کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8687451 | 1580847 | 2017 | 44 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Supervoxel-based statistical analysis of diffusion tensor imaging in schizotypal personality disorder
ترجمه فارسی عنوان
آنالیز آماری مبتنی بر سرککسل از تصویر برداری تنگی نفوذ در اختلال شخصیت اسکیزوتایپیک
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
چکیده انگلیسی
To study white matter changes in schizotypal personality disorder (SPD), we developed a new statistical analysis method based on supervoxels for diffusion tensor imaging. Twenty patients with SPD and eighteen healthy controls were recruited from a pool of 3000 first-year university undergraduates, and underwent MRI using a 3T scanner. Diffusion tensors were first normalized into ICBM-152 space followed by a supervoxel segmentation based on graph clustering to segment white matter tensors into diffusion homogeneous supervoxels. Fractional anisotropy (FA) values in supervoxels were compared between SPD and healthy controls using permutation test. Suprathreshold cluster size test was used to correct multiple comparison. At last, fibers with significant differences were extracted from supervoxel clusters with significance level PÂ <Â 0.05. Results showed that FA values in genu of corpus callosum were significantly reduced (PÂ =Â 0.012) in patients with SPD (FAÂ =Â 0.565) compared with healthy controls (FAÂ =Â 0.593). In summary, this study proposed a novel supervoxel segmentation method for diffusion tensor imaging using graph-based clustering, and extended permutation test and suprathreshold cluster size test to supervoxels for detection of white matter changes.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 163, December 2017, Pages 368-378
Journal: NeuroImage - Volume 163, December 2017, Pages 368-378
نویسندگان
Teng Zhang, Defeng Wang, Qing Zhang, Jianlin Wu, Jian Lv, Lin Shi,