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Microstimulation of human somatosensory cortex evokes task-dependent, spatially patterned responses in motor cortex
Natalya D Shelchkova1, John E Downey2, Charles M Greenspon3
1Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA.
Nature Communications
|November 10, 2023
Summary
Intracortical microstimulation (ICMS) of the somatosensory cortex (S1) activates the motor cortex (M1) through direct and indirect pathways. Biomimetic stimulation minimizes disruptions in brain-controlled virtual hands, enhancing tactile feedback.
Area of Science:
- Neuroscience
- Motor Control
- Sensory Feedback
Background:
- The primary motor (M1) and somatosensory (S1) cortices are crucial for motor control.
- Understanding the neural signaling between M1 and S1 is essential but remains limited.
Purpose of the Study:
- To investigate the signaling pathways between the hand representations of S1 and M1.
- To explore the impact of somatosensory stimulation on motor cortex activity and brain-computer interfaces.
Main Methods:
- Recorded M1 neuronal responses to intracortical microstimulation (ICMS) in S1 in three participants with paralyzed hands.
- Analyzed latency, variability, and spatial patterns of M1 activation.
- Tested biomimetic stimulation for tactile feedback in a virtual hand brain-control system.
Main Results:
- ICMS of S1 evoked both short-latency (monosynaptic) and variable (indirect) responses in M1.
- Spatial M1 activation patterns correlated with perceived finger sensations and finger-specific M1 neuronal activity.
- Indirect S1-M1 effects were context-dependent, varying with behavioral tasks.
- Biomimetic stimulation reduced disruption of virtual hand decoder performance caused by ICMS-evoked M1 activity.
Conclusions:
- S1 influences M1 through both direct and indirect, context-dependent pathways.
- Biomimetic tactile feedback strategies can mitigate the negative impact of sensory stimulation on brain-controlled systems.
- This research offers insights into sensory-motor integration and advanced brain-computer interface design.
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