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Updated: Dec 29, 2025

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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
Published on: April 21, 2023
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Representational Neural Mapping of Dexterous Grasping Before Lifting in Humans
Michelle Marneweck1, Scott T Grafton2
1Michelle Marneweck, School of Psychological Sciences, Monash University, Clayton, Victoria, 3800, Australia Scott Grafton, and.
Summary
The brain integrates grasp and lift forces for object manipulation within the same regions, not separate ones. Neural sensitivity to grasp emerges before lift sensitivity during object interaction.
Area of Science:
- Neuroscience
- Motor Control
- Cognitive Neuroscience
Background:
- Human object interaction relies on integrating grasp and lift forces.
- Previous research often studied reaching and grasping in isolation.
- The neural basis for coordinating grasp and lift remains unclear.
Purpose of the Study:
- To investigate how the brain represents grasp configuration and lift force during object manipulation.
- To determine if neural representations for grasp and lift are spatially or temporally distinct.
- To identify brain regions involved in coordinating grasp and lift actions.
Main Methods:
- fMRI was used to measure BOLD activity in human subjects performing a precision grasp and lift task.
- Object's center of mass was manipulated (left/right) to vary required grasp and lift forces.
- Bayesian variational representational similarity analysis was applied to fMRI data.
Main Results:
- Grasp configuration and lift force are not represented in spatially separate brain regions.
- The same cortical and cerebellar regions show sensitivity to grasp and lift at different time points.
- Neural sensitivity to grasp emerged sequentially before sensitivity to lift.
Conclusions:
- The brain uses shared neural resources, but with temporal dynamics, to represent grasp and lift.
- Coordinated grasp-to-lift actions are controlled by specific dorsolateral, parietal, somatosensory, and cerebellar regions.
- This temporal coding strategy allows for efficient integration of grasp and lift for dexterous object interaction.
Keywords:
dexterous object manipulationforce controlgraspingliftingneural representationsrepresentational similarity analyses
