BOLD & Non-BOLD Contrasts in Human fMRI
Bibliographic record
Abstract
This lecture was given as a part of the Weekend Educational Course on 'Advances in fMRI' at the 2023 Meeting of the International Society for Magnetic Resonance in Medicine. The video lecture is restricted to the attendees of the 2023 Meeting of the International Society for Magnetic Resonance in Medicine. Talk Synopsis: fMRI is a non-invasive method that allows scientists to study brain function during tasks or at rest. The BOLD contrast is the workhorse of functional neuroimaging. A cascade of physiological events following neuronal activity (changes in blood oxygenation, flow, and volume) culminates in the BOLD signal. The versatility of MRI enables imaging of blood flow and volume using techniques such as Arterial Spin Labeling (ASL) and Vascular Space Occupancy (VASO) respectively. In this talk, we will learn about BOLD and non-BOLD contrasts (CBF, CBV), discuss what they offer, and how they differ in their application to human fMRI.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.012 |
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".