Gray matter regions statistically mediating the cross‐sectional association of eotaxin and set‐shifting among older adults with major depressive disorder
Bibliographic record
Abstract
OBJECTIVE: Eotaxin is a chemokine that exerts negative effects on neurogenesis. We recently showed that peripheral eotaxin levels correlate with both lower gray matter volume and poorer executive performance in older adults with major depressive disorder. These findings suggest that the relationship between eotaxin and set-shifting may be accounted for by lower gray matter volume in specific regions. Prior studies have identified specific gray matter regions that correlate with set-shifting performance, but have not examined whether these specific gray matter regions mediate the cross-sectional association between eotaxin and set-shifting. METHOD: In 27 older adults (mean age: 68 ± 5.2 years) with major depressive disorder, we performed a whole brain (voxel-wise) analysis testing whether/where gray matter density statistically mediates the cross-sectional association of eotaxin and set-shifting performance. RESULTS: We found the association between eotaxin and set-shifting performance was fully statistically mediated by lower gray matter density in left middle cingulate, right pre-/post-central, lingual, inferior/superior frontal, cuneus, and middle temporal regions. CONCLUSION: The regions identified above may be both susceptible to a potential neurodegenerative effect of eotaxin, and critical to preserving set-shifting function. Longitudinal and intervention studies are needed to further evaluate whether targeting eotaxin levels will prevent neurodegeneration and executive impairment in older adults with depression. Copyright © 2016 John Wiley & Sons, Ltd.
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 | 0.000 | 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) | 0.002 | 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".