Abstract T MP118: Microinfarct Disruption of Cerebral White Matter: A Longitudinal Diffusion Tractography Analysis
Bibliographic record
Abstract
Introduction: Cerebral microinfarcts (CMI) are associated with cognitive decline in clinico-pathological studies. Acute CMI can be detected by diffusion-weighted imaging (DWI). We prospectively evaluated the effect of incidental CMI on white matter (WM) ultrastructure using longitudinal diffusion tensor imaging (DTI)-based tractography. Methods: Nine incidental DWI lesions were identified in six subjects (all males, age 67±9 years, mean pre-to-post lesional scan interval 19±4 months). All patients were diagnosed with probable cerebral amyloid angiopathy and underwent at least three MRIs as part of a prospective study. Silent DWI lesions were observed on the middle scan, enabling longitudinal analysis. Control regions-of-interest (ROIs) were generated in the contralateral hemisphere using semi-automated coregistration, and the lesion/control ROIs were coregistered to the pre- and post-lesional scans. DTI parameters [fractional anisotropy (FA); mean diffusivity (MD)] were measured within each ROI, along a short-segment of WM fiber tracts (within 6mm of the ROI), and along the entire tract. For the lesional scan, we compared DTI parameters between lesion and control ROIs. For the longitudinal analysis, we compared the ratio of lesion-to-control FA and MD at the pre-lesional and post-lesional scans. Results: On the lesional scan, FA within the lesion ROI was significantly lower than in the control ROI (0.28±0.13 vs. 0.40±0.20, p=0.04) and MD was non-significantly reduced in the lesion ROI versus the control ROI (p=0.09). A significant decline within lesion ROI in FA ratio (1.22±0.45 vs. 0.91±0.439, p=0.04) and an increase in MD ratio (0.96±0.14 vs. 1.25±0.37, p=0.02) were observed between the pre-lesional and post-lesional scans. There was no difference in FA ratio or MD ratio for the short segment or entire tracts at the time of the lesion and in the longitudinal analysis. Conclusion: We demonstrate persistent microstructural alterations of WM caused by incidental DWI lesions. Although these alterations do not extend outside the lesional ROI to associated fiber tracts, their accumulation over time may explain their association with cognitive decline.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".