P4‐296: LONGITUDINAL RELATIONSHIPS BETWEEN HIPPOCAMPAL VOLUME AND CORTICAL METABOLISM IN AMNESTIC MILD COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Hippocampal volume (HV) and fluorodeoxyglucose positron emission tomography (FDG-PET) have been proposed as biomarkers representing neurodegeneration and cortical metabolism, respectively, in Alzheimer's diseases (AD). Precision in the temporal staging of these biomarkers remains lacking. Here we propose that they be staged based on the causal influences of their comingling trajectories over time. We further examine how these trajectories are affected by comorbid small vessel disease, and their relationships with cerebrospinal fluid (CSF) biomarkers (i.e. amyloid-beta 42 [Aβ42] and phospho-tau [p-tau] concentrations). A latent growth curve model with two parallel processes was used to examine the longitudinal relationships between HV and FDG-PET in 525 participants of the Alzheimer's Disease Neuroimaging Initiative with mild cognitive impairment (MCI). The intercepts and slopes of the HV and FDG-PET (averaged signal from angular, inferior/middle temporal, and posterior cingulate gyri) trajectories were modelled as latent variables using their observed measures at three timepoints (baseline, 12 months, and 24 months). The influences of each intercept on each slope were assessed simultaneously, controlling for correlations between intercepts and slopes, and for relevant covariates, including baseline CSF Aβ42 and p-tau concentrations, and white matter hyperintensity (WMH) volumes. A lower FDG-PET signal at the intercept predicted a more negative HV slope (β=0.191, p=0.028) but the reciprocal influence was not found (β=−0.001, p=0.994; Figure 1). Lower CSF Aβ42 and higher CSF p-tau concentrations predicted a lower FDG-PET signal at the intercept (β=0.034, p<0.001; β=−1.053, p<0.001) and a more negative FDG-PET slope (β=0.068, p=0.021; β=−3.594, p=0.017). Higher CSF p-tau predicted a more negative HV slope (β=−1.469, p=0.001) but CSF Aβ42 did not influence HV parameters. Larger WMH volumes predicted a lower FDG-PET intercept (β=−0.083, p=0.006), and a more negative HV slope (β=−0.085, p=0.013).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".