Abstract P157: Tractography of White Matter Connections Predicts for Vascular Cognitive Impairment in Hypertensive Patients
Bibliographic record
Abstract
Vascular cognitive impairment (VCI) results by several vascular risk factors and, particularly, hypertension (HTN). The identification of early changes associated with later development of dementia is demanding. Great part of research has primarily focused on brain changes occuring in grey matter. However, more recent data highlighted that HTN may determine cognitive decline, even before manifest neurodegeneration. Diffusion tensor imaging (DTI) on magnetic resonance, opened the possibility to predict white matter connections that correlate with specific cognitive functions. In this study, we used DTI and cognitive assessment (CA), in order to identify a regional pattern of fractional anisotropy (FA) changes that could predict for VCI in hypertensive patients (HT). We have examined 15 HT (moderate to severe, with antihypertensive medications) vs 15 normotensive (NT), subjecting them to DTI and CA. HT had significant higher SBP (138±4 vs 118±3 in NT) and DBP (87±2 vs 75±2 in NT) (p<0.001), displayed a significant LV hypertrophic remodeling (LVM/BSA 112±5 vs 83±3 for NT) (p<0.0001), with a significant moderate increase in albuminuria (15.7±2.6 mg/24h vs 8.8±1.6 for NT) (p<0.03). When subjected to CA, HT had significantly worsen performance on both MoCA (22.66±0.97 vs 26.21±0.57 NT) and Stroop Test (34.50±3.87 vs 17.75±2.57 NT) (p<0.01). Conversely, tests regarding Verbal Fluency and Instrumental Activities of Daily Living revealed normal performance of HT, thus indicating a selective impairment of memory. Brain imaging showed that, while none of the patients had abnormal signal intensity on T1/T2-weighted MRI, DTI indices FA were significantly reduced in HT as vs NT. In particular, HT had lower FA in projection fibers related to impairment for non-verbal materials (Anterior Thalamic Radiation: 0.358±0.012 vs 0.330±0.006, p<0.05), association fibers involved in executive functioning and emotional regulation (Superior Longitudinal Fasciculus: 0.388±0.013 vs 0.356±0.007, p<0.05), limbic system fibers involved in attention tasks (cingulate gyrus: 0.364±0.009 vs 0.328±0.010, p<0.01). Our data highlight a novel paradigm of combined DTI/CA of HT patients, capable to identify, with great sensitivity, predictive signs of HTN-induced VCI.
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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 | 0.000 |
| 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) | 0.004 | 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".