Statistical maps of cerebral blood flow deficits in Alzheimer’s disease
Bibliographic record
Abstract
Images of cerebral blood flow or metabolism are useful as adjunct to the differential diagnosis of cortical dementia. The aim of this study was to create statistical objective voxel maps of significant differences in regional cerebral blood flow between patients with Alzheimer's disease and age-matched healthy volunteers. Maps of significantly reduced cerebral blood flow were created based on a spatially normalized distribution of cerebral blood flow, measured with O-15-water and positron emission tomography in 16 Alzheimer's patients, compared to 16 healthy age-matched volunteers. After spatial normalization of voxel counts, the t-statistic of the cerebral blood flow deficit was determined from the local voxel-SDs. In the patients, significant reduction (P < 0.05) of the flow distribution was present in regions near the hippocampus, extending rostrally to the temporo-parietal region in both hemispheres, including the medial parietal cortex plus smaller frontal areas. The maximum reduction occurred in the left tapetum/hippocampus (53%, P = 0.061). In conclusion, statistical maps of cerebral blood flow deficits objectively reveal the location of deficits, identifying areas that are difficult to identify by subjective visual inspection of conventional sections of cerebral blood flow maps. This is particularly well illustrated by the pronounced flow reduction of the medial parietal cortices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".