Vascular risk factor burden correlates with cerebrovascular reactivity but not resting state coactivation in the default mode network
Bibliographic record
Abstract
PURPOSE: White matter hyperintensities (WMH) are prevalent among older adults and are often associated with cognitive decline and increased risk of stroke and dementia. Vascular risk factors (VRFs) are linked to WMH, yet the impact of multiple VRFs on gray matter function is still unclear. The goal of this study was to test for associations between the number of VRFs and cerebrovascular reactivity (CVR) and resting state (RS) coactivation among individuals with WMH. MATERIALS AND METHODS: Twenty-nine participants with suspected WMH were grouped based on the number of VRFs (subgroups: 0, 1, or ≥2). CVR and RS coactivation were measured with blood oxygenation level-dependent (BOLD) imaging on a 3T magnetic resonance imaging (MRI) system during hypercapnia and rest, respectively. Default-mode (DMN), sensory-motor, and medial-visual networks, generated using independent component analysis of RS-BOLD, were selected as networks of interest (NOIs). CVR-BOLD was analyzed using two methods: 1) a model-based approach using CO2 traces, and 2) a dual-regression (DR) approach using NOIs as spatial inputs. Average CVR and RS coactivations within NOIs were compared between VRF subgroups. A secondary analysis investigated the correlation between CVR and RS coactivation. RESULTS: VRF subgroup differences were detected using DR-based CVR in the DMN (F20,2 = 5.17, P = 0.015) but not the model-based CVR nor RS coactivation. DR-based CVR was correlated with RS coactivation in the DMN (r(2) = 0.28, P = 0.006) but not the sensory-motor nor medial-visual NOIs. CONCLUSION: In individuals with WMH, CVR in the DMN was inversely associated with the number of VRFs and correlated with RS coactivation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".