Lowered ratio of corticospinal excitation to inhibition predicts greater disability, poorer motor and cognitive function in multiple sclerosis
Bibliographic record
Abstract
Objective Investigate excitatory-inhibitory (E/I) (im)balance using transcranial magnetic stimulation (TMS) in individuals with Multiple Sclerosis (MS) and determine its validity as a neurophysiological biomarker of disability. Methods Participants with MS (n = 83) underwent TMS, cognitive, and motor function assessments. TMS-induced motor evoked potential amplitudes (excitability) and cortical silent periods (inhibition) were assessed bilaterally through recruitment curves. The E/I ratio was calculated as the ratio of excitation to inhibition. Results Participants with greater disability (Expanded Disability Status Scale, EDSS≥3) exhibited lower excitability and increased inhibition compared to those with lower disability (EDSS<3). This resulted in lower E/I ratios in the higher disability group. Individuals with higher disability presented with asymmetrical E/I ratios between brain hemispheres, a pattern not present in the group with lower disability. In regression analyses controlling for demographics, lowered TMS-probed E/I ratio predicted variance in disability (R 2 = 0.37, p < 0.001), upper extremity function (R 2 = 0.35, p < 0.001), walking speed (R 2 = 0.22, p = 0.005), and cognitive performance (R 2 = 0.25, p = 0.007). Receiver Operating Characteristic curve analysis confirmed ‘excellent' discriminative ability of the E/I ratio in distinguishing high and low disability. Finally, excitation superiorly correlated with the E/I ratio than overall inhibition in both hemispheres (p ≤ 0.01). Conclusion The E/I ratio is a potential neurophysiological biomarker of disability level in MS, especially when assessed in the hemisphere corresponding to the weaker body side. Interventions aimed at increasing cortical excitation or reducing inhibition may restore E/I balance potentially stalling progression or improving function in MS.
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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 | 0.003 |
| 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) | 0.001 | 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".