Abstract TMP112: Evidence of Diffuse Cortical Vascular Dysfunction in Patients With White Matter Hyperintensities
Bibliographic record
Abstract
Introduction: White matter hyper intensities (WMHs) on T2 FLAIR images, are associated with cognitive decline in older populations. WMHs exhibit diminished vascular reactivity compared to normal appearing white matter (Sam et al. AJNR 2016). We hypothesize that patients with WMHs have diminished cortical vascular reactivity compared to controls. Methods: 23 patients with WMHs and 21 controls were studied. Patient inclusion criteria were: no cortical infarcts, Fazekas Grade >= 2, no vascular stenoses >= 70%, and no history of cardio/pulmonary disease. MRI protocol and vasodilatory stimulus The 3T MRI protocol included a 1 mm isotopic T1 3D sequence and a 3.5 mm isotropic T2* BOLD sequence (30 msec echo time). Cerebrovascular reactivity ( CVR ) was measured using 2 step changes in end-tidal pCO2 (from 40 to 50mmHg) using a controlled gas delivery system (RespirAct TM ) during BOLD imaging. Recent work has shown an age invariant cortical response to this stimulus delivery method (McKetton et al Neuroimage 2018). Image Analysis CVR magnitude maps, expressed in %BOLD/mmHg, were generated by calculating the linear regression of the BOLD response with the pCO2. The BOLD response was fitted with an exponential function in order to measure its time characteristic TAU providing a measure of speed of vascular response. Maps were corrected for partial volume errors. (Figure 1A). Average CVR and TAU within frontal, occipital, parietal and temporal grey matter were calculated for all subjects. t-tests corrected for multiple comparisons were calculated between patients and controls. Results: CVR magnitude was lower and TAU longer in patients with WMHs compared to normal controls in all four lobes (p < 0.05) (Figure 1B). Conclusions: Patients with moderate to severe WMHs show diffuse impairment of vascular reactivity affecting the cerebral cortex even in the absence of significant large vessel steno-occlusive disease and in the absence of visible cortical disease on MRI.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".