TY - T1的区分偏头痛和创伤后头痛使用机器学习分类器JF -神经学乔-神经病学SP - S5 LP - S6做wnl.0000801780.76758 10.1212/01.。首页b7六世- 98 - 1补充1 AU -吉娜Dumkrieger盟凯瑟琳Daniela庄盟——凯瑟琳·罗斯盟Visar贝里沙非盟-托德·j·Schwedt Y1 2022/01/04 UR - //www.ez-admanager.com/content/98/1_Supplement_1/S5.2.abstract N2 -目标目标是建立首页分类模型区分持续甲状旁腺素(PPTH)和偏头痛的临床资料和mri措施大脑结构和功能连通性。背景甲状旁腺素和偏头痛通常有相似的表型。此外,偏头痛是发展中甲状旁腺素的一个危险因素,有时很难区分甲状旁腺素和恶化的偏头痛症状。设计/方法34偏头痛患者没有历史的创伤性脑损伤和48轻度创伤性脑损伤患者归因于PPTH但没有偏头痛史或之前频繁的紧张性头痛了。受试者完成问卷评估头痛特征、情绪、感觉糖甙和认知功能,并进行了核磁共振成像在同一天。临床特征和大脑结构措施从t1加权成像、弥散张量成像和功能静息状态的措施包括作为潜在变量。分类器使用岭回归的主要组件(PC)是合适的。因为电脑可以阻碍识别重要的变量在模型中,第二个回归模型是适合直接数据。在基于个人计算机的模型中,输入变量选择基于最低t检验或卡方假定值形态。平均精度计算分析交叉验证。 The importance of variables to the classifier were examined.Results The PC-based classifier achieved an average classification accuracy of 85%. The non-PC based classifier achieved an average classification accuracy of 74.4%. Both classifiers were more accurate at classifying migraine subjects than PPTH. The PC-based model incorrectly classified 9/48 (18.8%) PPTH subjects compared to 3/34 (8.8%) migraine patients, whereas the non-PC classifier incorrectly classed 16/48 (33.3%) vs 5/34 (14.7%) of migraine subjects. Important variables in the non-PC model included static and dynamic functional connectivity values, several questions from the Beck Depression Inventory, and worsening symptoms and headaches with mental activity.Conclusions Multivariate models including clinical characteristics, functional connectivity, and brain structural data accurately classify and differentiate PPTH vs migraine. ER -