White Matter Damage in the Semantic Variant of Primary Progressive Aphasia
Bibliographic record
Abstract
BACKGROUND: The semantic variant of primary progressive aphasia (svPPA) is a form of dementia, mainly featuring language impairment, for which the extent of white matter (WM) damage is less described than its associated grey matter (GM) atrophy. Our study aimed to characterise the extent of this damage using a sensitive and unbiased approach. METHODS: We conducted a between-group study comparing 10 patients with a clinical diagnosis of svPPA, recruited between 2011 and 2014 at a tertiary reference centre, with 9 cognitively healthy, age-matched controls. From diffusion tensor imaging (DTI) data, we extracted fractional anisotropy (FA) values using a tract-based spatial statistics approach. We further obtained GM volumetric data using the Freesurfer automated segmentation tool. We compared both groups using non-parametric Wilcoxon rank-sum tests, correcting for multiple comparisons. RESULTS: Demographic data showed that patients and controls were comparable. As expected, clinical data showed lower results in svPPA than controls on cognitive screening tests. Tractography showed impaired diffusion in svPPA patients, with FA mostly decreased in the longitudinal, uncinate, cingulum and external capsule fasciculi. Volumetric data show significant atrophy in svPPA patients, mostly in the left entorhinal, amygdala, inferior temporal, middle temporal, superior temporal and temporal pole cortices, and bilateral fusiform gyri. CONCLUSIONS: This syndrome appears to be associated not only with GM but also significant WM degeneration. Thus, DTI could play a role in the differential diagnosis of atypical dementia by specifying WM damage specific to svPPA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".