Spared speech fluency is associated with increased functional connectivity in the speech production network in semantic variant primary progressive aphasia
Bibliographic record
Abstract
Semantic variant primary progressive aphasia is a clinical syndrome characterized by marked semantic deficits, anterior temporal lobe atrophy and reduced connectivity within a distributed set of regions belonging to the functional network associated with semantic processing. However, to fully depict the clinical signature of semantic variant primary progressive aphasia, it is necessary to also characterize preserved neural networks and linguistic abilities, such as those subserving speech production. In this case-control observational study, we employed whole-brain seed-based connectivity on task-free MRI data of 32 semantic variant primary progressive aphasia patients and 46 healthy controls to investigate the functional connectivity of the speech production network and its relationship with the underlying grey matter. We investigated brain-behaviour correlations with speech fluency measures collected through clinical tests (verbal agility) and connected speech (speech rate and articulation rate). As a control network, we also investigated functional connectivity within the affected semantic network. Patients presented with increased connectivity in the speech production network between left inferior frontal and supramarginal regions, independent of underlying grey matter volume. In semantic variant primary progressive aphasia patients, preserved (verbal agility) and increased (articulation rate) speech fluency measures correlated with increased connectivity between inferior frontal and supramarginal regions. As expected, patients demonstrated decreased functional connectivity in the semantic network (dependent on the underlying grey matter atrophy) associated with average nouns' age of acquisition during connected speech. Collectively, these results provide a compelling model for studying compensation mechanisms in response to disease that might inform the design of future rehabilitation strategies in semantic variant primary progressive aphasia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".