Abstract WP442: Diffusion Tensor Imaging of White Matter Tracts in Transient Ischemic Attack Patients
Bibliographic record
Abstract
Introduction: Transient ischemic attack (TIA) greatly increases the risk of developing dementia later in life. Measurement of microstructural changes in white matter (WM) using diffusion tensor imaging (DTI) tractography has potential for identifying those at greatest risk of cognitive decline. We hypothesized that patients presenting with TIA have abnormal DTI measures in major WM tracts. Our objective was to determine changes in DTI measures for fractional anisotropy (FA) and medial diffusivity (MD) in frontoparietal (Superior Longitudinal Fasciculus; SF) and medial temporal (Uncinate Fasciculus; UF) WM tracts for TIA patients and healthy controls. Methods: Patients presenting with symptoms of high risk TIA but free of dementia, and healthy volunteer controls were recruited acutely. Structural MRI, inclusive of DTI sequences (31 directions b1000) was performed. FA and MD values were collected in the left and right SLF and UF. Multiple linear regression was performed to determine predictors of DTI values (FA and MD), adjusted for age. Subject’s cognition was screened using Montreal Cognitive Assessment (MoCA). Results: Data from 60 TIA patients (mean age = 68.75 years, SD = 9.39, 45% female) and 33 healthy controls (mean age = 64.97 years, SD = 10.45, 67% female) was analyzed. Linear regression identified that TIA patients have higher FA values in left SLF, F(2, 90) = 9.210, p < .001, R 2 = .170); right SLF, F(2, 90) = 7.154, p = .001, R 2 = .137; and left UF, F(2, 90) = 3.513, p = .034, R 2 = .072, compared to healthy controls. No group changes in MD were observed when corrected for age. TIA patients (median score 24, (interquartile range (IQR) = 5) performed worse than healthy controls (median score = 27, IQR = 4) on the MoCA while controlling for age, F(1, 90) = 7.689, p < .007. Conclusion: TIA patients showed changes in FA of WM tracts related to language and memory function when compared to healthy controls, supporting the presence of incipient microstructural disease. Our results show that measures of microstructural changes using DTI may help identify TIA patients at a greater risk of developing cognitive impairment. Future work aims to identify deterioration of DTI measures over time and their relation to potential vascular and neurodegenerative etiologies.
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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 | 0.001 |
| 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) | 0.005 | 0.001 |
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".