Resting state connectivity alterations in multiple sclerosis revealed by 7 T MRI
Bibliographic record
Abstract
Multiple Sclerosis (MS) is an autoimmune disease that affects the central nervous system and is characterized by demyelination, axonal loss, and neurodegeneration. Functional connectivity (FC), the temporal correlation of multiple distinct brain regions, characterizes resting state networks (RSN) and may contain useful indicators of MS pathology that are not captured with traditional structural MRI metrics. In this project, we aimed to elucidate FC differences between a cohort of age/sex matched healthy controls (HC) (n=15), early stage relapsing-remitting MS (RRMS) (n=14) and late stage secondary progressive MS (SPMS) (n=11) patients. Subjects were scanned using high field 7 T MRI to acquire high quality resting state functional MRI data, as well as high resolution anatomical images. RSN’s were measured using two methods: (1) group independent component analysis, a fully data-driven technique that uncovered group-specific RSNs, and (2) a correlation matrix approach, implemented by selecting 100 predefined regions of interest (ROIs) across a set of template RSN’s. Group differences in FC were measured with reference to MS disability, neuropsychological testing scores, and cortical gray matter thickness. The RRMS phenotype exhibited globally increased FC compared to HC and SPMS. This was particularly evident within and between the default mode network and executive control network. RRMS subjects also showed statistically significant increases in connectivity within the visual and sensorimotor networks compared to HC in dual regression functional MRI data analysis. A regional connectivity analysis using predefined ROIs revealed increased FC in short-range, posterior DMN regions and disrupted long-range FC in thalamic DMN regions of RRMS subjects compared to HC. The strength of thalamic connections in the DMN was also directly correlated with cognitive processing speed in all three cohorts. Overall, the results of our study suggest possible short-term compensatory adaptations in RRMS that may preserve and even strengthen FC in the brain
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.005 | 0.001 |
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".