Recent analysis methods can capture nonlinear interactions between brain regions. However, noise sources might induce spurious nonlinear relationships between the responses in different regions. Previous research has demonstrated that traditional denoising techniques effectively remove noise-induced linear relationships between brain areas, but it is unknown whether these techniques can remove spurious nonlinear relationships. To address this question, we analyzed fMRI responses while participants watched the film Forrest Gump. We tested whether nonlinear Multivariate Pattern Dependence Networks (MVPN) outperform linear MVPN in non-denoised data, and whether this difference is reduced after CompCor denoising. Whereas nonlinear MVPN outperformed linear MVPN in the non-denoised data, denoising removed these nonlinear interactions. We replicated our results using different neural network architectures as the bases of MVPN, different activation functions (ReLU and sigmoid), different dimensionality reduction techniques for CompCor (PCA and ICA), and multiple datasets, demonstrating that CompCor’s ability to remove nonlinear interactions is robust across these analysis choices and across different groups of participants. Finally, we asked whether information contributing to the removal of nonlinear interactions is localized to specific anatomical regions of no interest or to specific principal components. We denoised the data 8 separate times by regressing out 5 principal components extracted from combined white matter (WM) and cerebrospinal fluid (CSF), each of the 5 components separately, 5 components extracted from WM only, and 5 components extracted solely from CSF. In all cases, denoising was sufficient to remove the observed nonlinear interactions.

最近的分析方法可以捕获大脑区域之间的非线性相互作用。然而，噪声源可能会在不同区域的响应之间引起虚假的非线性关系。先前的研究表明，传统的去噪技术可以有效地去除噪声引起的大脑区域之间的线性关系，但尚不清楚这些技术是否可以去除虚假的非线性关系。为了解决这个问题，我们在参与者观看电影《阿甘正传》时分析了 fMRI 反应. 我们测试了非线性多变量模式依赖网络 (MVPN) 在非去噪数据中是否优于线性 MVPN，以及这种差异在 CompCor 去噪后是否有所减少。虽然非线性 MVPN 在非去噪数据中优于线性 MVPN，但去噪消除了这些非线性相互作用。我们使用不同的神经网络架构作为 MVPN 的基础、不同的激活函数（ReLU 和 sigmoid）、CompCor 的不同降维技术（PCA 和 ICA）以及多个数据集来复制我们的结果，证明 CompCor 去除非线性交互的能力是强大的跨越这些分析选择和不同的参与者群体。最后，我们询问有助于消除非线性相互作用的信息是否局限于不感兴趣的特定解剖区域或特定的主要成分。我们通过回归从组合的白质 (WM) 和脑脊液 (CSF) 中提取的 5 个主要成分、5 个成分中的每一个、仅从 WM 中提取的 5 个成分和仅从 CSF 中提取的 5 个成分，分别对数据进行了 8 次去噪。在所有情况下，去噪足以消除观察到的非线性相互作用。