Associations between iron and mean kurtosis in iron-rich grey matter nuclei in aging
Bibliographic record
Abstract
Elevated kurtosis values have been observed in subcortical grey matter structures of patients with neurodegenerative diseases. Here, we examined relationships between iron measures and kurtosis in iron-rich subcortical grey matter structures.Please check and confirm the affiliation 4 for the author "Xiaoping P. Hu".Affiliation 4 for Xiaoping P. Hu was incorrect since he is not associated with that department. We have removed this affiliation. Thanks! Multi-shell diffusion and multi-echo gradient echo acquisitions were used to derive mean kurtosis and iron measures ( R 2 * and magnetic susceptibility), respectively, in subcortical grey matter nuclei and white matter tracts in a discovery cohort (110 healthy older and 63 younger adults) and replication cohort (72 healthy older adults).Please confirm if the author names are presented accurately and in the correct sequence (Ilana J. Bennett and Xiaoping P. Hu). Also, kindly confirm the details in the metadata are correct.Thanks for asking. We've checked and the names are presented accurately and in the correct sequence. We have corrected some details in the metadata - adding an affiliation to Murphy Shao (Department of Physics) and removing the Materials Science and Engineering affiliation for Xiaoping P. Hu. Is it possible to add an orcid id for Vala Masjedizadeh? His Orcid ID is 0009-0009-3692-2553 Everything else looks okay Iron-rich grey matter regions exhibited higher mean kurtosis, R 2 *, and magnetic susceptibility and white matter regions had lower mean kurtosis in the older adult group from the discovery cohort. In both cohorts, mean kurtosis was significantly correlated with R 2 * and magnetic susceptibility in iron-rich grey matter nuclei. No association was seen between signal-to-noise ratio and mean kurtosis in any grey matter region, indicating that the increase in mean kurtosis was not due to reduced signal-to-noise. As keywords are mandatory for this journal, please provide 3-6 keywords.I'm not sure where to put the keywords so I'll reply to the query with the keywords. Our keywords are: kurtosis, iron, grey matter, aging Our findings indicate that kurtosis is associated with iron-sensitive metrics in iron-rich grey matter structures, suggesting that iron deposits may be contributing to kurtosis.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".