文章基本信息

标题：Protocol for a machine learning algorithm predicting depressive disorders using the T1w/T2w ratio
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作者：David A.A. Baranger ; Yaroslav O. Halchenko ; Skye Satz 等
期刊名称：MethodsX
印刷版ISSN：2215-0161
电子版ISSN：2215-0161
出版年度：2021
卷号：8
页码：1-8
DOI：10.1016/j.mex.2021.101595
语种：English
出版社：Elsevier
摘要：AbstractThe T1w/T2w ratio is a novel magnetic resonance imaging (MRI) measure that is thought to be sensitive to cortical myelin. Using this novel measure requires developing novel pipelines for the data quality assurance, data analysis, and validation of the findings in order to apply the T1w/T2w ratio for classification of disorders associated with the changes in the myelin levels. In this article, we provide a detailed description of such a pipeline as well as the reference to the scripts used in our recent report that applied the T1w/T2w ratio and machine learning to classify individuals with depressive disorders from healthy controls.Graphical abstractDisplay Omitted
关键词：Depression;Cortical myelin;MRI;Machine learning;T1w/T2w ratio;Elastic net;LDA