Early detection of dementia with default-mode network effective connectivity
Bibliographic record
Abstract
Abstract Altered functional connectivity precedes structural brain changes and symptoms in dementia. Alzheimer’s disease is the largest contributor to dementia at the population level, and disrupts functional connectivity in the brain’s default-mode network (DMN). We investigated whether a neurobiological model of DMN effective connectivity could predict a future dementia diagnosis at the single-participant level. We applied spectral dynamic causal modeling to resting-state functional magnetic resonance imaging data in a nested case–control group from the UK Biobank, including 81 undiagnosed individuals who developed dementia up to nine years after imaging, and 1,030 matched controls. Dysconnectivity predicted both future dementia incidence (AUC = 0.82) and time to diagnosis ( R = 0.53), outperforming models based on brain structure and functional connectivity. We also evaluated associations between DMN dysconnectivity and major risk factors for dementia, revealing strong relationships with polygenic risk for Alzheimer’s disease and social isolation. Neurobiological models of effective connectivity may facilitate early detection of dementia at population level, supporting rational deployment of targeted dementia-prevention strategies.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 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".