Subcortical Hyperintensities in Alzheimer’s Disease: No Clear Relationship with Executive Function and Frontal Perfusion on SPECT
Bibliographic record
Abstract
BACKGROUND/AIMS: To investigate relationships between subcortical hyperintensities (SH), frontal perfusion and executive function (EF) in a sample of Alzheimer's disease (AD) patients with varying severities of SH. METHODS: A sample of 63 AD patients underwent brain imaging with magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) scans. Severity of SH was assessed using a standardized visual rating scale on MRI. Patients were classified into severe (n=20), moderate (n=23) or no SH (n=20) groups. Four frontal SPECT regions of interest (anterior cingulate cortex, dorsolateral prefrontal cortex) and neuropsychological assessment of EF were analyzed. RESULTS: Overall, no significant relationships were found between severity of SH and measures of SPECT perfusion or EF, except for one subsection of the Dementia Rating Scale, with severe SH scoring slightly worse than the other two groups. CONCLUSION: These findings support previous studies which suggest minimal adverse effects of SH on brain function and cognition. Global severity of SH on MRI in AD was not associated with decline in frontal perfusion and only mildly related to a decline in a specific EF task. More accurate measures of SH volume, not just global severity of SH, may be necessary to capture such complex brain behavior relationships; if they do indeed exist.
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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 | 0.001 | 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) | 0.002 | 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".