Functional dynamic network connectivity differentiates biological patterns in the Alzheimer's disease continuum
Bibliographic record
Abstract
Alzheimer's disease (AD) can be conceptualized as a network-based syndrome. Network alterations are linked to the molecular hallmarks of AD, involving amyloid-beta and tau accumulation, and consecutively neurodegeneration. By combining molecular and resting-state functional magnetic resonance imaging, we assessed whether different biological patterns of AD identified through a data-driven approach matched specific abnormalities in brain dynamic connectivity. We identified three main patient clusters. The first group displayed mild pathological alterations. The second cluster exhibited typical behavioral impairment alongside AD pathology. The third cluster demonstrated similar behavioral impairment but with a divergent tau (low) and neurodegeneration (high) profile. Univariate and multivariate analyses revealed two connectivity patterns encompassing the default mode network and the occipito-temporal cortex, linked respectively with typical and atypical patterns. These results support the key association between macro-scale and molecular alterations. Dynamic connectivity markers can assist in identifying patients with AD-like clinical profiles but with different underlying pathologies. Within the clinical continuum of Alzheimer's disease (AD), we identified two main groups: one characterized by typical AD neuropathological changes, and the other by atypical pathophysiological mechanisms. Univariate and multivariate analyses revealed two dynamic functional connectivity patterns, involving the default mode network and the occipito-temporal cortex, respectively. • The study assessed the dynamic functional connectivity profile linked with different ATN patterns. • We identified an atypical ATN cluster showing specific dynamic functional connectivity patterns. • Dynamic connectivity showed an accuracy of 87 % in discriminating between biological AD clusters.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".