Whole brain diffusion tensor imaging in diagnosing social anxiety disorder based on support vector machine
10.3760/cma.j.issn.1005-1201.2014.08.005
- VernacularTitle:基于支持向量机的全脑扩散张量成像诊断社交焦虑障碍
- Author:
Shiguang LI
;
Yuqing WANG
;
Xiaoqi HUANG
;
Su LYU
;
Wei ZHANG
;
Changjian QIU
;
Qiyong GONG
- Publication Type:Journal Article
- Keywords:
Anxiety;
Magnetic resonance imaging;
Diagnosis
- From:
Chinese Journal of Radiology
2014;48(8):636-640
- CountryChina
- Language:Chinese
-
Abstract:
Objective To evaluate the clinical value of whole-brain diffusion tensor imaging(DTI) in diagnosing patients with social anxiety disorder(SAD) using an automated method based on support vector machine(SVM) classification.Methods Whole brain DTI data were collected from 19 patients with SAD and 19 age-,gender-and education-matched healthy control(HC) subjects.Fractional anisotropy(FA) of whole brain was obtained by input all tensor images into Diffusion Toolkit software.Based upon the characteristics of brain FA,the pattern recognition of brain image data(PROBID) toolbox on the grounds of SVM algorithm was employed to classify the subjects,evaluate the diagnostic value of whole-brain FA data based SVM in diagnosing SAD patients and verify the robustness of the diagnostic results using permutation test with the threshold at P≤0.001.The weight vector score of each voxel was calculated according to the ratio between this voxel and whole brain in FA differences of the two groups.The white matter regions identified by setting the threshold to the top 30％ of the weight vector scores with at least 10 contiguous voxels were demonstrated by MRIcro software.Results Diagnostic accuracy of whole-brain FA based SVM in diagnosing SAD was 92.11％ (35/38) in which the specificity was 94.44％ (17/18),the sensitivity was 90.00％(18/20),the positive likelihood ratio was 17.01,the negative likelihood ratio was 0.11 and the diagnostic index was 184.22％.Permutation test suggested that the diagnostic results were significantly reliable.White matter regions showing major contributions favoring SAD over HC were located in the genu and splenium of the corpus callosum,the left uncinate fasciculus,the left inferior longitudinal fasciculus,the left inferior fronto-occipital fasciculus,bilateral frontal gyri and the left occipital lobe.Whereas,white matter in bilateral anterior cingula,the left middle cerebellar peduncle and the left inferior parietal lobule showed more contributions to diagnose HC than to diagnose SAD.Conclusions As whole brain FA data based on SVM showing a high accuracy in diagnosing SAD,brain DTI characteristics have the potential to be the specific indicators in the diagnosis of SAD.SVM might be used as a tool to verify the reliability of white matter abnormalities and provide regions of interest in DTI study of neurological and psychiatric diseases.
