Modulating Neural Networks With Transcranial Magnetic Stimulation Applied Over the Dorsal Premotor and Primary Motor Cortices
Bibliographic record
Abstract
Our study uses the combined transcranial magnetic stimulation/positron emission tomography (TMS/PET) method for elucidating neural connectivity of the human motor system. We first altered motor excitability by applying low-frequency repetitive TMS over two cortical motor regions in separate experiments: the dorsal premotor and primary motor cortices. We then assessed the consequences of modulating motor excitability by applying single-pulse TMS over the primary motor cortex and measuring: 1) muscle responses with electromyography and 2) cerebral blood flow with PET. Low-frequency repetitive stimulation reduced muscle responses to a similar degree in both experiments. To map networks of brain regions in which activity changes reflected modulation of motor excitability, we generated t-statistical maps of correlations between reductions in muscle response and differences in cerebral blood flow. Low-frequency repetitive stimulation altered neural activity differently in both experiments. Neural modulation occurred in multiple brain regions after dorsal premotor cortex stimulation; these included motor regions in the frontal cortex as well as more associational regions in the parietal and prefrontal cortices. In contrast, neural modulation occurred in a smaller number of brain regions after primary motor cortex stimulation, many of these confined to the motor system. These findings are consistent with the known differences between the dorsal premotor and primary motor cortices in the extent of cortico-cortical anatomical connectivity in the monkey.
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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 | 0.001 |
| 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) | 0.001 | 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".