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首页> 外文期刊>Medical image analysis >Major depressive disorder identification by referenced multiset canonical correlation analysis with clinical scores
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Major depressive disorder identification by referenced multiset canonical correlation analysis with clinical scores

机译：通过临床评分引用的多重规范相关分析鉴定主要抑郁症

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摘要

A novel method based on multiset canonical correlation analysis (mCCA) and linear discriminant analysis (LDA) is presented to identify the major depressive disorder (MDD). The new method comprises two parts, namely, the mCCA-rreg and sparse LDA models. The mCCA-rreg model extends the classical canonical correlation model to calculate functional connections by restricting the references to a reference space and adding a spatial regularization term. The reference space is used to ensure that the model extracts important components first from several datasets simultaneously by decreasing the importance of the components in which we are uninterested. The spatial regularization term helps in avoiding the multicollinearity and overfitting problems under the low signal-to-noise ratio circumstance. The sparse LDA model extends the classical LDA model to extract a small subset of discriminative classification features by fusing clinical scores. In the real data experiment, we extract two functional connection modes from 45 subjects by the mCCA-rreg model. Then, we construct classifiers to identify the patients with MDD based on the connections selected by the sparse LDA model. The best accuracy is higher than 95%. The results show that the mCCA-rreg model can retrieve the important components characterized by a preassigned reference space and exclude the noise or components of no interest. The sparse LDA model can extract discriminative classification features related to clinical scores. (C) 2019 Elsevier B.V. All rights reserved.
机译：提出了一种基于多型规范相关分析（MCCA）和线性判别分析（LDA）的新方法以鉴定主要抑郁症（MDD）。新方法包括两部分，即MCCA-RREG和LDA模型。 MCCA-RREG模型扩展了经典规范相关模型来通过限制参考空间引用并添加空间正则化术语来计算功能连接。参考空间用于通过减少我们无趣的组件的重要性，确保模型首先从多个数据集中提取重要组成部分。空间正则化术语有助于避免在低信噪比环境下的多色性和过度拟合问题。稀疏LDA模型扩展了经典LDA模型，通过融合临床评分来提取识别分类特征的小局部集。在真实数据实验中，我们通过MCCA-RREG模型从45个受试者中提取两个功能连接模式。然后，我们构建基于稀疏LDA模型选择的连接的MDD患者识别分类器。最佳精度高于95％。结果表明，MCCA-RREG模型可以检索特征的重要组成部分，其特征是预测的参考空间，并排除了无兴趣的噪声或组件。稀疏LDA模型可以提取与临床评分相关的鉴别分类特征。 （c）2019年Elsevier B.V.保留所有权利。

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