Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging
Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging
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DOI：
10.1016/j.dadm.2019.06.002
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发表时间：
2019-12-01
影响因子：
5.3
通讯作者：
Sedeno, Lucas
中科院分区：
文献类型：
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作者：
Donnelly-Kehoe, Patricio Andres;Pascariello, Guido Orlando;Sedeno, Lucas
前言：行为变异额颞叶痴呆(BvFTD)的及时诊断仍然具有挑战性，因为它依赖于临床专业知识和潜在的模棱两可的诊断指南。最近的建议强调了多模式神经成像和机器学习方法作为解决这一问题的补充工具的作用。方法：我们开发了一种自动的、跨中心的多模式计算方法，用于bvFTD患者和健康对照的稳健分类。我们使用全自动处理流水线分析了44例bvFTD患者和60例健康对照(三个不同采集协议的成像中心)的结构磁共振成像和静息状态功能连通性。结果：我们的方法成功地结合了多模式成像信息，具有高精度(91%)、高灵敏度(83.7%)和高特异性(96.6%)。讨论：这种多模式方法提高了系统的性能，为神经成像分析提供了临床信息方法。这突显了将多模式成像和机器学习相结合作为痴呆症诊断的黄金标准的相关性。(C)2019年提交人。由爱思唯尔公司代表阿尔茨海默氏症协会出版。
Introduction: Timely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods as complementary tools to address this problem.Methods: We developed an automatic, cross-center, multimodal computational approach for robust classification of patients with bvFTD and healthy controls. We analyzed structural magnetic resonance imaging and resting-state functional connectivity from 44 patients with bvFTD and 60 healthy controls (across three imaging centers with different acquisition protocols) using a fully automated processing pipeline, including site normalization, native space feature extraction, and a random forest classifier.Results: Our method successfully combined multimodal imaging information with high accuracy (91%), sensitivity (83.7%), and specificity (96.6%).Discussion: This multimodal approach enhanced the system's performance and provided a clinically informative method for neuroimaging analysis. This underscores the relevance of combining multimodal imaging and machine learning as a gold standard for dementia diagnosis. (C) 2019 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association.