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Sample-Poor Estimation of Order and Common Signal Subspace with Application to Fusion of Medical Imaging Data

机译：阶次和公共信号子空间的样本差估计及其在医学成像数据融合中的应用

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摘要

Due to their data-driven nature, multivariate methods such as canonical correlation analysis (CCA) have proven very useful for fusion of multimodal neurological data. However, being able to determine the degree of similarity between datasets and appropriate order selection are crucial to the success of such techniques. The standard methods for calculating the order of multimodal data focus only on sources with the greatest individual energy and ignore relations across datasets. Additionally, these techniques as well as the most widely-used methods for determining the degree of similarity between datasets assume sufficient sample support and are not effective in the sample-poor regime. In this paper, we propose to jointly estimate the degree of similarity between datasets and their order when few samples are present using principal component analysis and canonical correlation analysis (PCA-CCA). By considering these two problems simultaneously, we are able to minimize the assumptions placed on the data and achieve superior performance in the sample-poor regime compared to traditional techniques. We apply PCA-CCA to the pairwise combinations of functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI), and electroencephalogram (EEG) data drawn from patients with schizophrenia and healthy controls while performing an auditory oddball task. The PCA-CCA results indicate that the fMRI and sMRI datasets are the most similar, whereas the sMRI and EEG datasets share the least similarity. We also demonstrate that the degree of similarity obtained by PCA-CCA is highly predictive of the degree of significance found for components generated using CCA.
机译：由于其数据驱动的特性，已证明诸如规范相关分析（CCA）之类的多变量方法对于多模态神经病学数据的融合非常有用。但是，能够确定数据集之间的相似程度和适当的顺序选择对于此类技术的成功至关重要。计算多峰数据顺序的标准方法仅关注具有最大个体能量的源，而忽略数据集之间的关系。此外，这些技术以及确定数据集之间相似程度的最广泛使用的方法都假定有足够的样本支持，并且在样本匮乏的情况下无效。在本文中，我们建议使用主成分分析和规范相关分析（PCA-CCA）来联合估计在样本很少的情况下数据集之间的相似程度及其顺序。通过同时考虑这两个问题，与传统技术相比，我们能够在数据贫乏的情况下最大限度地减少对数据的假设，并实现出色的性能。我们将PCA-CCA应用于功能性磁共振成像（fMRI），结构性磁共振成像（sMRI）和脑电图（EEG）数据的配对组合，这些数据来自精神分裂症患者和健康对照者，同时执行听觉杂项任务。 PCA-CCA结果表明，fMRI和sMRI数据集最相似，而sMRI和EEG数据集的相似度最低。我们还证明了PCA-CCA获得的相似度可以高度预测使用CCA生成的组件的重要性。

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