Functional alteration due to structural damage is network dependent: insight from multiple sclerosis
Bibliographic record
Abstract
Little is known about how the brain's functional organization changes over time with respect to structural damage. Using multiple sclerosis as a model of structural damage, we assessed how much functional connectivity (FC) changed within and between preselected resting-state networks (RSNs) in 122 subjects (72 with multiple sclerosis and 50 healthy controls). We acquired the structural, diffusion, and functional MRI to compute functional connectomes and structural disconnectivity profiles. Change in FC was calculated by comparing each multiple sclerosis participant's pairwise FC to controls, while structural disruption (SD) was computed from abnormalities in diffusion MRI via the Network Modification tool. We used an ordinary least squares regression to predict the change in FC from SD for 9 common RSNs. We found clear differences in how RSNs functionally respond to structural damage, namely that higher-order networks were more likely to experience changes in FC in response to structural damage (default mode R2 = 0.160-0.207, P < 0.001) than lower-order sensory networks (visual network 1 R2 = 0.001-0.007, P = 0.157-0.387). Our findings suggest that functional adaptability to structural damage depends on how involved the affected network is in higher-order processing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".