Stabilities of negative correlations between blood oxygen level‐dependent signals associated with sensory and motor cortices
Bibliographic record
Abstract
Compared with positive correlations, negative correlations of blood oxygen level-dependent (BOLD) signals (NCOBSs) have been much less studied. In most related studies, the NCOBSs have been accepted as stable without further consideration. To investigate the stabilities of NCOBSs associated with the auditory, motor, and visual cortices, we evaluated the negative correlation maps of each brain region under different "task-backgrounds" within the same subject-group, as well as within different subject-groups during a conscious resting state. These "task-backgrounds" refer to tasks not expected to activate the specific ROI under consideration and are in some sense analogous to "resting states." We found that the negative correlation maps of the motor and visual cortices were quite variable between either different task-backgrounds or different subject-groups, whereas those of the auditory cortex exhibited some similarities. These results indicate that the NCOBSs associated with the motor and visual cortices were unstable both under task-backgrounds and during the conscious resting state. The auditory cortex tended to have stable NCOBSs during these "resting states" (but scanner noise could make the auditory cortex "less resting"). This study highlights the importance of paying attention to the influence of the stabilities of NCOBSs in related studies and establishes the need for further studies on NCOBSs.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".