ArticleNeuroImage2026
Preventing visual feedback of the moving limb during grasping preferentially activates parietofrontal premotor areas.
Article in NeuroImage, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
Accurate control of grasping relies on the brain's ability to estimate the position and configuration of the arm and hand, integrating sensory feedback with motor-based predictions derived from a copy of the movement command. Visual feedback of the moving limb plays an important role in calibrating these internal estimates. When visual feedback is limited, the brain must rely more heavily on proprioception and motor-based predictions. In this study, we use fMRI to test for cortical regions that are preferentially involved in grasp control when visual feedback of the moving limb is selectively prevented. Twenty healthy participants perform reaching-to-touch and reaching-to-grasp actions with and without visual feedback. Critically, target objects are always visible, fixed to illuminate only their surface properties. This contrasts with prior studies that remove vision of both the limb and the target, thereby confounding non-visual control with increased working-memory demands. Our findings reveal a distributed network of posterior parietal and frontal premotor areas that are preferentially active for grasping when visual feedback of the moving limb is unavailable. We interpret these results as reflecting an important role for these areas in maintaining an internal representation of the state of the arm and hand for online grasp control.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.