基于支持向量机的青少年吸烟者大脑白质各向异性分数研究  

Research on Fraction Anisotropy of Brain White Matter in Adolescent Smokers Based on Support Vector Machine

作　　者：贾少迪 薛婷[1] 程永欣 王宏德 王娟[1] 董芳[1] 周洋[1] 袁凯[1,3] 喻大华 JIA Shaodi;XUE Ting;CHENG Yongxin(School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou,the Nei Monggol Autonomous Region 014010,P.R.China)

机构地区：[1]内蒙古科技大学数智产业学院(网络安全学院),包头014010 [2]锡林郭勒盟蒙医医院,内蒙古锡林浩特026000 [3]西安电子科技大学生命科学与技术学院,710071 [4]内蒙古科技大学自动化与电气工程学院,包头014010

出　　处：《临床放射学杂志》2024年第4期510-513,共4页Journal of Clinical Radiology

基　　金：国家自然科学基金项目(编号:82260359、81871430、81871426);内蒙古自治区自然科学基金项目(编号:2020MS08059、2021MS08014)。

摘　　要：目的 通过采集60名年轻吸烟者和与之在性别、受教育程度等方面相匹配的60名年轻非吸烟者的扩散张量成像数据中各向异性分数。方法 使用基于纤维束的空间统计学分析方法和一种基于支持向量机的分类方法,在大脑白质50个区域对两组被试在体素水平上对其分类预测,为检测大脑的吸烟状况以及在区分成瘾患者和健康组之间提供生物标志物。结果 该分类的平均准确率为87.50%,曲线下面积为0.92。对分类结果影响最主要的在小脑下脚两侧、皮质脊髓束右侧、大脑脚右侧、扣带(海马体)两侧、钩束左侧、穹隆和小脑上脚右侧。结论各向异性分数在检测吸烟状况方面完全可以作为鉴别性生物标志物,并在预测分类方面具有巨大的潜力,并为机器学习研究与吸烟相关的神经生理学研究提供新的研究视角。Objective By collecting the anisotropy fraction FA in the diffusion tensor imaging data of 60 young smokers and 60 young non⁃smokers who were matched in terms of gender and education level,using the fiber bundle⁃based spatial statistical analysis method And a classification method based on support vector machine,in the 50 regions of the white mat⁃ter of the brain,the classification prediction of the two groups of subjects at the voxel level was provided to detect the smok⁃ing status of the brain and to distinguish between addicted patients and healthy groups’biomarkers.The classification had an average accuracy of 87.50%and an area under the curve of 0.92.The most important influence on the classification results was on both sides of the lower cerebellar peduncle,the right side of the corticospinal tract,the right side of the cerebral pe⁃duncle,both sides of the cingulate(hippocampus),the left side of the uncinate bundle,the fornix and the right side of the upper cerebellar peduncle.Our results demonstrated that the anisotropy score was fully functional as a discriminative bio⁃marker in detecting smoking status and had great potential in predictive classification and provides new insights into ma⁃chine learning studies of smoking⁃related neurophysiology perspective.

关 键 词：青少年吸烟者 扩散张量成像 各向异性分数 白质 支持向量机 

分 类 号：R163[医药卫生—公共卫生与预防医学] R445.2