Increased resting state connectivity between ipsilesional motor cortex and contralesional premotor cortex after transcranial direct current stimulation with physical therapy
Bibliographic record
Abstract
Non-invasive stimulation of the brain using transcranial direct current stimulation (tDCS) during motor rehabilitation can improve the recovery of movements in individuals with stroke. However, the neural substrates that underlie the clinical improvements are not well understood. In this proof-of-principle open-label pilot study, five individuals with stroke received 10 sessions of tDCS while undergoing usual care physical/occupational therapy for the arm and hand. Motor impairment as indexed by the Upper Extremity Fugl Meyer assessment was significantly reduced after the intervention. Resting state fMRI connectivity increased between ipsilesional motor cortex and contralesional premotor cortex after the intervention. These findings provide preliminary evidence that the neural underpinnings of tDCS coupled with rehabilitation exercises, may be mediated by interactions between motor and premotor cortex. The latter, of which has been shown to play an important role in the recovery of movements post-stroke. Our data suggest premotor cortex could be tested as a target region for non-invasive brain-stimulation to enhance connectivity between regions that might be beneficial for stroke motor recovery.
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) | 0.002 | 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".