Predicting primary progressive aphasias with support vector machine approaches in structural MRI data.
Predicting primary progressive aphasias with support vector machine approaches in structural MRI data.
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DOI：
10.1016/j.nicl.2017.02.003
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
FTLDc study group
中科院分区：
文献类型：
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作者：
Bisenius S;Mueller K;Diehl-Schmid J;Fassbender K;Grimmer T;Jessen F;Kassubek J;Kornhuber J;Landwehrmeyer B;Ludolph A;Schneider A;Anderl-Straub S;Stuke K;Danek A;Otto M;Schroeter ML;FTLDc study group
原发性进行性失语(PPA)包括非流利/无语法变异型PPA、语义变异型PPA和对数变异型PPA，以不同的语言障碍和脑萎缩为特征。为了验证结构磁共振成像数据在早期个体诊断中的潜力，我们在基于体素的形态计量分析获得的灰质密度图上使用支持向量机分类来区分20名健康对照(样本量、年龄和性别相匹配的)PPA亚型(44名患者：16名非流畅/无语法变异型PPA，17名语义型PPA，11名对数变异型PPA)。在这里，我们比较了全脑和基于荟萃分析的疾病特定感兴趣区域分类方法用于支持向量机分类。我们还使用支持向量机分类来区分这三个PPA亚型。与健康对照组相比，全脑支持向量机分类能够识别特定的PPA亚型，准确率在91%到97%之间，对于语义变体和非流利/无语法或对数形式的PPA变体的区分，准确率达到78%/95%。仅对不流利/无语法和对数形式的PPA变种的区分准确率较低，仅为55%。有趣的是，对患者的支持向量机分类贡献最大的区域与组比较显示的这些患者中萎缩的区域很大程度上相对应。尽管全脑方法也考虑了感兴趣区域方法中没有覆盖的区域，但由于所选网络的疾病特异性，这两种方法显示出类似的准确性。结论，多中心结构磁共振成像数据的支持向量机分类能够以非常高的精度预测PPA亚型，为其在临床上的应用铺平了道路。目的是评估多中心MRI数据在PPA个体化诊断中的潜力。我们在PPA变异体和健康对照中使用了支持向量机分类。我们比较了全脑方法和ROI(取自荟萃分析)方法。总体而言，无论是整个大脑还是ROI方法，准确率都相当高。
Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest approach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Interestingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Although the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings. Aim was to evaluate the potential of multi-center MRI data for individual PPA diagnosis. We used support vector machine classification in PPA variants and healthy controls. We compared a whole brain approach with a ROI (taken from meta-analyses) approach. Accuracies were overall quite high, for both, the whole brain and the ROI approach.