Functional Magnetic Resonance Imaging Reveals the Neural Substrates of Arm Transport and Grip Formation in Reach-to-Grasp Actions in Humans
Bibliographic record
Abstract
Picking up a cup requires transporting the arm to the cup (transport component) and preshaping the hand appropriately to grasp the handle (grip component). Here, we used functional magnetic resonance imaging to examine the human neural substrates of the transport component and its relationship with the grip component. Participants were shown three-dimensional objects placed either at a near location, adjacent to the hand, or at a far location, within reach but not adjacent to the hand. Participants performed three tasks at each location as follows: (1) touching the object with the knuckles of the right hand; (2) grasping the object with the right hand; or (3) passively viewing the object. The transport component was manipulated by positioning the object in the far versus the near location. The grip component was manipulated by asking participants to grasp the object versus touching it. For the first time, we have identified the neural substrates of the transport component, which include the superior parieto-occipital cortex and the rostral superior parietal lobule. Consistent with past studies, we found specialization for the grip component in bilateral anterior intraparietal sulcus and left ventral premotor cortex; now, however, we also find activity for the grasp even when no transport is involved. In addition to finding areas specialized for the transport and grip components in parietal cortex, we found an integration of the two components in dorsal premotor cortex and supplementary motor areas, two regions that may be important for the coordination of reach and grasp.
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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 | 0.001 |
| 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".