Parietal area BA7 integrates motor programs for reaching, grasping, and bimanual coordination
Bibliographic record
Abstract
Skillful interaction with the world requires that the brain uses a multitude of sensorimotor programs and subroutines, such as for reaching, grasping, and the coordination of the two body halves. However, it is unclear how these programs operate together. Networks for reaching, grasping, and bimanual coordination might converge in common brain areas. For example, Brodmann area 7 (BA7) is known to activate in disparate tasks involving the three types of movements separately. Here, we asked whether BA7 plays a key role in integrating coordinated reach-to-grasp movements for both arms together. To test this, we applied transcranial magnetic stimulation (TMS) to disrupt BA7 activity in the left and right hemispheres, while human participants performed a bimanual size-perturbation grasping task using the index and middle fingers of both hands to grasp a rectangular object whose orientation (and thus grasp-relevant width dimension) might or might not change. We found that TMS of the right BA7 during object perturbation disrupted the bimanual grasp and transport/coordination components, and TMS over the left BA7 disrupted unimanual grasps. These results show that right BA7 is causally involved in the integration of reach-to-grasp movements of the two arms. NEW & NOTEWORTHY: Our manuscript describes a role of human Brodmann area 7 (BA7) in the integration of multiple visuomotor programs for reaching, grasping, and bimanual coordination. Our results are the first to suggest that right BA7 is critically involved in the coordination of reach-to-grasp movements of the two arms. The results complement previous reports of right-hemisphere lateralization for bimanual grasps.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".