Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, 21250 MD, USA.
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, 21250 MD, USA.
J Neurosci Methods. 2021 Jul 1;358:109214. doi: 10.1016/j.jneumeth.2021.109214. Epub 2021 May 3.
Data-driven methods such as independent component analysis (ICA) makes very few assumptions on the data and the relationships of multiple datasets, and hence, are attractive for the fusion of medical imaging data. Two important extensions of ICA for multiset fusion are the joint ICA (jICA) and the multiset canonical correlation analysis and joint ICA (MCCA-jICA) techniques. Both approaches assume identical mixing matrices, emphasizing components that are common across the multiple datasets. However, in general, one would expect to have components that are both common across the datasets and distinct to each dataset.
We propose a general framework, disjoint subspace analysis using ICA (DS-ICA), which identifies and extracts not only the common but also the distinct components across multiple datasets. A key component of the method is the identification of these subspaces and their separation before subsequent analyses, which helps establish better model match and provides flexibility in algorithm and order choice.
We compare DS-ICA with jICA and MCCA-jICA through both simulations and application to multiset functional magnetic resonance imaging (fMRI) task data collected from healthy controls as well as patients with schizophrenia.
The results show DS-ICA estimates more components discriminative between healthy controls and patients than jICA and MCCA-jICA, and with higher discriminatory power showing activation differences in meaningful regions. When applied to a classification framework, components estimated by DS-ICA results in higher classification performance for different dataset combinations than the other two methods.
These results demonstrate that DS-ICA is an effective method for fusion of multiple datasets.
数据驱动方法，如独立成分分析（ICA），对数据和多个数据集之间的关系几乎没有假设，因此对于医学成像数据的融合很有吸引力。ICA 用于多数据集融合的两个重要扩展是联合 ICA（jICA）和多数据集典范相关分析和联合 ICA（MCCA-jICA）技术。这两种方法都假设存在相同的混合矩阵，强调的是跨多个数据集共有的成分。然而，一般来说，人们期望存在既跨数据集共有的成分，又与每个数据集不同的成分。
我们提出了一种通用框架，即使用 ICA 的不相交子空间分析（DS-ICA），该方法可以识别和提取不仅跨多个数据集共有的而且独特的成分。该方法的一个关键组成部分是在后续分析之前识别这些子空间并对其进行分离，这有助于建立更好的模型匹配，并在算法和顺序选择方面提供灵活性。
我们通过模拟和应用于从健康对照者和精神分裂症患者收集的多数据集功能磁共振成像（fMRI）任务数据，将 DS-ICA 与 jICA 和 MCCA-jICA 进行了比较。
结果表明，DS-ICA 估计的成分比 jICA 和 MCCA-jICA 更能区分健康对照者和患者，并且具有更高的区分能力，显示出在有意义的区域存在激活差异。当应用于分类框架时，DS-ICA 估计的成分在不同数据集组合的分类性能优于其他两种方法。
这些结果表明，DS-ICA 是一种有效的多数据集融合方法。
