P1‐022: Characterizing abnormal white matter structure in primary progressive aphasia
Bibliographic record
Abstract
Primary progressive aphasia (PPA) shows early selective language impairment, and relative sparing of other cognitive domains. Cortical atrophy is noted in the left perisylvian language areas, though different PPA subtypes may have additional areas involved. To date, little work has examined the integrity of white matter tracts in PPA, but there is some suggestion that the arcuate region of the left superior longitudinal fasciculus (SLF) may be involved. The present study uses diffusion tensor imaging (DTI) and tract-based spatial statistics (TBSS)5 to examine differences in fractional anisotropy (FA), radial (DR), and axial diffusivity (DA) between a group of PPA patients and healthy controls. 10 patients with PPA (3 non-fluent, 7 semantic dementia) were recruited from two clinical sites in Toronto. 17 healthy controls were matched to patients for age and education. Diffusion weighted images were collected on a 3.0T GE Signa scanner with 23 directions and two repetitions. Analysis was carried out using TBSS within the FSL package. FA, DA, and DR images were calculated for each individual. Images were transformed into MNI space using the FMRIB58 FA template and nonlinear registration. FA images were skeletonised to define a search space for voxel-wise comparisons. 2-sample t-tests compared FA, DR, and DA between groups, with randomise for non-parametric estimation and threshold-free cluster enhancement for multiple-comparisons correction. FA values were higher in controls across a number of territories, including left SLF, bilateral inferior longitudinal fasciculi (ILF) and uncinate fasciculi (UF). Patients showed higher DR overlapping these changes in FA, while DA was not reduced in patients. DA was higher in patients than controls within voxels contributing to the left UF and forceps major. Results suggest white matter disruption is widespread in PPA, but includes left-hemisphere SLF/arcuate involvement. This disruption is marked by an increase in radial diffusivity without loss of axial diffusivity and may reflect demyelination in multiple tracts. Increased DA and DR in left UF may represent a unique degenerative process in this tract which could relate to loss of semantic memory in PPA. Future work will examine differences between PPA subtypes and clinical correlates of these findings.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".