[论文解读] Analysis of BOLD fMRI Signal Preprocessing Pipeline on Different Datasets while Reducing False Positive Rates

Yunxiang Ge, Yu Pan+1 more|arXiv (Cornell University)|Dec 27, 2017

Functional Brain Connectivity Studies参考文献 20被引用 3

一句话总结

本研究评估了在多个数据集上，不同预处理流程对静息态BOLD fMRI数据的影响，以降低统计分析中的假阳性率。通过系统性地改变预处理步骤（如头动校正、标准化、平滑和滤波），研究发现：移除初始时间点并进行去趋势处理可降低假阳性率，而滤波在单被试分析中始终能降低假阳性率，尽管协变量回归与滤波在不同数据集中的影响复杂且不一致。

ABSTRACT

The technology of functional Magnetic Resonance Imaging (fMRI) based on Blood Oxygen Level Dependent (BOLD) signal has been widely used in clinical treatments and brain function researches. The BOLD signal has to be preprocessed before being analyzed using either functional connectivity measurements or statistical methods. Current researches show that data preprocessing steps may influence the results of analysis, yet there is no consensus on preprocessing method. In this paper, an evaluation method is proposed for analyzing the preprocessing pipeline of resting state BOLD fMRI (rs-BOLD fMRI) data under putative task experiment designs to cast some lights on the preprocessing stage, covering both first and second level analysis. The choices of preprocessing parameters and steps are altered to investigate preprocessing pipelines while observing statistical analysis results, trying to reduce false positives as reported by Eklund et al. in their 2016 PNAS paper. All of the experiment data are separated into 7 datasets, consisting of 220 healthy control samples and 136 patient data that are from 38 incomplete Spinal Cord Injury (SCI) patients and 16 Cerebral Stroke (CS) patients, including multiple scans of some patients at different time. These data were acquired from two different MRI scanners, which may cause difference in analysis results. The evaluation result shows that it has little effect to change parameters in each steps of the classical preprocessing pipeline, which consists head motion correction, normalization and smoothing. Removing time points and the following detrend step can reduce false positives. However, covariates regression and filtering has complicated effects on the data. Note that for single subject analysis, false positives declined consistently after filtering. The result of patient data and healthy controls data which are scanned under the same machine with ...

研究动机与目标

• 研究BOLD fMRI预处理流程的差异如何影响功能连接和任务相关分析中的统计假阳性率。
• 评估预处理选择在多个数据集（包括健康对照组及脊髓损伤或脑卒中患者）中的稳健性。
• 评估扫描仪差异（两种不同MRI扫描仪）对预处理结果和统计可靠性的影响。
• 识别出能持续降低假阳性率的预处理步骤，特别是在Eklund等人2016年PNAS研究发现类型I误差率被夸大之后。
• 为提升fMRI研究中统计有效性的预处理流程提供基于证据的建议。

提出的方法

• 本研究系统评估了经典预处理步骤：头动校正、标准化和平滑，同时调整相关参数。
• 评估了移除初始时间点并应用去趋势处理对减少虚假信号波动的影响。
• 测试了协变量回归和时间滤波对不同数据集中假阳性率的影响。
• 分析包含220名健康对照组和136名患者扫描数据（38例SCI，16例CS），部分患者有多次扫描。
• 数据来自两种不同MRI扫描仪，以评估扫描仪相关差异对预处理结果的影响。
• 统计分析在一级（单被试）和二级（组间）阶段均进行，以评估不同预处理配置下的假阳性率。

实验结果

研究问题

• RQ1 预处理参数的变化在rs-BOLD fMRI数据中如何影响假阳性率？
• RQ2 哪些预处理步骤（如去趋势、滤波或协变量回归）最能有效降低假阳性率？
• RQ3 两种MRI系统之间的扫描仪差异如何影响预处理结果和统计可靠性？
• RQ4 滤波是否在健康对照组和患者人群中均一致地降低假阳性率？
• RQ5 预处理流程选择在多大程度上影响功能连接和任务相关分析的有效性？

主要发现

• 移除初始时间点并应用去趋势处理在多个数据集中显著降低了假阳性率。
• 滤波在单被试分析中始终能降低假阳性率，表明其在个体水平fMRI研究中的价值。
• 协变量回归与滤波对假阳性率的影响复杂且不一致，因数据集和扫描仪类型而异。
• 经典预处理流程（头动校正、标准化、平滑）在假阳性膨胀方面对参数变化的敏感性极低。
• 两种MRI扫描仪之间的差异引入了变异性，但预处理选择对统计误差率的影响比扫描仪类型本身更一致。
• 本研究支持采用时间点移除和去趋势处理作为提高fMRI预处理统计有效性的可靠策略。