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Published on: September 11, 2017
Targeting grasp-related cortical areas for intracortical brain-machine interfaces
Tyler R Johnson1,2,3, Crispin Foli2,3, Emily C Conlan2,3
1Department of Neurosciences, Cleveland Clinic, Cleveland, OH, USA.
Neuroimage. Reports
|July 20, 2026
Summary
This study improved brain-computer interface electrode placement for grasp decoding in tetraplegia using advanced imaging and 3D models. High-fidelity decoding of arm and hand movements was achieved, aiding spinal cord injury recovery.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Rehabilitation Medicine
Background:
- Intracortical microelectrode arrays are crucial for brain-computer interfaces (BCIs) in individuals with paralysis.
- Precise electrode placement is essential for effective motor decoding, particularly for grasp-related movements.
- Current methods often lack integration of detailed anatomical, functional, and vascular information for optimal targeting.
Purpose of the Study:
- To enhance the precision of intracortical microelectrode array implantation for grasp-related motor decoding.
- To integrate anatomical, functional, and vascular imaging with 3D modeling for preoperative planning.
- To identify surgically feasible and functionally relevant cortical targets for BCIs in individuals with C5 tetraplegia.
Main Methods:
- A multimodal imaging approach combining anatomical MRI, diffusion-weighted imaging, and task-based fMRI was employed.
- Quicktome software integrated structural connectivity and functional activation data for refined target selection.
- 3D-printed models of the skull and cortex facilitated preoperative surgical planning and simulation.
- Postoperative validation involved analyzing neural data during attempted arm and hand movements.
Main Results:
- Functional imaging identified significant grasp-related activation in the anterior intraparietal area (AIP), ventral premotor cortex (PMv), and inferior frontal gyrus (IFG).
- The anterior intraparietal area (AIP) was selected due to strong connectivity with motor cortex and distinct functional activation.
- Subregions 6v and 6r of the ventral premotor cortex (PMv) were chosen for their robust activity and surgical accessibility.
- Postoperative arrays achieved 96% classification accuracy for decoding arm and hand movements.
Conclusions:
- A novel multimodal approach effectively improves intracortical electrode placement for grasp decoding.
- Combining MRI, fMRI, connectivity data, and 3D modeling enables precise and surgically feasible targeting.
- This method is vital for advancing BCIs that utilize grasp-related brain activity for individuals with spinal cord injury.
