Frontal Variant of Alzheimer Disease Differentiated From Frontotemporal Dementia Using in Vivo Amyloid and Tau Imaging
Bibliographic record
Abstract
The frontal variant of Alzheimer disease (fvAD) is characterized by behavioral and/or dysexecutive impairments that can resemble those of behavioral-variant frontotemporal dementia (bvFTD). This overlap, in addition to the lack of consensus clinical criteria for fvAD, complicates its identification. We provide the first case report of fvAD differentiated in vivo from bvFTD using amyloid-beta and tau PET imaging. The patient, a right-handed woman, presented with forgetfulness at age 60. Cognitive testing at that time revealed mild impairments in memory, attention, and executive functions. Three years later, her family reported that she was displaying socially inappropriate behaviors, inertia, diminished social interest, and altered food preferences-the sum of which met the criteria for possible bvFTD. PET using an amyloid-beta tracer (F-AZD4694) identified diffuse amyloid plaques across the cerebral cortex. PET using a tau tracer specific for neurofibrillary tangles (F-MK6240) identified substantial tau pathology in the brain's frontal lobes. Together with the clinical findings, these images supported the diagnosis of fvAD rather than bvFTD. Considering past and emerging evidence that tau topography in Alzheimer disease (AD) matches the clinical features of AD, we discuss the potential utility of in vivo tau imaging using F-MK6240 for identifying fvAD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".