Evidence suggesting common mechanisms underlie contralateral and ipsilateral negative BOLD responses in the human visual cortex
Bibliographic record
Abstract
The task-evoked positive BOLD response (PBR) to a unilateral visual hemi-field stimulation is often accompanied by robust and sustained contralateral as well as ipsilateral negative BOLD responses (NBRs) in the visual cortex. The signal characteristics and the neural and/or vascular mechanisms that underlie these two types of NBRs are not completely understood. In this paper, we investigated the properties of these two types of NBRs. We first demonstrated the linearity of both NBRs with respect to stimulus duration. Next, we showed that the hemodynamic response functions (HRFs) of the two NBRs were similar to each other, but significantly different from that of the PBR. Moreover, the subject-wise expressions of the two NBRs were tightly coupled to the degree that the correlation between the two NBRs was significantly higher than the correlation between each NBR and the PBR. However, the activation patterns of the two NBRs did not show a high level of interhemispheric spatial similarity, and the functional connectivity between them was not different than the interhemispheric functional connectivity between the NBRs and PBR. Finally, while attention did modulate both NBRs, the attention-related changes in their HRFs were similar. Our findings suggest that the two NBRs might be generated through common neural and/or vascular mechanisms involving distal/deep brain regions that project to the two hemispheres.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".