According to a paper published in the Neural Regeneration Research (Vol. 9, No. 2, 2014), both depressive patients and healthy controls presented typical small-world attributes, and 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. An artificial neural network algorithm was applied for classifcation 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.
Article: " Resting-state functional connectivity abnormalities in first-onset unmedicated depression," by Hao Guo1, Chen Cheng1, Xiaohua Cao2, Jie Xiang1, Junjie Chen1, Kerang Zhang2(1 College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, Shanxi Province, China; 2 Department of Psychiatry, First Affiliated Hospital, Shanxi Medical University, Taiyuan 030002, Shanxi Province, China)
Guo H, Cheng C, Cao XH, Xiang J, Chen JJ, Zhang KR. Resting-state functional connectivity abnormalities in first-onset unmedicated depression. Neural Regen Res. 2014;9(2):153-163.