Parietal direct current stimulation during sensory-motor learning with reversed vision prevents performance gains and increases in cortical excitability in the untrained hand
Bibliographic record
Abstract
We previously showed that improved skill performance in the untrained hand after sensory-motor learning with reversed vision was associated with increased excitability in the human primary motor cortex (M1) and parietofrontal circuits in the untrained hemisphere. Neuroimaging studies have implicated the posterior parietal cortex (PPC) as a critical node in this reversed visuomotor mapping. Here we tested whether transcranial direct current stimulation (tDCS) to right PPC alters sensory-motor learning with reversed vision. We examined whether the degree of sensorimotor learning not only influences the transfer of skill performance in the untrained hand, but also modulates M1 excitability and PPC-M1 interactions in the untrained hemisphere. We measured transfer of skill performance in the untrained left-hand on a serial reaction-time task (SRTT) and cortical excitability with transcranial magnetic stimulation (TMS) in the untrained right hemisphere before and after four training interventions: (1) anodal tDCS to right PPC combined with directly viewing the active learning right-hand; (2) anodal tDCS to right PPC combined with viewing the 'mirrored' image of the moving right-hand superimposed over the inactive hand with left-right optical reversing spectacles (prism); (3) cathodal tDCS to right PPC combined with prism; and (4) sham tDCS combined with prism. We found that both anodal and cathodal tDCS to PPC: (i) impaired visuomotor adaptations; (ii) prevented transfer of skill learning to the untrained left-hand; (iii) decreased PPC-M1 excitability; and (iv) interfered with M1 'plasticity'. These results suggest that right PPC operates at an optimal excitability level and modulations of excitability interfere with the neural circuits for visuomotor adaptation. Acknowledgments: NSERC & CIHR
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".