Resting-state functional connectivity abnormalities in ifrst-onset unmedicated depression
Hao Guo
Chen Cheng
Xiaohua Cao
Jie Xiang
Junjie Chen
Kerang Zhang
摘要：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 col-lected resting-state functional MRI data from 36 ifrst-onset unmedicated depression patients and 27 healthy controls. The resting-state functional connectivity was constructed using the Auto-mated 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 signiifcantly 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 se-lected 270 local attributes as the classiifcation features and their P values were regarded as criteria for statistically significant differences. An artificial neural network algorithm was applied for classiifcation research. The results showed that brain network metrics could be used as an effec-tive feature in machine learning research, which brings about a reasonable application prospect for brain network metrics. The present study also highlighted a signiifcant positive correlation between the importance of the attributes and the intergroup differences;that is, the more sig-niifcant the differences in node attributes, the stronger their contribution to the classiifcation. Experimental ifndings indicate that statistical signiifcance is an effective quantitative indicator of the selection of brain network metrics and can assist the clinical diagnosis of depression.
机标关键词：neural network algorithmtopological structurestatistical analysisapplication prospectpartial correlationmachine learningthe differencescomplex network
资助基金：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 Ed-ucation Ministry, (No.20121003)Youth Fund by Taiyuan University of Technology, (No.2012L014)Youth Team Fund by Taiyuan University of Technology, (No.2013T047)
论文发表日期：2014-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：11( 153-163 )
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