一种使用T1w/T2w比率预测抑郁症的机器学习算法方案。
Protocol for a machine learning algorithm predicting depressive disorders using the T1w/T2w ratio.
作者信息
Baranger David A A, Halchenko Yaroslav O, Satz Skye, Ragozzino Rachel, Iyengar Satish, Swartz Holly A, Manelis Anna
机构信息
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA.
Department of Psychological and Brain Sciences, Dartmouth College, NH, USA.
出版信息
MethodsX. 2021 Dec 2;8:101595. doi: 10.1016/j.mex.2021.101595. eCollection 2021.
The 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.
T1加权/ T2加权比值是一种新型的磁共振成像（MRI）测量指标，被认为对皮质髓鞘敏感。使用这种新型测量指标需要开发用于数据质量保证、数据分析以及研究结果验证的新型流程，以便将T1加权/ T2加权比值应用于与髓鞘水平变化相关的疾病分类。在本文中，我们详细描述了这样一个流程，并提供了我们最近一份报告中使用的脚本的参考文献，该报告应用T1加权/ T2加权比值和机器学习从健康对照中对患有抑郁症的个体进行分类。
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