Functional magnetic resonance adaptation reveals the involvement of the dorsomedial stream in hand orientation for grasping
Bibliographic record
Abstract
Reach-to-grasp actions require coordination of different segments of the upper limbs. Previous studies have examined the neural substrates of arm transport and hand grip components of such actions; however, a third component has been largely neglected: the orientation of the wrist and hand appropriately for the object. Here we used functional magnetic resonance imaging adaptation (fMRA) to investigate human brain areas involved in processing hand orientation during grasping movements. Participants used the dominant right hand to grasp a rod with the four fingers opposing the thumb or to reach and touch the rod with the knuckles without visual feedback. In a control condition, participants passively viewed the rod. Trials in a slow event-related design consisted of two sequential stimuli in which the rod orientation changed (requiring a change in wrist posture while grasping but not reaching or looking) or remained the same. We found reduced activation, that is, adaptation, in superior parieto-occipital cortex (SPOC) when the object was repeatedly grasped with the same orientation. In contrast, there was no adaptation when reaching or looking at an object in the same orientation, suggesting that hand orientation, rather than object orientation, was the critical factor. These results agree with recent neurophysiological research showing that a parieto-occipital area of macaque (V6A) is modulated by hand orientation during reach-to-grasp movements. We suggest that the human dorsomedial stream, like that in the macaque, plays a key role in processing hand orientation in reach-to-grasp movements.
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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) | 0.002 | 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".