Investigating Functional Network Abnormalities and Associations With Disability in Multiple Sclerosis
Bibliographic record
Abstract
Background and Objectives In multiple sclerosis (MS), functional networks undergo continuous reconfiguration and topography changes over the disease course. In this study, we aimed to investigate functional network to pography abnormalities in MS and their association with disease phenotype, clinical and cognitive disability, and structural MRI damage. Methods This is a multicenter cross-sectional study. Enrolled participants performed MRI and neurologic and neuropsychological assessment. Network topography was assessed on resting state fMRI data using degree centrality, which counted the number of functional connections of each gray matter voxel with the rest of the brain. SPM12 age-adjusted, sex-adjusted, scanner-adjusted, framewise displacement, and gray matter–volume adjusted analysis of variance and multivariable regressions were used (p < 0.05, family-wise error [FWE] corrected). Results We enrolled 971 patients with MS (624 female patients; mean age = 43.1 ± 11.8 years; 47 clinically isolated syndrome [CIS], 704 relapsing-remitting MS [RRMS], 145 secondary progressive MS [SPMS], and 75 primary progressive MS [PPMS]) and 330 healthy controls (186 female patients; mean age = 41.2 ± 13.3 years). Patients with MS showed reduced centrality in the salience and sensorimotor networks as well as increased centrality in the default-mode network vs controls (p < 0.05, FWE). Abnormal centrality was already found in CIS vs controls and in RRMS vs CIS (p < 0.001, uncorrected); however, it became more severe in SPMS vs RRMS (p < 0.05, FWE) and in PPMS vs controls (p < 0.001, uncorrected). Cognitively impaired patients (39%) showed reduced centrality in the salience network and increased centrality in the default-mode network vs cognitively preserved patients (p < 0.001, conjunction analysis). More severe disability correlated with increased centrality in the right precuneus (r = 0.18, p < 0.05 FWE). Higher T2 lesion volume and brain/gray matter atrophy were associated with reduced centrality in the bilateral insula and cerebellum (r = range −0.17/−0.15 and 0.26/0.28, respectively; p < 0.05, FWE). Higher brain/gray matter atrophy was also associated with increased centrality in the default-mode network (r = range −0.31/−0.22, p < 0.05, FWE). Discussion Patients with MS presented with reduced centrality in the salience and primary sensorimotor networks and increased centrality in the default-mode network. Centrality abnormalities were specific for different disease phenotypes and associated with clinical and cognitive disability, hence suggesting that voxel-wise centrality analysis may reflect pathologic substrates underpinning disability accrual.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".