Functional Brain Network Alterations in Patients With Systemic Lupus Erythematosus With Different Cognitive Function States: A Graph Theory Analysis Study
Bibliographic record
Abstract
OBJECTIVE: The study aimed to investigate the characteristics of brain functional network disruption in patients with systemic lupus erythematosus (SLE) with different cognitive function states by using graph theory analysis and to explore their relationship with clinical data and neuropsychiatric scales. METHODS: Resting-state functional magnetic resonance imaging data were collected from 38 female SLE patients and 44 healthy controls. Based on Montreal Cognitive Assessment (MoCA) scores, SLE patients were divided into a high MoCA group (MoCA-H; MoCA score, ≥26) and a low MoCA group (MoCA-L; MoCA score, <26). The matrix of resting-state functional brain networks of subjects in the 3 groups was constructed by using the graph theory approach. The topological properties of the functional brain networks, including global and local metrics, in the 3 groups were calculated. The differences in the topological properties of networks between the 3 groups were compared. In addition, Spearman correlation analysis was used to explore the correlation between altered topological properties of brain networks and clinical indicators, as well as neuropsychiatric scales in SLE patients in the MoCA-L group. RESULTS: At the global level, in the sparsity threshold range of 0.10 to 0.34, the values of small-world properties were greater than 1 in all 3 groups, indicating that functional brain networks of both 3 groups had small-world properties. There were statistically significant differences in the characteristic path length, global, and local efficiency between 3 groups ( F = 3.825, P = 0.0260; F = 3.722, P = 0.0285; and F = 3.457, P = 0.0364, respectively). Systemic lupus erythematosus patients in the MoCA-L group showed increased characteristic path length ( t = 2.816, P = 0.00651), decreased global ( t = -2.729, P = 0.00826), and local efficiency ( t = -2.623, P = 0.0109) compared with healthy controls. No statistically significant differences in local metrics were found between the MoCA-H group and the healthy control, MoCA-L groups. At the local level, there was statistically significant difference in the node efficiency among the 3 groups ( P < 0.05 after Bonferroni correction). Compared with healthy controls, SLE patients in the MoCA-L group showed decreased node efficiency in left anterior cingulate paracingulate gyrus, bilateral putamen, bilateral pallidum, and left Heschl gyrus. No statistically significant differences in the local metrics were found between the MoCA-H, MoCA-L, and healthy control groups. Correlation analysis in SLE patients in the MoCA-L group showed that the characteristic path length was positively correlated with C4 levels ( r = 0.587, P = 0.007), the global and local efficiencies were negatively correlated with C4 levels ( r = -0.599, P = 0.005; r = -0.599, P = 0.005, respectively), and the node efficiency in the bilateral putamen was negatively correlated with C4 levels ( r = -0.611, P = 0.004; r = -0.570, P = 0.009). The node efficiency in the left pallidum was negatively correlated with disease duration ( r = -0.480, P = 0.032). The node efficiency in the left Heschl gyrus was negatively correlated with IgM levels ( r = -0.478, P = 0.033). No correlation was noted between other network metrics, clinical indicators, and neuropsychological scales. CONCLUSIONS: The topological properties of functional brain networks were disrupted in SLE patients with low MoCA scores, suggesting that altered topological properties of the brain networks were associated with cognitive function in SLE patients. Correlation between altered topological properties of the brain networks and clinical indicators was noted in SLE patients with low MoCA scores, suggesting that altered topological properties of brain networks in SLE patients may have clinical significance as imaging markers for monitoring disease changes in patients with SLE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".