Longitudinal gray matter contraction in three variants of primary progressive aphasia: A tenser-based morphometry study
Bibliographic record
Abstract
The present study investigated the pattern of longitudinal changes in cognition and anatomy in three variants of primary progressive aphasia (PPA). Eight patients with the non-fluent variant of PPA (nfvPPA), 13 patients with the semantic variant (svPPA), seven patients with the logopenic variant (lvPPA), and 29 age-matched, neurologically healthy controls were included in the study. All participants underwent longitudinal MRI, neuropsychological and language testing at baseline and at a 1-year follow-up. Tenser-based morphometry (TBM) was applied to T1-weighted MRI images in order to map the progression of gray and white matter atrophy over a 1-year period. Results showed that each patient group was characterized by a specific pattern of cognitive and anatomical changes. Specifically, nfvPPA patients showed gray matter atrophy progression in the left frontal and subcortical areas as well as a decline in motor speech and executive functions; svPPA patients presented atrophy progression in the medial and lateral temporal lobe and decline in semantic memory abilities; and lvPPA patients showed atrophy progression in lateral/posterior temporal and medial parietal regions with a decline in memory, sentence repetition and calculations. In addition, in all three variants, the white matter fibers underlying the abovementioned cortical areas underwent significant volume contraction over a 1-year period. Overall, these results indicate that the three PPA variants present distinct patterns of neuroanatomical contraction, which reflect their clinical and cognitive progression.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".