Atrophy Pattern Maps of Frontotemporal Dementia variants (bvFTD, svPPA, pnfaPPA)
Bibliographic record
Abstract
The files contain voxel-wise t-statistics maps contrasting deformation based morphometry (DBM) measurements of frontotemporal dementia (FTD) patients, divided according to subtype diagnosis, against matched normal controls. BV = behavioral-variant Frontotemporal Dementia SV = semantic-variant Primary Progressive Aphasia PNFA = non-fluent-variant Primary Progressive Aphasia FTD map is based on NIFD data, available at: https://memory.ucsf.edu/research/studies/nifd For more information regarding the participants and method details, see: Dadar, Mahsa, et al. "White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer's disease and frontotemporal dementia." Neurobiology of aging 111 (2022): 54-63. Metz, Amelie, et al. "Brain Atrophy and White Matter Hyperintensities in Frontotemporal Dementia Variants and their Impact on Cognition" [Conference presentation] CCNA 2024 Partners Forum and Science Days (2024, March 19-21).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.032 |
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".