Medial Temporal Hypoperfusion and Aggression in Alzheimer Disease
Bibliographic record
Abstract
BACKGROUND: It is not understood why some patients with Alzheimer disease (AD) display aggression and others do not. OBJECTIVE: To examine the relation between regional brain perfusion and aggression in AD. DESIGN: Single-photon emission computed tomographic scans were coregistered to a standardized template in Talairach space, generating mean ratios of uptake referenced to the cerebellum. PARTICIPANTS: Forty-nine outpatients (25 men and 24 women; mean +/- SD age, 74 +/- 11 years) with probable AD (Mini-Mental State Examination score, 17.7 +/- 5.0; 30 aggressive and 19 nonaggressive), comparable in age, sex, and severity of cognitive impairment. MAIN OUTCOME MEASURES: Regional perfusion ratios were determined for 5 bilateral regions of interest: orbitofrontal, middle medial temporal, inferior medial temporal, hypothalamus/thalamus, and anterior cingulate. RESULTS: Compared with nonaggressive patients, aggressive ones displayed hypoperfusion in the right and left middle medial temporal regions of interest (P = .02 for both), but not the others (all (t tests, unpaired, 2-tailed). On regression analyses, right middle temporal hypoperfusion (P = .001), younger age (P = .002), greater activity disturbances (P = .004), and higher Mini-Mental State Examination scores (P = .04) independently predicted aggression, accounting for 44% of the total variance (F = 8.7; P<.001). Statistical parametric mapping analyses supported right middle medial temporal hypoperfusion in the aggressive group (P = .008). CONCLUSION: In this sample of patients with AD, the right middle medial temporal region emerged as an important neural correlate of aggression.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".