Influence of systemic low-frequency oscillations on fMRI identifiability -Implications for denoising and fingerprinting
Bibliographic record
Abstract
Systemic low-frequency oscillations (sLFOs) can significantly influence blood oxygen level-dependent (BOLD) signals in functional magnetic resonance imaging (fMRI), confounding neural activity assessments. This study evaluates the impact of sLFO correction on functional connectivity-based identifiability using data from the Human Connectome Project. We compare global signal regression (GSR) and partial global signal regression (pGSR), which consists in removing the portion of the global signal explained by peripheral recordings (cardiac and breathing), in their ability to isolate sLFOs while preserving neural low-frequency oscillations (nLFOs). Our results show that GSR achieves significantly higher identifiability than the null model and pGSR suggesting improved sLFO removal based on a previous report that identifiability is primarily driven by the neural component as opposed to the measurable noise components. Our findings support the use of GSR as an effective sLFOs denoising strategy in fMRI fingerprinting and connectivity studies. In situations where less aggressive denoising is required, we propose pGSR as an alternative to GSR whenever the partial global signal can be estimated accurately from the peripheral recordings.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".