical network for each of 79 schizophrenia patients and for 96 age and gender matched healthy subjects using diffusion tensor tractography and calculated the topological properties of the networks using a graph theoretical method. The topological properties of the patients' anatomical networks were altered, in that global efficiency decreased but local efficiency remained unchanged. The deleterious effects of schizophrenia on network performance appear to be localized as reduced regional efficiency in hubs such as the frontal associative cortices, the paralimbic/limbic regions and a subcortical structure (the left putamen). Additionally, scores on the Positive and Negative Symptom Scale correlated negatively with efficient network properties in schizophrenia. These findings suggest that complex brain network analysis may potentially be used to detect an imaging biomarker for schizophrenia. (C) 2011 Elsevier Inc. All rights reserved.

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