Authors: Ai Phuong S Tong; Kurt Weaver, PhD; Andrew Ko, MD; Jeff Ojemann (Lynnwood, WA)
Neuroprosthetics can improve functionality for amputees because they can be controlled by the intact mind independent of peripheral integrity. However, movement requires integration of sensory feedback and motor planning, which is difficult to map with task-based experiments with limb loss. Objective–compare neural activity during a tactile task and at rest to determine if resting-state coupling localizes integration centers where sensory and motor information converge. Areas of parietal cortex are activated during tasks that require sensory-motor integration. Hypothesis–these areas exhibit increased phase-amplitude coupling (PAC) and correlation with sensory or motor regions at rest because these regions communicate for a functional purpose.
We used epilepsy monitoring procedures used to identify seizure onset zones. We recorded brain activity from five patients at Harborview Medical Center using 8-x-8 electrocorticography grids during rest and a tactile task in which the patient responds to light touch. PAC, a measure of neural coupling, was used to identify information flow between parietal cortex and other regions. Bootstrapping identified statistically significant interactions.
There was a significantly increased proportion of above average response time, >265 ±23 ms, when touch occurred during peaks of 8-12 Hz (alpha band) waveforms within posterior parietal cortex (PPC) (P<0.05). Significant average PAC occurred at rest between the phase of intraparietal sulcus (IPS), bordering PPC, and amplitude of somatosensory (S1) and frontal eye field (FEF) (Z-score>1.645). IPS activity is not correlated with S1 at rest.
Slower response during greater alpha band activity in the PPC indicates it modulates sensory-motor integration during motor response. IPS-FEF coupling is consistent with previously reported resting-state networks. IPS-S1 coupling at rest also suggests information sharing between these two regions. Overall, parietal regions involved in motor control may be identified by resting-state coupling patterns, offering a more reliable biomarker to localize targets for neuroprosthetic feedback.