The link between steady-state EEG and rs-fMRI metrics in healthy young adults: The effect of macrovascular correction
Bibliographic record
Abstract
To improve the clinical utility of resting-state fMRI (rs-fMRI), enhancing its interpretability is paramount. Establishing links with electrophysiological activities remains the benchmark for understanding the neuronal basis of rs-fMRI signals. Existing research, while informative, suffers from inconsistencies and a limited scope of rs-fMRI metrics (e.g., seed-based functional connectivity). Phenotypic variables like sex and age are suspected to obscure reliable fMRI-electroencephalography (EEG) associations. A major contributing factor to these inconsistencies may be the neglect of macrovascular correction in rs-fMRI metrics. Given that macrovascular contributions can inflate rs-fMRI connectivity and power, they may lead to misleading fMRI-EEG associations that do not reflect genuine neuronal underpinnings. In this study, we addressed this by applying macrovascular correction and performing a systematic, inter-participant analysis of multiple rs-fMRI and EEG metrics. Our key findings are: (1) macrovascular correction enhances the relationship between EEG and rs-fMRI metrics and improves model fit in many instances; (2) sex significantly modulates EEG-fMRI associations; and (3) EEG complexity is significantly associated with resting-state functional activity (RSFA). This research provides crucial insights into the interplay between rs-fMRI and EEG, ultimately improving the interpretability of rs-fMRI measurements and building upon our prior work linking fMRI and metabolism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".