Influence of BOLD Contributions to Diffusion fMRI Activation of the Visual Cortex
Bibliographic record
Abstract
Reliance on the hemodynamic response as a surrogate marker of neural activity imposes an intrinsic limit on the spatial specificity of functional MRI. An alternative approach based on diffusion-weighted functional MRI (DfMRI) has been reported as a contrast less reliant on hemodynamic effects, however current evidence suggests that both hemodynamic and unique neural sources contribute to the diffusion signal. Here we compare activation patterns obtained with the standard blood oxygenation level-dependent (BOLD) contrast to DfMRI in order to gain a deeper understanding of how the BOLD proportion contributes to the observable diffusion signal. Both individual and group-level activation patterns obtained with DfMRI and BOLD to a visual field stimulation paradigm were analyzed. At the individual level, the DfMRI contrast showed a strong, positive relationship between the volumes of cortex activated in response to quadrant- and hemi-field visual stimulation. This was not observed in the corresponding BOLD experiment. Overall, the DfMRI response indicated less between-subject variability, with random effects analyses demonstrating higher statistical values at the peak voxel for DfMRI. Furthermore, the spatial extent of the activation was more restricted to the primary visual region for DfMRI than BOLD. However, the diffusion signal was sensitive to the hemodynamic response in a manner dependent on experimental manipulation. It was also limited by its low signal-to-noise ratio (SNR), demonstrating lower sensitivity than BOLD. Together these findings both support DfMRI as a contrast that bears a closer spatial relationship to the underlying neural activity than BOLD, and raise important caveats regarding its utilization. Models explaining the DfMRI signal change need to consider the dynamic vascular contributions that may vary with neural activity.
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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".