IC‐06‐04: ANTEMORTEM LONGITUDINAL MRI METRICS AS A BIOMARKER OF POSTMORTEM BRAAK NFT STAGING
Bibliographic record
Abstract
The diagnosis of Alzheimer's disease (AD) can only be established through postmortem examination. The most widely used assessment method is “ABC staging”, involving the neuropathological gradation of diffuse amyloid plaques, neurofibrillary tangles (NFT), and neuritic plaques. NFT pathology is the strongest correlate of atrophy on structural magnetic resonance imaging (MRI) amongst all AD pathological features. Conversely, our goal was to identify regional MRI metrics for which longitudinal trajectories were the strongest predictors of postmortem neurofibrillary degeneration. We selected participants from three databases (ADNI, NACC and Rush Memory and Aging Project) providing up to 10 years of clinical and MRI longitudinal follow-up, as well as postmortem neuropathological data. After initial quality control, 104 subjects with at least two in vivo brain MRIs were retained, for a total of 402 scans. Bilateral surfaces, thicknesses and volumes from cortical and subcortical brain structures were extracted using FreeSurfer 5.3 (longitudinal processing pipeline). Yearly atrophy rates were then calculated for each of the resulting 232 measures. Nonparametric comparisons and correlation analyses using Kruskal-Wallis and Spearman's rank correlation tests were performed to screen for the best predictors of postmortem NFT staging as assessed by Braak score. There was a significant difference between Braak transentorhinal (I-II), limbic (III-IV) and isocortical (V-VI) stages for 75 radiological variables (p < .05). Most of these entities are part of the temporal lobe and ventricular system; were also present some limbic lobe structures such as the posterior cingulate cortex. When adjusted for multiple comparisons, only the annual atrophy rates of the left inferior temporal and right middle temporal volumes remained significantly different between Braak stages (p < .0002); these structures showed good correlations with the severity of NFT aggregation (-0.463 and -0.406, respectively). Trajectories of regional brain atrophy as detected by serial in vivo brain imaging reflect underlying severity and distribution of AD-associated neurofibrillary degeneration. In vivo MRI metrics may therefore be considered as a potential biomarker for the prediction of AD neuropathological staging in the living brain.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".