Yue Kun, Webster Jason, Grabowski Thomas, Shojaie Ali, Jahanian Hesamoddin
Department of Biostatistics, University of Washington, Seattle, WA, United States.
Department of Radiology, University of Washington, Seattle, WA, United States.
Front Neurosci. 2024 Aug 2;18:1381722. doi: 10.3389/fnins.2024.1381722. eCollection 2024.
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.
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.
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.
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.
功能磁共振成像（fMRI）已成为研究脑功能的一项基础工具。然而，fMRI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fMRI研究得出错误结论。
在本文中，我们表明，为较长重复时间（TRs）（>2秒）的数据设计的传统白化程序不足以应对短TR fMRI数据日益增加的使用情况。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采用具有灵活且数据驱动阶数的高阶自回归（AR）模型，能够对短TR和长TR fMRI数据集中复杂的序列相关结构进行建模。
传统的白化方法，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有效，但在减少短TR数据中的序列相关性方面基本无效。相比之下，在处理长TR数据尤其是短TR数据的序列相关性、功效和I型错误方面，IDAR明显优于传统方法。然而，IDAR无法同时有效解决残余相关性和过高的I型错误。
本研究突出了迫切需要解决短TR（<1秒）fMRI数据中的序列相关性问题，该数据在该领域的使用日益增加。尽管IDAR可以在广泛的应用和数据集中解决这个问题，但短TR数据的复杂性需要持续探索和创新方法。这些努力对于在不影响分析功效的情况下同时减少序列相关性和控制I型错误率至关重要。
