Department of Psychology, University of Houston Houston, TX, USA.
Front Psychol. 2010 Oct 8;1:35. doi: 10.3389/fpsyg.2010.00035. eCollection 2010.
Functional data analysis (FDA) considers the continuity of the curves or functions, and is a topic of increasing interest in the statistics community. FDA is commonly applied to time-series and spatial-series studies. The development of functional brain imaging techniques in recent years made it possible to study the relationship between brain and mind over time. Consequently, an enormous amount of functional data is collected and needs to be analyzed. Functional techniques designed for these data are in strong demand. This paper discusses three statistically challenging problems utilizing FDA techniques in functional brain imaging analysis. These problems are dimension reduction (or feature extraction), spatial classification in functional magnetic resonance imaging studies, and the inverse problem in magneto-encephalography studies. The application of FDA to these issues is relatively new but has been shown to be considerably effective. Future efforts can further explore the potential of FDA in functional brain imaging studies.
功能数据分析（FDA）考虑了曲线或函数的连续性，是统计学领域日益关注的一个话题。FDA 通常应用于时间序列和空间序列研究。近年来，功能性脑成像技术的发展使得研究大脑和思维之间的关系成为可能。因此，大量的功能数据被收集并需要进行分析。针对这些数据设计的功能技术需求量很大。本文讨论了在功能性脑成像分析中利用 FDA 技术的三个具有统计学挑战性的问题。这些问题是降维（或特征提取）、功能磁共振成像研究中的空间分类，以及脑磁图研究中的逆问题。FDA 在这些问题中的应用相对较新，但已经被证明是非常有效的。未来的研究可以进一步探索 FDA 在功能性脑成像研究中的潜力。
