Network efficiency predicts resilience to cognitive decline in elderly at risk for Alzheimer’s
Bibliographic record
Abstract
Abstract To determine whether white matter network efficiency (WMNE) may be a surrogate marker of the physiological basis of resilience to cognitive decline in elderly persons without dementia and age and AD-related cerebral pathology, we quantified WMNE from baseline MRI scans and investigated its association with longitudinal neuropsychological assessments independent of baseline amyloid, tau and white matter hyperintensity volume. 85 cognitively normal elderly subjects and patients with mild cognitive impairment (MCI) with baseline diffusion imaging, CSF specimens, AV45-PET and longitudinal cognitive assessments were included. WMNE was calculated from reconstructed cerebral white matter networks for each individual. Mixed linear effects models were estimated to investigate the association of higher resilience to cognitive decline with higher WMNE and the modulation of this association by increased cerebral amyloid, CSF tau or WMHV. For the majority of cognitive outcome measures, higher WMNE was associated with higher resilience to cognitive decline independently of pathology measures (beta: .074 – .098; p: .011 – .039). Additionally, WMNE was consistently associated with higher resilience to cognitive decline in subjects with higher cerebral amyloid burden (beta: .024 – .276; p: .000 – .036) and with lower CSF tau (beta: −.030 – −.074; p: .015 – .002) across all cognitive outcome measures. The results of this study indicate that WMNE in particular and possibly white matter organization in general may be worthy targets of investigation to provide measures quantifying a patient’s resilience to cognitive decline and thus provide an individual prognosis.
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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 | 0.004 |
| 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 | 0.001 |
| 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".