Abstract WP454: Multi-modal Neuroimaging Biomarkers of Aneurysmal Subarachnoid Hemorrhage
Bibliographic record
Abstract
Aneurysmal subarachnoid hemorrhage (aSAH) is associated with high mortality rates and survivors of aSAH often experience persistent deficits across multiple domains, including language, memory, perception and frontal lobe executive function. At present, little is known about the alterations in brain structure and function associated with aSAH, and specific brain biomarkers of post-stroke impairment. In this study, N=28 individuals were scanned using a comprehensive multi-modal neuroimaging battery, including 14 with chronic aSAH and 14 matched controls. Using a 3T Magnetic Resonance Imaging (MRI) scanner, we acquired resting state functional MRI (rs-fMRI), diffusion tensor imaging (DTI) and T1-weighted voxel-based morphometry (VBM). Comparing aSAH patients to controls, we observed elevated rs-fMRI measures of global connectivity and decreased VBM measures of grey matter, primarily within the midcingulate cortex, but no consistent alterations were seen in DTI measures of white matter. Within-group analysis of aSAH patients subsequently showed that greater impairments in executive function were associated with altered DTI measures, including elevated diffusivity and reduced fractional anisotropy, mainly within posterior corona radiata. These findings indicate that brain function and cortical volume are reliably altered post-stroke, whereas the effects on white matter microstructure depend on the severity of post-stroke impairment. These results provide novel information about potential neuroimaging biomarkers of recovery following aSAH.
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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) | 0.001 | 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".