Resting-State fMRI and the Risk of Overinterpretation: Noise, Mechanisms, and a Missing Rosetta Stone

Resting-state fMRI is widely used to describe spontaneous neural activity via correlation-based synchronization measures, yet it faces two fundamental obstacles: pervasive non-neural noise and the absence of a definitive "Rosetta Stone" linking the measured BOLD signals to underlying neural events. Although "correlation does not imply causation" has become a cliche, leveraging correlations effectively--and understanding their inferential pitfalls--remains a nuanced challenge. Correlation-based analyses are typically not able to yield causal conclusions, yet they are frequently used to underpin causal narratives in neuroscience research and especially in clinical contexts, which represent a problematic case of overinterpretation. Using causal inference reasoning, simulations and analytic methods, we address three critical questions when performing resting-state fMRI: (1) How reliable are correlation estimates for capturing cross-regional synchrony? (2) What are the consequences of inaccuracies in estimated correlations? (3) To what extent do estimated correlations reflect causal neural interactions? We identify two principal pitfalls. First, correlation estimates are systematically distorted by diverse noise sources, including variability in neurovascular coupling, which can generate spurious, suppressed, or even reversed effects. Second, graph-based approaches lack causal interpretability, meaning that even large samples and strong statistical evidence may obscure fundamental ambiguities in what the correlations represent. In light of these challenges, we advocate for three priorities: (1) cautious interpretation that avoids causal overreach, (2) multimodal validation to cross-check findings against independent measures, and (3) enhanced methodological rigor, particularly in biomarker discovery and clinical trials, to ensure that resting-state fMRI provides meaningful insights.