Associations of Resting-State fMRI Functional Connectivity with Flow-BOLD Coupling and Regional Vasculature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There has been tremendous interest in applying functional magnetic resonance imaging-based resting-state functional connectivity (rs-fcMRI) measurements to the study of brain function. However, a lack of understanding of the physiological mechanisms of rs-fcMRI limits their ability to interpret rs-fcMRI findings. In this work, the authors examine the regional associations between rs-fcMRI estimates and dynamic coupling between the blood oxygenation level-dependent (BOLD) and cerebral blood flow (CBF), as well as resting macrovascular volume. Resting-state BOLD and CBF data were simultaneously acquired using a dual-echo pseudocontinuous arterial spin labeling (pCASL) technique, whereas macrovascular volume fraction was estimated using time-of-flight MR angiography. Functional connectivity within well-known functional networks—including the default mode, frontoparietal, and primary sensory-motor networks—was calculated using a conventional seed-based correlation approach. They found the functional connectivity strength to be significantly correlated with the regional increase in CBF-BOLD coupling strength and inversely proportional to macrovascular volume fraction. These relationships were consistently observed within all functional networks considered. Their findings suggest that highly connected networks observed using rs-fcMRI are not likely to be mediated by common vascular drainage linking distal cortical areas. Instead, high BOLD functional connectivity is more likely to reflect tighter neurovascular connections, attributable to neuronal pathways.
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.
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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it