Linking Electrophysiological Metrics to Oxidative Metabolism: Implications for EEG–fMRI Association
Bibliographic record
Abstract
Abstract Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study brain function, yet its biophysiological basis remains incompletely understood. Building on our recent work, we investigated how EEG activity and cerebral metabolic rate of oxygen (CMRO 2 ) are related to one another, and how they jointly underpin rs-fMRI metrics. Using a multimodal dataset with macrovascular correction applied to all rs-fMRI metrics, we first examined associations between EEG metrics and CMRO 2 , then applied mediation analysis to evaluate how CMRO 2 mediates EEG–fMRI associations. We found that bandlimited EEG theta and alphafractional power was significantly associated with CMRO 2 . Bandlimited EEG coherence was also associated with CMRO 2 across all the bands. Bandlimited EEG fractional power and coherence were also significantly associated with cerebral blood flow (CBF) and oxygen extraction fraction (OEF) in a manner that varied by frequency. EEG broadband temporal complexity was positively associated with CMRO₂ and EEG coherence was negatively associated with OEF. Notably, there are pronounced sex differences in these relationships, which suggests that the biophysical underpinnings of rs-fMRI are sex dependent. Moreover, the baseline metabolic and hemodynamic variables did partially mediate EEG–fMRI associations, with CMRO 2 serving as the primary mediator. However, most of the mediations are partial, highlighting the complex interplay among electrophysiological activity, oxidative metabolism, and hemodynamics. This study advances our understanding of the biophysical basis of rs-fMRI and provides a foundation for developing sex-specific diagnostic and therapeutic strategies for neurological disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".