Lahaye Pierre-Jean, Poline Jean-Baptiste, Flandin Guillaume, Dodel Silke, Garnero Line
IFR-49, Imagerie NeuroFonctionnelle, France.
Neuroimage. 2003 Oct;20(2):962-74. doi: 10.1016/S1053-8119(03)00340-9.
Correlation analysis has been widely used in the study of functional connectivity based on fMRI data. It assumes that the relevant information about the interactions of brain regions is reflected by a linear relationship between the values of two signals at the same time. However, this hypothesis has not been thoroughly investigated yet. In this work, we study in depth the information shared by BOLD signals of pairs of brain regions. In particular, we assess the amount of nonlinear and/or nonsynchronous interactions present in data. This is achieved by testing models reflecting linear, synchronous interactions against more general models, encompassing nonlinear, nonsynchronous interactions. Many factors influencing measured BOLD signals are critical for the study of connectivity, such as paradigm-induced BOLD responses, preprocessing, motion artifacts, and geometrical distortions. Interactions are also influenced by the proximity of brain regions. The influence of all these factors is taken into account and the nature of the interactions is studied using various experimental conditions such that the conclusions reached are robust with respect to variation of these factors. After defining nonlinear and/or nonsynchronous interaction models in the framework of general linear models, statistical tests are performed on different fMRI data sets to infer the nature of the interactions. Finally, a new connectivity metric is proposed which takes these inferences into account. We find that BOLD signal interactions are statistically more significant when taking into account the history of the distant signal, i.e., the signal from the interacting region, than when using a model of linear instantaneous interaction. Moreover, about 75% of the interactions are symmetric, as assessed with the proposed connectivity metric. The history-dependent part of the coupling between brain regions can explain a high percentage of the variance in the data sets studied. As these results are robust with respect to various confounding factors, this work suggests that models used to study the functional connectivity between brain areas should in general take the BOLD signal history into account.
相关性分析已广泛应用于基于功能磁共振成像（fMRI）数据的功能连接性研究中。它假定关于脑区相互作用的相关信息由同一时刻两个信号值之间的线性关系反映出来。然而，这一假设尚未得到充分研究。在这项工作中，我们深入研究了成对脑区的血氧水平依赖（BOLD）信号所共享的信息。特别是，我们评估了数据中存在的非线性和/或非同步相互作用的量。这是通过将反映线性、同步相互作用的模型与包含非线性、非同步相互作用的更一般模型进行对比测试来实现的。许多影响测量到的BOLD信号的因素对于连接性研究至关重要，例如范式诱导的BOLD反应、预处理、运动伪影和几何失真。相互作用也受到脑区接近程度的影响。我们考虑了所有这些因素的影响，并使用各种实验条件研究相互作用的性质，以使得出的结论对于这些因素的变化具有稳健性。在一般线性模型框架中定义非线性和/或非同步相互作用模型后，对不同的fMRI数据集进行统计测试，以推断相互作用的性质。最后，提出了一种新的连接性度量，该度量考虑了这些推断结果。我们发现，考虑远距离信号的历史，即来自相互作用区域的信号时，BOLD信号相互作用在统计学上比使用线性瞬时相互作用模型时更显著。此外，用所提出的连接性度量评估，约75%的相互作用是对称的。脑区之间耦合的历史依赖部分可以解释所研究数据集中的高比例方差。由于这些结果对于各种混杂因素具有稳健性，这项工作表明，用于研究脑区之间功能连接性的模型通常应考虑BOLD信号历史。
