Frӧmer et al. (2024 , Nature Human Behaviour) apply a deconvolution method to correct for component overlap in the event-related potential. They report that this method eliminates signatures of sensory evidence accumulation from response-aligned measurements of the centro-parietal positivity (CPP), suggesting that these signatures arise artifactually. Here, we argue that the analysis and interpretation of their perceptual choice data are critically flawed. We demonstrate with simulations that the deconvolution analyses used by the authors are not designed to reliably test for the presence or absence of bounded accumulation signals.

In principle, dynamic functional connectivity in fMRI is just a statistical measure. A passer-by might think it to be a specialist topic, but it continues to attract widespread attention and spark controversy. Why?

While dynamic functional connectivity remains controversial in human neuroimaging, the transient nature of interareal coupling is considered a robust finding in other fields of neuroscience. Nevertheless, the origin and interpretation of these dynamics are still under debate. This letter argues that ongoing cognition is not sufficient to account for dynamic functional connectivity. Instead, it is proposed that the baseline state of the brain is inherently unstable, leading to dynamics that are of neural origin but not directly implicated in cognition. This perspective also reinforces the usefulness of conducting experiments during the resting state.

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.