Effect of CSF biomarkers on cortical thickness in males and females
Bibliographic record
Abstract
Abstract Background The level of elevated Tau, phosphorylated Tau, and aggregated Amyloid β in the cerebrospinal fluid (CSF) are markers of Alzheimer’s pathology in the brain. In this study, we analyzed how these CSF biomarkers are related to the cortical thickness in males and females, for subjects who are cognitively normal (CN), with mild cognitive impairment (MCI), and diagnosed as Alzheimer’s disease (AD). Method We studied the CN (305), MCI (264) and AD (230) subjects from the ADNI dataset at baseline. We first studied the CSF biomarker level differences between males and females in all three groups. The effect of age was controlled using general linear models (GLM) We also looked at the effect of the CSF biomarkers on cortical thickness maps generated separately for males and females for all three groups, also with GLM‐based covariate regression to control the effect of age, ICV, and field strength of the scan. Result For the CSF biomarker levels, we observed Tau to be significantly higher in females in the AD group. No other significant sex difference in the biomarker levels were observed. We found MCI males [Figure 1] and AD [Figure 2] females showing a negative relationship between Tau and thickness. When comparing CSF pTau levels to cortical thickness, a negative correlation was found only in males [Figure 3] for the AD group. When comparing Aβ and cortical thickness, negative correlations were found for males in the CN [Figure 4] and AD group [Figure 5], and for females [Figure 6] in the MCI group. Conclusion The results suggest that the relationship between cortical thickness and CSF biomarkers is different in males and females in CN, MCI, and AD groups. We found that there is a negative relationship between CSF biomarkers and cortical thickness.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".