Liu Jingyu, Demirci Oguz, Calhoun Vince D
J. Liu and V. D. Calhoun are with the MIND Institute and the Department of Electrical Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA (e-mail:
IEEE Signal Process Lett. 2008 Jan 1;15:413-416. doi: 10.1109/LSP.2008.922513.
Relationships between genomic data and functional brain images are of great interest but require new analysis approaches to integrate the high-dimensional data types. This letter presents an extension of a technique called parallel independent component analysis (paraICA), which enables the joint analysis of multiple modalities including interconnections between them. We extend our earlier work by allowing for multiple interconnections and by providing important overfitting controls. Performance was assessed by simulations under different conditions, and indicated reliable results can be extracted by properly balancing overfitting and underfitting. An application to functional magnetic resonance images and single nucleotide polymorphism array produced interesting findings.
基因组数据与功能性脑图像之间的关系备受关注，但需要新的分析方法来整合这些高维数据类型。本文介绍了一种名为并行独立成分分析（paraICA）技术的扩展，它能够对多种模态进行联合分析，包括它们之间的相互联系。我们通过允许多种相互联系并提供重要的过拟合控制来扩展我们早期的工作。通过在不同条件下的模拟评估性能，结果表明通过适当平衡过拟合和欠拟合可以提取可靠的结果。将其应用于功能磁共振图像和单核苷酸多态性阵列产生了有趣的发现。
