P3‐159: Predicting Alzheimer's‐related cognitive decline with correlational network changes
Bibliographic record
Abstract
Alzheimer's disease (AD) is characterized by a progressive decline in cognitive performance, as well as stereotypical patterns of brain atrophy, network degeneration, hypermetabolism, and neuropathology. Recent efforts have attempted to associate the spatiotemporal patterns of these disease markers in order to better establish the causal relationships between them. Here, we present a novel approach to associate AD-related degeneration with behavioural outcomes, using the large, multicentre ADNI database. We focus in particular on the network changes associated with cognitive impairment, as determined by correlative structural networks inferred from cortical thickness (CT) measurements. We obtained CT measurements from T1-weighted images using the Civet pipeline. Regions-of-interest (ROIs) were assigned using the Automated Anatomical Labeling atlas (AAL). To assess the relative association of (1.) vertex-wise CT and (2.) abnormality of pair-wise ROI correlations, with behavioural performance, we analyzed a general linear model using the “lasso” regression approach (GLMNET) to isolate the strongest predictors. We analyzed the Alzheimer's Disease Assessment Scale (ADAS-cog), along with nine additional behavioural outcome measures. The highest vertex-wise predictors for the ADAS-cog were found bilaterally in posterior cingulate, precuneus, medial temporal lobe, and lateral temporal and parietal lobes; and unilaterally in the right anterior cingulate. The network edges which most strongly predict ADAS-cog are predominantly antero-posterior and contralateral. We find a variety of different vertex- and edge-wise predictors for the other behavioural measures, which demonstrate the involvement of distinct subnetworks in the performance of these tasks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".