Rodriguez Pedro A, Calhoun Vince D, Adalı Tülay
University of Maryland, Baltimore County, Department of CSEE, Baltimore, MD 21250.
Pattern Recognit. 2012 Jun 1;45(6):2050-2063. doi: 10.1016/j.patcog.2011.04.033.
Analysis of functional magnetic resonance imaging (fMRI) data in its native, complex form has been shown to increase the sensitivity both for data-driven techniques, such as independent component analysis (ICA), and for model-driven techniques. The promise of an increase in sensitivity and specificity in clinical studies, provides a powerful motivation for utilizing both the phase and magnitude data; however, the unknown and noisy nature of the phase poses a challenge. In addition, many complex-valued analysis algorithms, such as ICA, suffer from an inherent phase ambiguity, which introduces additional difficulty for group analysis. We present solutions for these issues, which have been among the main reasons phase information has been traditionally discarded, and show their effectiveness when used as part of a complex-valued group ICA algorithm application. The methods we present thus allow the development of new fully complex data-driven and semi-blind methods to process, analyze, and visualize fMRI data.We first introduce a phase ambiguity correction scheme that can be either applied subsequent to ICA of fMRI data or can be incorporated into the ICA algorithm in the form of prior information to eliminate the need for further processing for phase correction. We also present a Mahalanobis distance-based thresholding method, which incorporates both magnitude and phase information into a single threshold, that can be used to increase the sensitivity in the identification of voxels of interest. This method shows particular promise for identifying voxels with significant susceptibility changes but that are located in low magnitude (i.e. activation) areas. We demonstrate the performance gain of the introduced methods on actual fMRI data.
对功能磁共振成像（fMRI）的原始复杂形式数据进行分析已被证明能提高数据驱动技术（如独立成分分析（ICA））和模型驱动技术的敏感性。在临床研究中提高敏感性和特异性的前景，为同时利用相位和幅度数据提供了强大动力；然而，相位的未知性和噪声特性带来了挑战。此外，许多复数值分析算法（如ICA）存在固有的相位模糊性，这给组分析带来了额外困难。我们针对这些问题提出了解决方案，这些问题一直是传统上舍弃相位信息的主要原因，并展示了将其用作复数值组ICA算法应用一部分时的有效性。我们提出的方法因此允许开发新的完全复数据驱动和半盲方法来处理、分析和可视化fMRI数据。我们首先介绍一种相位模糊校正方案，它既可以在fMRI数据的ICA之后应用，也可以以前验信息的形式纳入ICA算法，从而无需进一步进行相位校正处理。我们还提出了一种基于马氏距离的阈值化方法，该方法将幅度和相位信息纳入单个阈值，可用于提高识别感兴趣体素的敏感性。该方法在识别具有显著磁化率变化但位于低幅度（即激活）区域的体素方面显示出特别的前景。我们在实际的fMRI数据上展示了所引入方法的性能提升。
