Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, United States.
Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, United States; Department of Psychology, University of Minnesota, Minneapolis, MN, United States.
J Neurosci Methods. 2018 Oct 1;308:74-87. doi: 10.1016/j.jneumeth.2018.06.029. Epub 2018 Jun 30.
fMRI provides spatial resolution that is unmatched by non-invasive neuroimaging techniques. Its temporal dynamics however are typically neglected due to the sluggishness of the hemodynamic signal.
We present temporal multivariate pattern analysis (tMVPA), a method for investigating the temporal evolution of neural representations in fMRI data, computed on single-trial BOLD time-courses, leveraging both spatial and temporal components of the fMRI signal. We implemented an expanding sliding window approach that allows identifying the time-window of an effect.
We demonstrate that tMVPA can successfully detect condition-specific multivariate modulations over time, in the absence of mean BOLD amplitude differences. Using Monte-Carlo simulations and synthetic data, we quantified family-wise error rate (FWER) and statistical power. Both at the group and single-subject levels, FWER was either at or significantly below 5%. We reached the desired power with 18 subjects and 12 trials for the group level, and with 14 trials in the single-subject scenario.
We compare the tMVPA statistical evaluation to that of a linear support vector machine (SVM). SVM outperformed tMVPA with large N and trial numbers. Conversely, tMVPA, leveraging on single trials analyses, outperformed SVM in low N and trials and in a single-subject scenario.
Recent evidence suggesting that the BOLD signal carries finer-grained temporal information than previously thought, advocates the need for analytical tools, such as tMVPA, tailored to investigate BOLD temporal dynamics. The comparable performance between tMVPA and SVM, a powerful and reliable tool for fMRI, supports the validity of our technique.
功能磁共振成像（fMRI）提供了无与伦比的空间分辨率，但其时间动态通常由于血液动力学信号的迟缓而被忽略。
我们提出了时变多变量模式分析（tMVPA），这是一种在 fMRI 数据中研究神经表示的时间演化的方法，该方法基于单试次 BOLD 时间序列计算，利用 fMRI 信号的空间和时间成分。我们实现了一种扩展的滑动窗口方法，允许识别效应的时间窗口。
我们证明 tMVPA 可以成功地检测到时间上的条件特异性多变量调制，而无需平均 BOLD 幅度差异。使用蒙特卡罗模拟和合成数据，我们量化了组级和单级的假阳性率（FWER）和统计功效。无论是在组级还是单级水平，FWER 要么达到要么显著低于 5%。我们在组级达到 18 个被试和 12 个试次的期望功效，在单级达到 14 个试次的期望功效。
我们将 tMVPA 的统计评估与线性支持向量机（SVM）的评估进行了比较。SVM 在大 N 和试次数时优于 tMVPA。相反，tMVPA 利用单试次分析，在低 N 和试次以及单级场景中优于 SVM。
最近的证据表明，BOLD 信号比以前认为的携带更精细的时间信息，因此需要分析工具，如 tMVPA，专门用于研究 BOLD 时间动态。tMVPA 和 SVM 的性能相当，SVM 是 fMRI 的一种强大可靠的工具，支持我们技术的有效性。
