Resting-State fMRI and the Risk of Overinterpretation: Noise, Mechanisms, and a Missing Rosetta Stone
Bibliographic record
Abstract
Abstract Resting-state fMRI has generated influential insights into large-scale brain organization and contributed to clinically relevant applications, largely through correlation-based measures of cross-regional association in BOLD responses. At the same time, interpreting these statistical associations as reflecting underlying neural interactions requires careful consideration of fundamental methodological constraints. Here, we distinguish two fundamental but often conflated limitations. The first is measurement distortion : the fMRI signal is an indirect and heterogeneous measurement of neural activity, arising from neurovascular coupling, physiology, and measurement-related processes, which can introduce systematic and incompletely characterized biases into estimated correlations. The second is causal non-identifiability : even if correlations perfectly reflected neural synchrony, the resulting correlation structure would not uniquely determine the underlying neural interactions. Using causal reasoning, simulations, and analytic arguments, we distinguish these limitations and examine their consequences for interpretation. Although both apply broadly to fMRI, their implications are particularly important in resting-state analyses, where correlation structure is the primary object of inference in the absence of experimental perturbation. We show that measurement-related biases can distort estimated correlations (e.g., attenuating or in-flating associations and affecting group comparisons) and can produce reproducible patterns that do not necessarily reflect underlying neural relationships, highlighting that statistical reliability does not guarantee biological validity. We further show that graph-theoretic, geometric, and other higher-order representations derived from these correlations do not, by themselves, justify mechanistic interpretation. We argue not against the utility of resting-state fMRI, but for greater precision in interpretation. Our conclusions concern the interpretation of correlation-based analyses rather than their methodological utility. Distinguishing measurement distortion from causal non-identifiability clarifies the inferential boundaries separating descriptive association, predictive utility, and mechanistic interpretation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.209 | 0.498 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.031 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".