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Biomed Eng Online. 2012 Aug 16;11:50. doi: 10.1186/1475-925X-11-50.

Identify schizophrenia using resting-state functional connectivity: an exploratory research and analysis.

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Biomedical Engineering Laboratory, School of Geosciences and Info-Physics, Central South University, Changsha, Hunan, 410083, Peoples Republic of China.



Schizophrenia is a severe mental illness associated with the symptoms such as hallucination and delusion. The objective of this study was to investigate the abnormal resting-state functional connectivity patterns of schizophrenic patients which could identify furthest patients from healthy controls.


The whole-brain resting-state fMRI was performed on patients diagnosed with schizophrenia (n = 22) and on age- and gender-matched, healthy control subjects (n = 22). To differentiate schizophrenic individuals from healthy controls, the multivariate classification analysis was employed. The weighted brain regions were got by reconstruction arithmetic to extract highly discriminative functional connectivity information.


The results showed that 93.2% (p < 0.001) of the subjects were correctly classified via the leave-one-out cross-validation method. And most of the altered functional connections identified located within the visual cortical-, default-mode-, and sensorimotor network. Furthermore, in reconstruction arithmetic, the fusiform gyrus exhibited the greatest amount of weight.


This study demonstrates that schizophrenic patients may be successfully differentiated from healthy subjects by using whole-brain resting-state fMRI, and the fusiform gyrus may play an important functional role in the physiological symptoms manifested by schizophrenic patients. The brain region of great weight may be the problematic region of information exchange in schizophrenia. Thus, our result may provide insights into the identification of potentially effective biomarkers for the clinical diagnosis of schizophrenia.

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