White Matter Correlates of Speech in Cerebrovascular Disease
Bibliographic record
Abstract
Abstract Background Analyses of spoken discourse (speech beyond single words or sentences) can detect cognitive impairment in cerebrovascular disease (CVD) [Roberts, 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 visible white matter damage detected using structural magnetic resonance imaging (MRI) and potential early‐stage damage in normal‐appearing white matter (NAWM) detected using diffusion tensor imaging (DTI) to better define the link between CVD‐related neurodegeneration and altered speech. Method Spoken discourse and 3T MRI data were obtained from the CVD cohort of the Ontario Neurodegenerative Disease Research Initiative (n = 127). Spoken discourse analyses were completed previously [Roberts et al., 2021]. White matter hyperintensities (WMH) and the NAWM portions of the inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus – parietal (SLFp) and temporal (SLFt) endings, and uncinate fasciculus (UNC) were segmented in each hemisphere (Figure 1). Mean values of fractional anisotropy (FA) and mean diffusivity (MD) within NAWM, and normalized WMH volume were calculated for each of the white matter tracts. Three canonical correlation analyses examined associations between 10 spoken discourse measures and the imaging metrics (WMH volume, FA, MD) in each tract. Result Canonical correlations were significant for FA (rc = 0.51, p<0.05) and MD (rc = 0.56, p<0.05) but not WMH volume (rc = 0.42, p = 0.69). Among the white matter tracts, the strongest canonical loadings were seen for FA and MD in the left SLFp (FA: 0.80; MD: ‐0.61) and left SLFt (FA: 0.60; MD: ‐0.40) (Figure 2). Higher FA and lower MD in the left SLFp and left SLFt were associated with fluency, information content, and syntax performance. Conclusion Spoken discourse performance was associated with microstructural integrity of NAWM but not visible white matter damage. These results suggest that DTI metrics provide a more relevant assessment of white matter tract function in relation to speech than a volumetric measure of tract lesion load. Of the white matter tracts investigated in this study, spoken discourse performance in CVD was most strongly linked to white matter microstructure in the parietal and temporal endings of the left superior longitudinal fasciculus.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".