Abnormal topological properties and functional-structural coupling of large-scale brain networks in primary angle-closure glaucoma
Bibliographic record
Abstract
Background: A growing body of evidence suggests the presence of functional and structural abnormalities in patients with primary angle-closure glaucoma (PACG). The aim of this study was to examine the topological characteristics of the large-scale functional connectivity (FC) network, structural connectivity (SC) network, and strength of FC-SC coupling in patients with PACG. Methods: This study included 47 patients with PACG and 48 healthy controls (HCs) matched for age, sex, and education. All participants underwent a detailed ophthalmological examination and cognitive assessment via the Montreal Cognitive Assessment scale. Resting-state functional magnetic resonance imaging and diffusion tensor imaging data of all participants were acquired separately. Large-scale FC and SC networks were constructed based on the Automated Anatomical Labeling 90 (AAL90) region atlas. Graph theoretic analysis was used for the computation of global attribute and node attribute indices. Subsequently, whole-brain FC-SC coupling was evaluated by analyzing the correspondence between FC and SC matrices to determine how brain anatomy constrains functional dynamics. Finally, the relationships between topological properties and coupling strengths with ophthalmic parameters and cognitive scales were assessed. Bonferroni correction was applied to all multiple comparisons. Results: Compared with the HC group, the PACG group had a lower normalized clustering coefficient (t=-2.339; P=0.024) and small-worldness (t=-2.017; P=0.047) for the FC network and lower normalized characteristic path length (t=-2.054; P=0.043) for the SC network. In terms of node attributes, the PACG group showed abnormal node degree, node betweenness, and node efficiency for FC and SC, mainly in the frontal, temporal, and occipital lobes. In addition, the strength of FC-SC coupling in the whole brain of patients with PACG was reduced (t=-2.622; P=0.01). The topological characteristics of these functional and structural abnormalities were correlated with visual acuity, disease duration, and Montreal Cognitive Assessment score (P<0.05). Conclusions: We observed significantly weaker FC-SC coupling in patients with PACG compared to HCs, indicating impaired integration of brain structure and function. This decoupling suggests widespread network-level disruption, and this finding advances our understanding of optic nerve damage and cognitive deficits in PACG.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".