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Year : 2014  |  Volume : 9  |  Issue : 2  |  Page : 153-163

Resting-state functional connectivity abnormalities in first-onset unmedicated depression

1 College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, Shanxi Province, China
2 Department of Psychiatry, First Affiliated Hospital, Shanxi Medical University, Taiyuan, Shanxi Province, China

Correspondence Address:
Junjie Chen
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, Shanxi Province
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Source of Support: This study was supported by the National Natural Science Foundation of China, No. 61070077, 61170136, 61373101, 81171290; the Natural Science Foundation of Shanxi Province in China, No. 2010011020-2, 2011011015-4; Programs for Science and Technology Social Development of Shanxi Province, No. 20130313012-2; Science and Technology Projects by Shanxi Provincial Education Ministry, No. 20121003; Youth Fund by Taiyuan University of Technology, No. 2012L014; Youth Team Fund by Taiyuan University of Technology, No. 2013T047., Conflict of Interest: None

DOI: 10.4103/1673-5374.125344

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Depression is closely linked to the morphology and functional abnormalities of multiple brain regions; however, its topological structure throughout the whole brain remains unclear. We collected resting-state functional MRI data from 36 first-onset unmedicated depression patients and 27 healthy controls. The resting-state functional connectivity was constructed using the Automated Anatomical Labeling template with a partial correlation method. The metrics calculation and statistical analysis were performed using complex network theory. The results showed that both depressive patients and healthy controls presented typical small-world attributes. Compared with healthy controls, characteristic path length was significantly shorter in depressive patients, suggesting development toward randomization. Patients with depression showed apparently abnormal node attributes at key areas in cortical-striatal-pallidal-thalamic circuits. In addition, right hippocampus and right thalamus were closely linked with the severity of depression. We selected 270 local attributes as the classification features and their P values were regarded as criteria for statistically significant differences. An artificial neural network algorithm was applied for classification research. The results showed that brain network metrics could be used as an effective feature in machine learning research, which brings about a reasonable application prospect for brain network metrics. The present study also highlighted a significant positive correlation between the importance of the attributes and the intergroup differences; that is, the more significant the differences in node attributes, the stronger their contribution to the classification. Experimental findings indicate that statistical significance is an effective quantitative indicator of the selection of brain network metrics and can assist the clinical diagnosis of depression.

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