Reassessing the Functional Significance of BOLD Variability
Bibliographic record
Abstract
Abstract BOLD variability (SD BOLD ) has emerged as a unique measure of the adaptive properties of neural systems that facilitate fast, stable responding, based on claims that SD BOLD is independent of mean BOLD signal (mean BOLD ) and a powerful predictor of behavioural performance. We challenge these two claims. First, the apparent independence of SD BOLD and mean BOLD may reflect the presence of deactivations; we hypothesize that while SD BOLD may not be related to raw mean BOLD it will be linearly related to absolute mean BOLD . Second, the observed relationship between SD BOLD and performance may be an artifact of using fixed-length trials longer than response times. Such designs provide opportunities to toggle between on- and off-task states, and fast responders likely engage in more frequent state-switching, thereby artificially elevating SD BOLD . We hypothesize that SD BOLD will be higher and more strongly related to performance when using such fixed-length trials relative to self-paced trials that terminate upon a response. We test these two hypotheses in an fMRI study using blocks of fixed-length or self-paced trials. Results confirmed both hypotheses: (1) SD BOLD was robustly related with absolute mean BOLD ; and (2) toggling between on- and off-task states during fixed-length trials reliably contributed to SD BOLD . Together, these findings suggest that a reappraisal of the functional significance of SD BOLD as a unique marker of cognitive performance is warranted.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".