Remapping of fMRI functional connectivity during resting state in multiple sclerosis represents compensatory mechanisms (P6.146)
Bibliographic record
Abstract
OBJECTIVE: To differentiate between changes in overall functional connection strength and remapping of connectivity in multiple sclerosis (MS) using resting state fMRI. BACKGROUND: Altered functional connectivity appears to be a robust feature in MS; both decreases and increases in connectivity strength have been reported. Decreased functional connectivity likely indicates disrupted pathways, while increased connectivity may represent compensatory mechanisms. DESIGN/METHODS: Baseline resting state fMRI scans were acquired for 25 relapsing-remitting MS patients enrolled in a Phase III clinical trial of ocrelizumab (OPERA) and 40 age/gender-matched controls. Cortical parcellation was performed in Freesurfer and 38 cognition-associated regions were chosen for connectivity analysis. Data preprocessing was performed with in-house scripts including SPM and FSL functions. Connectivity analysis was derived using a PCfdr-initialized Bayesian Network (BN) learning approach. The group-wise false-discovery rate was set at 5[percnt]. In addition, simple correlation results were calculated to compare to the BN approach. RESULTS: MS subjects had increased overall connectivity compared to controls using the correlation approach. However, overall connectivity strength from the BN approach was comparable between groups. This suggests a fundamental remapping of connectivity involving inferior prefrontal regions: in controls this area was connected to superior frontal and medial orbitofrontal regions, whereas in MS it was connected to anterior cingulate and posterior parietal regions. In posterior cingulate regions, connections to/from orbitofrontal and precuneus regions in controls were connected to supramarginal regions in MS. CONCLUSIONS: The results emphasize the importance of distinguishing direct vs indirect (co-activation) connectivity in MS, which is possible with the BN approach but not with correlation analysis. Our observations suggest that the increased overall co-activation in MS is a result of de-differentiation of normally focal connectivity patterns between discrete regions. The co-activation changes likely represent compensatory, broadly-enhanced connectivity of still-intact pathways. Study Supported by: Roche Phase III clinical trial of Ocrelizumab (OPERA)
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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 | 0.000 | 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".