Whole‐brain white matter disruption in semantic and nonfluent variants of primary progressive aphasia
Bibliographic record
Abstract
Semantic (svPPA) and nonfluent (nfPPA) variants of primary progressive aphasia are associated with distinct patterns of cortical atrophy and underlying pathology. Little is known, however, about their contrasting spread of white matter disruption and how this relates to grey matter (GM) loss. We undertook a structural MRI study to investigate this relationship. We used diffusion tensor imaging, tract-based spatial statistics, and voxel-based morphometry to examine fractional anisotropy (FA) and directional diffusivities in nine patients with svPPA and nine patients with nfPPA, and compared them to 16 matched controls after accounting for global GM atrophy. Significant differences in topography of white matter changes were found, with more ventral involvement in svPPA patients and more widespread frontal involvement in nfPPA individuals. However, each group had both ventral and dorsal tract changes, and both showed spread of diffusion abnormalities beyond sites of local atrophy. There was a clear dissociation in sensitivity of diffusion tensor imaging measures between groups. SvPPA patients showed widespread changes in FA and radial diffusivity, whereas changes in axial diffusivity were more restricted and proximal to sites of GM atrophy. NfPPA patients showed isolated changes in FA, but widespread axial and radial diffusivity changes. These findings reveal the extent of white matter disruption in these variants of PPA after accounting for GM loss. Further, they suggest that differences in the relative sensitivity of diffusion metrics may reflect differences in the nature of underlying white matter pathology in these two subtypes.
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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 | 0.001 |
| 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".