Perivascular Space Predicts Brain Hypometabolism of Individuals with Underlying Amyloid Pathology
Bibliographic record
Abstract
BACKGROUND: Reduced signal on fluorodeoxyglucose-positron emission tomography (FDG-PET) is a valid proxy for neurodegeneration in Alzheimer's disease (AD). Perivascular space (PVS) is believed to be associated with AD pathology and cognitive decline. OBJECTIVE: This study aimed to investigate the associations of PVS with FDG-PET and cognitive performance based on the burden of amyloid pathology. METHODS: We used magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). MRI-visible PVS in basal ganglia (BG) and centrum semi-oval (CSO) were visually classified as: none/mild, moderate or frequent/severe. The association of PVS with brain FDG-PET was explored based on the burden of amyloid pathology, where a cerebrospinal fluid (CSF) t-tau/Aβ42 with the ratio≥0.27 was defined as high amyloid pathology. Moreover, the relationships between PVS and cognitive performance variables (ADNI-MEM and ADNI-EF) were studied. RESULTS: For participants with higher tau/Aβ42 ratio, CSO-PVS severity was independently associated with lower FDG-PET. There were significant interaction effects between moderate or frequent/severe CSO-PVS and time on FDG decline in people with high amyloid pathology. The interaction between CSO-PVS and time (follow-up) was consistently associated with ADNI-MEM and ADNI-EF decline in individuals with high amyloid pathology. CONCLUSION: The study established the differential utility of PVS in BG and CSO for predicting brain metabolism. These findings suggest that CSO-PVS serves as a contributing factor to brain metabolism and cognitive decline associated with amyloid pathology.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".