Cortical morphology changes in default mode network regions as predictors of cognitive decline in relation to amyloid and tau deposits
Bibliographic record
Abstract
Alzheimer's disease can be classified based on amyloid, tau and neurodegeneration status. The Default Mode Network is notably vulnerable to these processes, making early structural alterations in this network of particular interest for identifying prodromal biomarkers. In this longitudinal cross-sectional study, we analysed data from 279 participants in the Alzheimer's Disease Neuroimaging Initiative (mean age = 73.7 ± 9 years, 53.2% males). Structural measures-sulcal depth, gyrification and cortical thickness-were extracted for all Default Mode Network regions. Their ability to predict memory performance (encoding, retrieval and recall) was tested at baseline and 2-year follow-up by means of multiple linear regression models, which were all corrected for the risk of multiple comparisons. Covariates included Mini Mental State Examination scores, amyloid status and regional tau burden, to examine interactions with structural changes. Our results showed distinct Default Mode Network alteration patterns based on tau burden and amyloid status, highlighting patterns of morphological features with different susceptibility to proteinopathy. In individuals with concordant (both positive or both negative) amyloid and tau status, preserved structural integrity and complexity were linked to better cognitive performance and appeared protective against decline. However, mainly negative associations were instead observed in individuals with discordant amyloid or tau status (i.e. positive for only either amyloid or tau accumulation). We discuss these findings as a possible reflection of a mismatch between abnormal protein accumulation and structural damage in these populations. The multimodal nature of this study helps clarifying the heterogeneous findings reported in existing literature regarding structural integrity and cognitive outcomes in Alzheimer's disease.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".