Dissociable Effects of Alzheimer Disease and White Matter Hyperintensities on Brain Metabolism
Bibliographic record
Abstract
IMPORTANCE: Cerebrovascular disease and Alzheimer disease (AD) frequently co-occur and seem to act through different pathways in producing dementia. OBJECTIVE: To examine cerebrovascular disease and AD markers in relation to brain glucose metabolism in patients with mild cognitive impairment. DESIGN AND SETTING: Cohort study among the Alzheimer Disease Neuroimaging Initiative clinical sites in the United States and Canada. PARTICIPANTS: Two hundred three patients having amnestic mild cognitive impairment (74 of whom converted to AD) with serial imaging during a 3-year follow-up period. MAIN OUTCOMES AND MEASURES: Quantified white matter hyperintensities (WMHs) represented cerebrovascular disease, and cerebrospinal fluid β-amyloid represented AD pathology. Brain glucose metabolism in temporoparietal and frontal brain regions was measured using positron emission tomography with fluorodeoxyglucose F18. RESULTS: In converters, greater WMHs were associated with decreased frontal metabolism (-0.048; 95% CI, -0.067 to -0.029) but not temporoparietal metabolism (0.010; 95% CI, -0.010 to 0.030). Greater cerebrospinal fluid β-amyloid (per 10-pg/mL increase) was associated with increased temporoparietal metabolism (0.005; 95% CI, 0.000-0.010) but not frontal metabolism (0.002; 95% CI, -0.004 to 0.007) in the same patients. In nonconverters, similar relationships were observed except for a positive association of greater WMHs with increased temporoparietal metabolism (0.051; 95% CI, 0.027-0.076). CONCLUSIONS AND RELEVANCE: The dissociation of WMHs and cerebrospinal fluid β-amyloid in relation to regional glucose metabolism suggests that these pathologic conditions operate through different and independent pathways in AD that reflect dysfunction in different brain systems. The positive association of greater WMHs with temporoparietal metabolism suggests that these pathologic processes do not co-occur in nonconverters.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".