Cerebral small vessel disease in aging and <scp>A</scp>lzheimer's disease: a comparative study using <scp>MRI</scp> and <scp>SPECT</scp>
Bibliographic record
Abstract
BACKGROUND: White matter hyperintensities (WMH) are associated with aging and are prevalent in various brain pathologies. The purpose of the current study was to characterize WMH perfusion in age-matched elderly controls (ECs) and patients with Alzheimer's disease (ADs). METHODS: Fifty ECs (23 men) and 61 ADs (33 men) underwent magnetic resonance imaging (MRI), 99mTc-ECD single-photon emission computed tomography (SPECT) and cognitive testing. Brain tissue type was classified on T1 weighted images, and WMH were identified on interleaved proton density/T2 weighted images. Co-registered MR images were used to characterize SPECT perfusion patterns. RESULTS: WMH perfusion was lower than normal appearing white matter (NAWM) perfusion (P < 0.001) in both EC and AD groups. There was no WMH perfusion difference between groups when considering the mean perfusion from all WMH voxels (P > 0.43). However, locations that were likely to be considered WMH tended to have lower perfusion in ADs compared with ECs. Perfusion gradients along watershed white matter regions were significantly different between EC and AD groups (P < 0.05). A relationship was found between the volume of a WMH lesion and its mean perfusion (P < 0.001) in both ECs and ADs. CONCLUSION: Global WMH were hypoperfused compared with NAWM to the same degree in EC and AD participants, which suggests a common WMH etiology between groups. However, white matter locations that were likely to contain WMH tended to be hypoperfused in ADs compared with healthy aging. This finding is suggestive of AD-specific pathology that reduces the perfusion at anatomic locations susceptible to the formation of WMH through either the neurodegenerative process or AD-related vasculopathy or both.
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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 | 0.001 |
| 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 | 0.000 |
| 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".