Frontal White Matter Tracts Sustaining Speech Production in Primary Progressive Aphasia
Bibliographic record
Abstract
In primary progressive aphasia (PPA), speech and language difficulties are caused by neurodegeneration of specific brain networks. In the nonfluent/agrammatic variant (nfvPPA), motor speech and grammatical deficits are associated with atrophy in a left fronto-insular-striatal network previously implicated in speech production. In vivo dissection of the crossing white matter (WM) tracts within this "speech production network" is complex and has rarely been performed in health or in PPA. We hypothesized that damage to these tracts would be specific to nfvPPA and would correlate with differential aspects of the patients' fluency abilities. We prospectively studied 25 PPA and 21 healthy individuals who underwent extensive cognitive testing and 3 T MRI. Using residual bootstrap Q-ball probabilistic tractography on high angular resolution diffusion-weighted imaging (HARDI), we reconstructed pathways connecting posterior inferior frontal, inferior premotor, insula, supplementary motor area (SMA) complex, striatum, and standard ventral and dorsal language pathways. We extracted tract-specific diffusion tensor imaging (DTI) metrics to assess changes across PPA variants and perform brain-behavioral correlations. Significant WM changes in the left intrafrontal and frontostriatal pathways were found in nfvPPA, but not in the semantic or logopenic variants. Correlations between tract-specific DTI metrics with cognitive scores confirmed the specific involvement of this anterior-dorsal network in fluency and suggested a preferential role of a posterior premotor-SMA pathway in motor speech. This study shows that left WM pathways connecting the speech production network are selectively damaged in nfvPPA and suggests that different tracts within this system are involved in subcomponents of fluency. These findings emphasize the emerging role of diffusion imaging in the differential diagnosis of neurodegenerative diseases.
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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".