Where One Hand Meets the Other: Limb-Specific and Action-Dependent Movement Plans Decoded from Preparatory Signals in Single Human Frontoparietal Brain Areas
Bibliographic record
Abstract
Planning object-directed hand actions requires successful integration of the movement goal with the acting limb. Exactly where and how this sensorimotor integration occurs in the brain has been studied extensively with neurophysiological recordings in nonhuman primates, yet to date, because of limitations of non-invasive methodologies, the ability to examine the same types of planning-related signals in humans has been challenging. Here we show, using a multivoxel pattern analysis of functional MRI (fMRI) data, that the preparatory activity patterns in several frontoparietal brain regions can be used to predict both the limb used and hand action performed in an upcoming movement. Participants performed an event-related delayed movement task whereby they planned and executed grasp or reach actions with either their left or right hand toward a single target object. We found that, although the majority of frontoparietal areas represented hand actions (grasping vs reaching) for the contralateral limb, several areas additionally coded hand actions for the ipsilateral limb. Notable among these were subregions within the posterior parietal cortex (PPC), dorsal premotor cortex (PMd), ventral premotor cortex, dorsolateral prefrontal cortex, presupplementary motor area, and motor cortex, a region more traditionally implicated in contralateral movement generation. Additional analyses suggest that hand actions are represented independently of the intended limb in PPC and PMd. In addition to providing a unique mapping of limb-specific and action-dependent intention-related signals across the human cortical motor system, these findings uncover a much stronger representation of the ipsilateral limb than expected from previous fMRI findings.
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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".