Layer Dependence of Monocular and Binocular Responses in Human Ocular Dominance Columns at 7T using VASO and BOLD
Bibliographic record
Abstract
Abstract The neurons located in the striate cortex (V1) preferentially respond to the input from one eye or another, forming a fingerprint-like pattern of ocular dominance columns (ODCs). At this mesoscopic scale, accessible by ultra-high field fMRI, V1 is supplied/drained by a network of surface (pial) vessels that connect to descending/ascending tangential vessels that penetrate the cortex and supply/drain a capillary bed whose density is also layer dependent. In this study, we measured the layer dependence of monocular and binocular responses of ocular dominance columns in V1 at 7T using Blood Oxygenation Level Dependent (BOLD) and VAscular Space Occupancy (VASO) contrasts. Our results indicate that the microvascular blood volume changes that give rise to VASO are well confined to the site of neural activity across the layers of the cortex and between the columns. Pial veins dominate the BOLD response and mix the signal between columns. When the GRE BOLD response was examined in only the VASO specific voxels (thus eliminating the pial vein signal), the laminar profile was very similar to VASO, however, the columnar response was still blurred. Caution needs to be exercised in the interpretation of signal changes in BOLD at the mesoscale both in terms of feedforward/feedback effects and inhibitory and excitatory effects. Highlights - VASO produced laminar profiles that were consistent with the known layer-dependent neuronal response to monocular and binocular stimulations. - VASO better differentiated the response between columns belonging to the left and right eyes. - GRE BOLD signal spatial specificity was poor in both laminar and columnar directions, however, when the pial veins were suppressed, the laminar BOLD signal was very similar to the VASO signal. - Caution needs to be exercised when interpreting cognitive neuroscience BOLD studies at the mesoscale level due to the confounding effects of pial and sub-pial veins and venules.
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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".