P1‐304: Changes in MRI cortical thickness and [18F]FDG PET data over 24 months in subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) Study
Bibliographic record
Abstract
Alzheimer's disease (AD)-related changes in cortical thickness and cerebral glucose metabolism are invaluable imaging biomarkers for elucidating the natural disease progression and response to disease-modifying therapeutic intervention. In order to improve our understanding of the complex relationship between structural and functional changes in the cerebral cortex in subjects with variable degrees of cognitive performance impairment, we performed a fully-automated image analysis of longitudinal anatomical MRI and [18F]FDG PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. T1-weighted MRI and [18F]FDG PET images were obtained from ADNI study subjects (healthy elders, MCI, AD) that completed baseline, 12 month, and 24 month scans. MRI-defined cortical thickness measures and standardized uptake value ratio (SUVR) for FDG PET with pons as the reference region were processed using the fully-automated PIANO™ pipeline. All MRI and FDG data were registered to a customized nonlinear template in MNI stereotaxic space. Vertex-wise and ROI-based statistical analysis was performed on the cortical thickness and surface-projected FDG SUVR data. Cortical thinning and cerebral glucose hypometabolism demonstrated different temporal and spatial patterns. The MCI and AD subjects showed progressive regional cortical thinning in medial and lateral temporal lobe (anterior> posterior), while the AD subjects also had thinning in posterior lateral parietal lobe (Year 1 >Year 2) and frontal lobe (Year 1< Year 2). Atrophy-corrected FDG SUVR measures revealed glucose hypometabolism in the posterior cingulate cortex, posterior temporal and parietal cortex, and precuneus in the MCI (Year 1 < Year 2) and AD subjects, while the AD subjects also showed frontal lobe hypometabolism (Year 1 >Year 2). Left-right hemispheric asymmetry in progression was evident in both cortical thickness and FDG SUVR data. MRI-derived cortical thickness and FDG PET-derived glucose metabolism measures provide complementary information regarding the natural progression of AD. Based on our 24-month data, glucose hypometabolism appears to precede cortical thinning in particular regions (e.g. posterior temporal and parietal lobes), while the two measures are dissociated in other regions (e.g. precuneus). Our observations of regional left-right asymmetries and nonlinear trajectories of thinning and hypometabolism may be valuable for disease staging/prognosis and monitoring response to therapeutic agents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".