Diffusion MRI subnetwork efficiency is associated with cognitive resilience to AD pathology in cognitively unimpaired older adults at risk of AD dementia
Bibliographic record
Abstract
Abstract Background While higher levels of Aβ and tau pathology are typically associated with more pronounced cognitive decline in Alzheimer’s disease (AD), some older adults exhibit normal cognition even with a substantial burden of AD pathology. This phenomenon is known as ‘cognitive resilience’. The potential role of structural connectivity in cognitive resilience remains inconclusive. The present study tested whether higher global efficiency in structural brain networks moderate the relationship between AD pathology and cognitive performance in the preclinical phase of AD. Method We studied 118 cognitively normal older adults from the family history enriched Prevent‐AD cohort. Diffusion‐weighted MRI was used to measure the structural connectome, PET to measure global Aβ (18F‐NAV4694) and entorhinal tau (18F‐Flortaucipir) pathology, and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) to evaluate cognitive performance. We first used graph theory analyses to calculate global efficiency of networks known to be affected in early AD (i.e., limbic and default mode network (DMN)). Then multiple linear regressions were performed to examine relationships between Aβ / tau pathology, cognitive performance, and global efficiency of the two subnetworks. Interaction analyses were used to examine whether network efficiency attenuates the relationships between AD pathology and cognitive performance. Result As expected, higher levels of Aβ and tau pathology in the brain were associated with worse cognitive performance in individuals at risk of AD (Figure 1). Higher entorhinal tau pathology was also associated with lower global efficiency in the limbic network (Figure 2). Subsequent interaction analyses showed that global efficiency in the limbic moderated the relationships between AD pathology and cognitive performance. Specifically, higher global efficiency was associated with an attenuated effect of both amyloid and tau pathology on delayed memory performance (Figure 3). Conclusion Individuals with higher global efficiency in the limbic structural network exhibit better memory performance at a given level of AD pathology in preclinical stages of the disease. Structural network properties of the brain may play an important role in maintaining cognitive performance in the face of AD pathology, and could serve as a potential biomarker for cognitive resilience.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".