Open-label study of the short-term effects of memantine on FDG-PET in frontotemporal dementia
Bibliographic record
Abstract
BACKGROUND: Memantine has shown effects on cortical metabolism in Alzheimer's disease (AD), and the mechanism of action may not be specific to AD alone. We hypothesized that participants with frontotemporal dementia taking memantine would show an increased cortical metabolic activity in frontal regions, temporal regions, or in salience network hubs. METHODS: Sixteen participants with behavioral or language variant frontotemporal dementia syndromes (FTD) were recruited from tertiary FTD clinics and treated with memantine hydrochloride 10 mg twice daily in this fixed-dose, open-label pilot study. The primary endpoint was enhancement of cortical metabolic activity after 7-8 weeks of treatment. Secondary endpoints were measures of mood and behavior disturbance, frontal executive function, and motor disturbance. RESULTS: Voxel-wise parametric image analysis of positron emission tomography (PET) data from seven behavioral variant FTD patients, eight semantic dementia patients, and one progressive nonfluent aphasia patient, of mean age 64.3 years, mean duration of illness 4.25 years, and baseline mean sum of boxes Clinical Dementia Rating score 6.59, revealed an increase in [(18)F]-fluorodeoxyglucose (FDG) normalized metabolic activity in bilateral insulae and the left orbitofrontal cortex (P < 0.01). The increase on FDG-PET did not correlate with changes on behavioral inventories. Post hoc analysis indicated that semantic dementia participants drove this finding. CONCLUSION: This open-label clinical PET study suggests that memantine induces an increase in metabolism in the salience network in FTD. A placebo-controlled follow-up study is warranted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".