Progressive reduction in gray matter in patients with schizophrenia assessed with MR imaging by using causal network analysis

Yuchao Jiang, Cheng Luo, Xin Li, Mingjun Duan, Hui He, Xi Chen, Hang Yang, Jinnan Gong, Xin Chang, Marie Woelfer, Bharat B. Biswal, Dezhong Yao

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

Purpose: To investigate the temporal and causal relationships of structural changes in the brain in patients with schizophrenia. Materials and Methods: T1-weighted magnetic resonance (MR) images of 97 patients with schizophrenia (29 women; mean 6 standard deviation age, 41 years ± 11.5; range, 16-66 years; illness duration, 16.3 years ± 10.9; range, 0-50 years) and 126 age- and sex-matched (38 years ± 14.9; range, 18-68 years; 42 women) healthy control subjects were evaluated. The causal network of structural covariance was used to assess the causal relationships of structural changes in patients with schizophrenia. This was accomplished by applying Granger causality analysis to the morphometric T1-weighted images ranked according to duration of disease. Results: With greater disease duration, reduction in gray matter volume began in the thalamus and progressed to the frontal lobe, and then to the temporal and occipital cortices as well and the cerebellum (P <.00001, false discovery rate corrected). The thalamus was shown to be the primary hub of the directional network and exhibited positive causal effects on the frontal, temporal, and occipital regions as well as on the cerebellum (P <.05, false discovery rate corrected). The frontal regions, which were identified to be transitional points, projected causal effects to the occipital lobe, temporal regions, and the cerebellum and received causal effects from the thalamus (P <.05, false discovery rate corrected). Conclusion: Schizophrenia shows progression of gray matter abnormalities over time, with the thalamus as the primary hub and the frontal regions as prominent nodes.

Original languageEnglish (US)
Pages (from-to)633-642
Number of pages10
JournalRadiology
Volume287
Issue number2
DOIs
StatePublished - May 2018

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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