■功能磁共振成像(fMRI)已成为研究大脑功能的基本工具。然而,功能磁共振成像数据中序列相关性的存在使数据分析变得复杂,违反了分析方法的统计假设,并可能导致功能磁共振成像研究中的错误结论。
■在本文中,我们表明,为具有较长重复时间(TR)(>2s)的数据设计的常规白化程序不足以增加短TRfMRI数据的使用。此外,我们全面研究了现有白化方法的缺点,并引入了一种名为“IDAR”(迭代数据自适应自回归模型)的迭代白化方法来解决这些缺点。IDAR采用具有灵活和数据驱动顺序的高阶自回归(AR)模型,提供在短TR和长TRfMRI数据集中建模复杂序列相关结构的能力。
■常规美白方法,如AR(1),ARMA(1,1),和高阶AR,在减少长TR数据中的序列相关性方面是有效的,但在减少短TR数据中的序列相关性方面在很大程度上是无效的。相比之下,IDAR在解决序列相关性方面明显优于传统方法,电源,长TR和特别是短TR数据的I型错误。然而,IDAR不能同时有效地解决残差相关性和膨胀的I型误差。
■这项研究强调了迫切需要解决短TR(<1s)功能磁共振成像数据中的序列相关问题,越来越多地用于该领域。尽管IDAR可以为广泛的应用程序和数据集解决这个问题,短TR数据的复杂性需要继续探索和创新方法。这些努力对于同时减少串行相关性和控制I型错误率而不损害分析能力至关重要。
UNASSIGNED: Functional magnetic resonance imaging (fMRI) has become a fundamental tool for studying brain function. However, the presence of serial correlations in fMRI data complicates data analysis, violates the statistical assumptions of analyses methods, and can lead to incorrect conclusions in fMRI studies.
UNASSIGNED: In this paper, we show that conventional whitening procedures designed for data with longer repetition times (TRs) (>2 s) are inadequate for the increasing use of short-TR fMRI data. Furthermore, we comprehensively investigate the shortcomings of existing whitening methods and introduce an iterative whitening approach named \"IDAR\" (Iterative Data-adaptive Autoregressive model) to address these shortcomings. IDAR employs high-order autoregressive (AR) models with flexible and data-driven orders, offering the capability to model complex serial correlation structures in both short-TR and long-TR fMRI datasets.
UNASSIGNED: Conventional whitening methods, such as AR(1), ARMA(1,1), and higher-order AR, were effective in reducing serial correlation in long-TR data but were largely ineffective in even reducing serial correlation in short-TR data. In contrast, IDAR significantly outperformed conventional methods in addressing serial correlation, power, and Type-I error for both long-TR and especially short-TR data. However, IDAR could not simultaneously address residual correlations and inflated Type-I error effectively.
UNASSIGNED: This study highlights the urgent need to address the problem of serial correlation in short-TR (< 1 s) fMRI data, which are increasingly used in the field. Although IDAR can address this issue for a wide range of applications and datasets, the complexity of short-TR data necessitates continued exploration and innovative approaches. These efforts are essential to simultaneously reduce serial correlations and control Type-I error rates without compromising analytical power.