Contralateral Brain Region Exhibits a Hemispheric Difference in Brain Dynamic Functional Connectivity in Patients with Chronic ICA Occlusion
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Many imaging studies focused on the assessment of volume atrophy, structural damage, and abnormal functional connectivity (FC) in patients with carotid artery stenosis disease. Our purpose was to investigate the differences in dynamic functional connectivity within the contralateral brain in patients with chronic ICA occlusion using resting-state fMRI. MATERIALS AND METHODS: In this trial, 21 patients with chronic occlusion of the ICA (17 men and 4 women) and 11 patients with chronic occlusion of the left ICA (10 men and 1 woman) underwent resting-state fMRI. The control group consisted of 10 healthy men and 6 healthy women. The Mini-Mental State Examination and the Montreal Cognitive Assessment Scale were used to evaluate cognitive function. Cluster analysis and dynamic graph theory were used to analyze the dynamic functional connectivity data obtained through the sliding window method. RESULTS: The analysis of dynamic graph theory revealed that the regions affected in patients with occlusion of the right ICA primarily involved the primary motor sensory network (precentral gyrus and postcentral gyrus) and the anterior default network (orbital inferior frontal gyrus and anterior cingulate gyrus). The damaged regions of the contralateral hemisphere in patients with left ICA occlusion primarily encompassed the posterior default network (precuneus, inferior parietal lobe, angular gyrus, and amygdala), the salience network (Rolandic operculum, middle cingulate cortex, and amygdala), and the frontal parietal lobe network (angular gyrus, supramarginal gyrus, and inferior parietal lobe). Most of these brain regions are localized within the dominant hemispheric areas. CONCLUSIONS: Cluster analysis and dynamic graph theory analysis of contralateral brain regions in patients with chronic ICA occlusion on different sides revealed significant variations.
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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) | 0.002 | 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".