Neuroanatomical Correlates of Macrolinguistic Aspects in Narrative Discourse in Unilateral Left and Right Hemisphere Stroke: A Voxel-Based Morphometry Study
Bibliographic record
Abstract
Background A growing body of literature has demonstrated the importance of discourse assessment in patients who suffered from brain injury, both in the left and right hemispheres, as discourse represents a key component of functional communication. However, little is known about the relationship between gray matter density and macrolinguistic processing. Purpose This study aimed to investigate this relationship in a group of participants with middle–low to low socioeconomic status. Method Twenty adults with unilateral left hemisphere ( n = 10) or right hemisphere ( n = 10) chronic ischemic stroke and 10 matched (age, education, and socioeconomic status) healthy controls produced three oral narratives based on sequential scenes. Voxel-based morphometry analysis was conducted using structural magnetic resonance imaging. Results Compared to healthy controls, the left hemisphere group showed cohesion impairments, whereas the right hemisphere group showed impairments in coherence and in producing macropropositions. Cohesion positively correlated with gray matter density in the right primary sensory area (PSA)/precentral gyrus and the pars opercularis. Coherence, narrativity, and index of lexical informativeness were positively associated with the left PSA/insula and the superior temporal gyrus. Macropropositions were mostly related to the left PSA/insula and superior temporal gyrus, left cingulate, and right primary motor area/insula. Discussion Overall, the present results suggest that both hemispheres are implicated in macrolinguistic processes in narrative discourse. Further studies including larger samples and with various socioeconomic status should be conducted. Supplemental Material https://doi.org/10.23641/asha.14347550
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".