Modulation of the Left Prefrontal Cortex with High Frequency Repetitive Transcranial Magnetic Stimulation Facilitates Gait in Multiple Sclerosis
Bibliographic record
Abstract
Multiple Sclerosis (MS) is a chronic central nervous system (CNS) demyelinating disease. Gait abnormalities are common and disabling in patients with MS with limited treatment options available. Emerging evidence suggests a role of prefrontal attention networks in modulating gait. High-frequency repetitive transcranial magnetic stimulation (rTMS) is known to enhance cortical excitability in stimulated cortex and its correlates. We investigated the effect of high-frequency left prefrontal rTMS on gait parameters in a 51-year-old Caucasian male with chronic relapsing/remitting MS with residual disabling attention and gait symptoms. Patient received 6 Hz, rTMS at 90% motor threshold using figure of eight coil centered on F 3 location (using 10-20 electroencephalography (EEG) lead localization system). GAITRite gait analysis system was used to collect objective gait measures before and after one session and in another occasion three consecutive daily sessions of rTMS. Two-tailed within subject repeated measure t-test showed significant enhancement in ambulation time, gait velocity, and cadence after three consecutive daily sessions of rTMS. Modulating left prefrontal cortex excitability using rTMS resulted in significant change in gait parameters after three sessions. To our knowledge, this is the first report that demonstrates the effect of rTMS applied to the prefrontal cortex on gait in MS patients.
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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 | 0.000 |
| 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) | 0.001 | 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".