Orbitofrontal Cortex and Olfactory Bulb Volume Predict Distinct Aspects of Olfactory Performance in Healthy Subjects
Bibliographic record
Abstract
While recent studies suggest an important role of higher order olfactory brain areas for basic olfactory performance, the extent to which cortical and peripheral neural markers account for separate portions of the variability in olfactory perceptual acuity is still unclear. We addressed this question by correlating voxel-based morphometry data from 90 healthy adults with olfactory performance measures. Supplementing this approach with region of interest (ROI) analyses of functionally defined olfactory cortical regions and olfactory bulb volume, we sought to disentangle the relative contribution of central and peripheral areas to behavioral variability. Whole-brain analyses revealed a significant positive correlation of gray matter volume and olfactory function scores in the right orbital sulcus. This effect was confirmed by the ROI analyses, which further indicated a significant association of the olfactory score with olfactory bulb volume. Moreover, a functional dissociation was observed, with central and peripheral mechanisms explaining different aspects of the observed behavioral variance in the olfactory subscores. In line with previous clinical studies, these data thus suggest an important role of regional gray matter volume in the right orbitofrontal cortex and olfactory bulb volume for olfactory performance in healthy individuals.
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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".