Frontiers in Neuroscience (Nov 2024)
Restoration of natural somatic sensations to the amputees: finding the right combination of neurostimulation methods
Abstract
Limb amputation results in such devastating consequences as loss of motor and sensory functions and phantom limb pain (PLP). Neurostimulation-based approaches have been developed to treat this condition, which provide artificial somatosensory feedback such as peripheral nerve stimulation (PNS), spinal cord stimulation (SCS), and transcutaneous electrical nerve stimulation (TENS). Yet, the effectiveness of different neurostimulation methods has been rarely tested in the same participants. Meanwhile, such tests would help to select the most effective method or a combination of methods and could contribute to the development of multisensory limb prostheses. In this study, two transhumeral amputees were implanted with stimulating electrodes placed in the medial nerve and over the spinal cord epidurally. PNS and SCS were tested in each participant as approaches to enable tactile and proprioceptive sensations and suppress PLP. Both PNS and SCS induced sensation in different parts of the phantom hand, which correlated with cortical responses detected with electroencephalographic (EEG) recordings. The sensations produced by PNS more often felt natural compared to those produced by SCS. Еvoked response potentials (ERPs) were more lateralized and adapted faster for PNS compared to SCS. In the tasks performed with the bionic hand, neurostimulation-induced sensations enabled discrimination of object size. As the participants practiced with neurostimulation, they improved on the object-size discrimination task and their sensations became more natural. А combination of PNS and TENS enabled sensations that utilized both tactile and proprioceptive information. This combination was effective to convey the perception of object softness. In addition to enabling sensations, neurostimulation led to a decrease in PLP.Clinical trial registrationhttps://clinicaltrials.gov/, identifier, #NCT05650931.
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