White Matter Correlates of Spoken Discourse in Cerebrovascular Disease
Bibliographic record
Abstract
Abstract Background Spoken discourse (language beyond single words or sentences) performance can be used to detect cognitive impairment in cerebrovascular disease (CVD) [Roberts A, et al. (2021). Top Lang Disord 41(1):73‐98]. However, the neurological basis for altered spoken discourse in CVD is poorly defined. This study examined the association between spoken discourse and indicators of white matter microstructural integrity provided by diffusion tensor imaging (DTI) to better define the link between CVD‐related neurodegeneration and altered spoken discourse. Method Spoken discourse and 3T DTI data (30‐32 directions, b=1000) were obtained from the CVD cohort of the Ontario Neurodegenerative Disease Research Initiative (n=133). Spoken discourse analyses were completed previously [Roberts, 2021]. A DTI analysis pipeline [Hassan SMH, et al. (2019) PLoS One 14(12):e0226715] was used to generate brain maps of fractional anisotropy (FA) and mean diffusivity (MD) and calculate mean FA and MD values for the inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus – parietal (SLFp) and temporal (SLFt) endings, and uncinate fasciculi (UNC) in each hemisphere. Canonical correlation analyses examined associations between DTI metrics and 10 spoken discourse measures separately for FA in left hemisphere, MD in left hemisphere, FA in right hemisphere, MD in right hemisphere. Result Canonical correlations were significant in the left hemisphere (FA: r=0.47, p<0.05; MD: r=0.51, p<0.01) but not the right (FA: r=0.34, p=0.90; MD: r=0.40, p=0.15)(Figure 1). Among the white matter tracts, the strongest canonical loadings were seen for the SLFp (FA: 0.81; MD: ‐0.59) and SLFt (FA: 0.71; MD: ‐0.40) compared to the ILF (FA: 0.44; MD: 0.03) and UNC (FA: ‐0.34; MD: 0.10). Higher FA in the SLFp and SLFt was associated with better performance on measures of fluency and information content. Lower MD in these tracts was associated with better performance on measures of fluency, information content, and syntax. Conclusion Spoken discourse performance was associated with white matter microstructural integrity in the left hemisphere of the brain. Of the white matter tracts investigated in this study, impaired spoken discourse performance in CVD was most strongly linked to altered tissue microstructure in the parietal and temporal endings of the superior longitudinal fasciculus.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".