Van De Ville Dimitri, Liégeois Raphaël
Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.
Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
Imaging Neurosci (Camb). 2024 Nov 19;2. doi: 10.1162/imag_a_00364. eCollection 2024.
Resting-state fMRI has spurred an impressive amount of methods development, among which dynamic functional connectivity (dFC) is one important branch. However, the relevance of time-varying and time-resolved features has led to debate, to which we want to bring in our viewpoint. We argue that, while statistically many dFC features extracted from resting state are contained within a sufficiently strong null model, these features can still reflect underlying neuronal activity. The use of naturalistic experimental paradigms, at the boundary between resting state and task, is essential to validate their relevance. In parallel, leveraging methods that specifically rely on sparsity is an avenue to strengthen the statistical significance of time-resolved information carried by ongoing brain activity.
静息态功能磁共振成像（fMRI）推动了大量方法的发展，其中动态功能连接（dFC）是一个重要分支。然而，时变和时间分辨特征的相关性引发了争论，对此我们想提出自己的观点。我们认为，虽然从静息状态提取的许多dFC特征在统计学上包含在一个足够强大的零模型中，但这些特征仍然可以反映潜在的神经元活动。在静息状态和任务之间的边界处使用自然主义实验范式对于验证它们的相关性至关重要。同时，利用特别依赖稀疏性的方法是加强由持续脑活动携带的时间分辨信息的统计显著性的一条途径。
