Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity
Bibliographic record
Abstract
Recent studies indicate that functional connectivity using low-frequency BOLD fluctuations (LFBFs) is reduced between the bilateral primary sensorimotor regions in multiple sclerosis. In addition, it has been shown that pathway-dependent measures of the transverse diffusivity of water in white matter correlate with related clinical measures of functional deficit in multiple sclerosis. Taken together, these methods suggest that MRI methods can be used to probe both functional connectivity and anatomic connectivity in subjects with known white matter impairment. We report the results of a study comparing anatomic connectivity of the transcallosal motor pathway, as measured with diffusion tensor imaging (DTI) and functional connectivity of the bilateral primary sensorimotor cortices (SMC), as measured with LFBFs in the resting state. High angular resolution diffusion imaging was combined with functional MRI to define the transcallosal white matter pathway connecting the bilateral primary SMC. Maps were generated from the probabilistic tracking employed and these maps were used to calculate the mean pathway diffusion measures fractional anisotropy FA, mean diffusivity MD, longitudinal diffusivity lambda(1), and transverse diffusivity lambda(2). These were compared with LFBF-based functional connectivity measures (F(c)) obtained at rest in a cohort of 11 multiple sclerosis patients and approximately 10 age- and gender-matched control subjects. The correlation between FA and F(c) for MS patients was r = -0.63, P < 0.04. The correlation between all subjects lambda(2) and F(c) was r = 0.42, P < 0.05. The correlation between all subjects lambda(2) and F(c) was r = -0.50, P < 0.02. None of the control subject correlations were significant, nor were FA, lambda(1), or MD significantly correlated with F(c) for MS patients. This constitutes the first in vivo observation of a correlation between measures of anatomic connectivity and functional connectivity using spontaneous LFBFs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".