Spatial specificity of the functional gradient echo and spin echo BOLD signal across cortical depth at 7 T
Bibliographic record
Abstract
Abstract Functional magnetic resonance imaging (fMRI) at high magnetic field strengths (≥ 7 T) is a promising technique to study the functioning of the human brain at the spatial scale of cortical columns and layers. However, measurements most often rely on the blood oxygenation level dependent (BOLD) response sampled with a gradient echo (GE) sequence, which is known to be most sensitive to macrovascular contributions that limit their effective spatial resolution. Alternatively, a spin echo (SE) sequence can be used to increase the weighting toward the microvasculature and, therefore, the location of neural activation. In addition, due to the heterogeneous structure of the cortical cerebrovascular system, the effective spatial resolution can change across cortical depth. For high-resolution fMRI applications, it is hence important to know how much the effective spatial resolution varies across cortical depth. In this study, we used flickering rotating wedge stimuli to induce traveling waves with varying spatial frequencies in the retinotopically organized primary visual cortex (V1), which allowed us to infer the modulation transfer function (MTF) of the BOLD response that characterizes the spatial specificity of the measured signal. We acquired GE- and SE-BOLD data at 7 T and compared the MTF between acquisition techniques at different cortical depths. Our results show a small but consistent increase in spatial specificity when using SE-BOLD. But across cortical depth, both acquisition techniques generally show a similar decrease of specificity toward the pial surface demonstrating the dependence on macrovascular contributions, which needs to be carefully considered when interpreting the results of high-resolution fMRI studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".