A Multi-Dataset Evaluation of Frame Censoring for Motion Correction in Task-Based fMRI.
A Multi-Dataset Evaluation of Frame Censoring for Motion Correction in Task-Based fMRI.
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DOI：
10.52294/apertureneuro.2022.2.nxor2026
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Peelle JE
中科院分区：
文献类型：
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作者：
Jones MS;Zhu Z;Bajracharya A;Luor A;Peelle JE
功能磁共振成像期间的受试者运动会影响我们准确测量感兴趣信号的能力。近年来，帧审查（即使用有害回归量在一般线性模型中统计排除运动污染的数据）已作为一种缓解策略出现在一些基于任务的功能磁共振成像研究中。然而，很少有系统的研究来量化其功效。在本研究中，我们使用开放数据和可重复的工作流程，将帧审查的性能与其他几种常见的基于任务的 fMRI 运动校正方法进行了比较。我们分析了 8 个公开数据集，代表儿童、青少年和成人参与者的 11 项不同任务。使用组分析中的最大 t 值以及单个受试者中基于兴趣区域的平均激活度和分半可靠性来量化表现。我们将跨多个阈值的帧审查与使用 6 和 24 个规范运动回归器、小波去尖峰、鲁棒加权最小二乘法和未经训练的基于 ICA 的去噪进行了总共 240 次单独的分析。用于识别审查帧的阈值基于运动估计 (FD) 和图像强度变化 (DVARS)。相对于标准运动回归器，我们发现适度的帧审查（例如 1-2% 的数据丢失）得到了一致的改进，尽管这些增益通常与使用其他技术可以实现的效果相当。重要的是，没有一种方法在所有数据集和任务中始终优于其他方法。这些发现表明，运动缓解策略的选择取决于数据集和感兴趣的结果指标。
Subject motion during fMRI can affect our ability to accurately measure signals of interest. In recent years, frame censoring—that is, statistically excluding motion-contaminated data within the general linear model using nuisance regressors—has appeared in several task-based fMRI studies as a mitigation strategy. However, there have been few systematic investigations quantifying its efficacy. In the present study, we compared the performance of frame censoring to several other common motion correction approaches for task-based fMRI using open data and reproducible workflows. We analyzed eight publicly available datasets representing 11 distinct tasks in child, adolescent, and adult participants. Performance was quantified using maximum t-values in group analyses, and region of interest–based mean activation and split-half reliability in single subjects. We compared frame censoring across several thresholds to the use of 6 and 24 canonical motion regressors, wavelet despiking, robust weighted least squares, and untrained ICA-based denoising, for a total of 240 separate analyses. Thresholds used to identify censored frames were based on both motion estimates (FD) and image intensity changes (DVARS). Relative to standard motion regressors, we found consistent improvements for modest amounts of frame censoring (e.g., 1–2% data loss), although these gains were frequently comparable to what could be achieved using other techniques. Importantly, no single approach consistently outperformed the others across all datasets and tasks. These findings suggest that the choice of a motion mitigation strategy depends on both the dataset and the outcome metric of interest.