Altered dynamic functional network connectivity in rheumatoid arthritis associated with peripheral inflammation and neuropsychiatric disorders
Bibliographic record
Abstract
OBJECTIVE: This study explored the dynamic functional connective (DFC) alterations in patients with rheumatoid arthritis (RA) and investigated the correlation between the neuropsychiatric symptoms, peripheral inflammation and DFC alterations. METHOD: Using resting-state functional MRI, we investigated the DFC based on spatial independent component analysis and sliding window method for 30 patients with RA and 30 healthy controls (HCs). The Spearman correlation was calculated between aberrant DFC alterations, Montreal Cognitive Assessment (MoCA), Hospital Anxiety and Depression Scale (HAD), C reactive protein (CRP) and erythrocyte sedimentation rate (ESR). Diagnostic efficacy of indicators was assessed using receiver operating characteristic analysis (ROC). RESULTS: Three dynamic functional states were identified. Compared with HC, patients with RA showed reduced FC variabilities between sensorimotor network (SMN) and insula, SMN and orbitofrontal cortex, which were the crucial regions of sensory processing network. The above FC variabilities were correlated with the MoCA, HAD, CRP and ESR in patients with RA. Additionally, the CRP and ESR were negatively correlated to MoCA and positively related to HAD in patients with RA. The ROC analysis results showed that MoCA, HAD and FC variabilities of the sensory processing network could distinguish patients with RA from HC and also identify patients with RA with high ESR. CONCLUSION: Our findings demonstrated that abnormal DFC patterns in sensory processing networks in patients with RA were closely associated with peripheral inflammation and neuropsychiatric symptoms. This indicates that the dynamic temporal characteristics of the brain functional network may be potential neuroimaging biomarkers for revealing the pathological mechanism of RA.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".