Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, USA.
Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, USA; Interdepartmental Program in Neuroscience, Northwestern University, Chicago, USA.
Neuroimage. 2015 Aug 15;117:67-79. doi: 10.1016/j.neuroimage.2015.05.015. Epub 2015 May 15.
Resting-state functional magnetic resonance imaging (rs-fMRI) has become an increasingly important tool in mapping the functional networks of the brain. This tool has been used to examine network changes induced by cognitive and emotional states, neurological traits, and neuropsychiatric disorders. However, noise that remains in the rs-fMRI data after preprocessing has limited the reliability of individual-subject results, wherein scanner artifacts, subject movements, and other noise sources induce non-neural temporal correlations in the blood oxygen level-dependent (BOLD) timeseries. Numerous preprocessing methods have been proposed to isolate and remove these confounds; however, the field has not coalesced around a standard preprocessing pipeline. In comparisons, these preprocessing methods are often assessed with only a single metric of rs-fMRI data quality, such as reliability, without considering other aspects in tandem, such as signal-to-noise ratio and group discriminability. The present study seeks to identify the data preprocessing pipeline that optimizes rs-fMRI data across multiple outcome measures. Specifically, we aim to minimize the noise in the data and maximize result reliability, while retaining the unique features that characterize distinct groups. We examine how these metrics are influenced by bandpass filter selection and noise regression in four datasets, totaling 181 rs-fMRI scans and 38 subject-driven memory scans. Additionally, we perform two different rs-fMRI analysis methods - dual regression and region-of-interest based functional connectivity - and highlight the preprocessing parameters that optimize both approaches. Our results expand upon previous reports of individual-scan reliability, and demonstrate that preprocessing parameter selection can significantly change the noisiness, reliability, and heterogeneity of rs-fMRI data. The application of our findings to rs-fMRI data analysis should improve the validity and reliability of rs-fMRI results, both at the individual-subject level and the group level.
静息态功能磁共振成像（rs-fMRI）已成为绘制大脑功能网络的重要工具。该工具已用于研究认知和情绪状态、神经特征和神经精神障碍引起的网络变化。然而，预处理后仍存在于 rs-fMRI 数据中的噪声限制了个体受试者结果的可靠性，其中扫描仪伪影、受试者运动和其他噪声源会在血氧水平依赖（BOLD）时间序列中引起非神经的时间相关性。已经提出了许多预处理方法来隔离和去除这些混杂因素；然而，该领域尚未围绕标准预处理流水线达成一致。在比较中，这些预处理方法通常仅使用 rs-fMRI 数据质量的单一指标进行评估，例如可靠性，而没有同时考虑其他方面，例如信噪比和组可分辨性。本研究旨在确定优化 rs-fMRI 数据的多个结果测量的预处理流水线。具体来说，我们旨在最大限度地减少数据中的噪声并提高结果可靠性，同时保留表征不同群体的独特特征。我们研究了这些指标如何受到四个数据集的带通滤波器选择和噪声回归的影响，总共有 181 个 rs-fMRI 扫描和 38 个受受试者驱动的记忆扫描。此外，我们还进行了两种不同的 rs-fMRI 分析方法 - 双回归和基于感兴趣区域的功能连接 - 并突出了优化这两种方法的预处理参数。我们的结果扩展了以前关于个体扫描可靠性的报告，并证明预处理参数选择可以显著改变 rs-fMRI 数据的噪声、可靠性和异质性。我们的研究结果应用于 rs-fMRI 数据分析应提高 rs-fMRI 结果的有效性和可靠性，无论是在个体受试者水平还是在组水平。
