1. ID 265 – Hand flexor and extensor muscles cortical representations during motor imagery: Topographic and neurophysiological differences
- Author
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Roman Lyukmanov, Alexandra G. Poydasheva, O. A. Mokienko, L. A. Chernikova, M. A. Piradov, Alexander A. Frolov, Alexander V. Chervyakov, and N. A. Suponeva
- Subjects
Motor threshold ,Paired stimuli ,medicine.medical_specialty ,Resting state fMRI ,medicine.medical_treatment ,Neurophysiology ,musculoskeletal system ,Sensory Systems ,Transcranial magnetic stimulation ,Physical medicine and rehabilitation ,Motor imagery ,Neurology ,Physiology (medical) ,Healthy volunteers ,medicine ,Neurology (clinical) ,Psychology ,Extensor Digitorum Communis ,Neuroscience - Abstract
Objective To study the topography and neurophysiological differences in cortical motor representations of hand flexor and extensor muscles during motor imagery (MI). Methods Seven healthy volunteers (age 27 ± 3.4) were enrolled. Navigated transcranial magnetic stimulation was performed for mapping mm. Flexor digitorum superficialis, extensor digitorum communis cortical areas at rest and during MI (fingers flexion or extension). We assessed cortical excitability changes by the dynamics of motor threshold (MT) and paired stimuli with interstimulus intervals 2 ms (SICI) and 12 ms (ICF). Results The localization of flexor and extensor “hotspots” was different with a range of 1 cm. MT decreased during MI significantly from 50% to 47% at the flexor hotspot and from 47% to 46% at the extensor hotspot compared to resting state. There was observed the decrease of ICF during MI of both movements compared to resting state. SICI was reduced during imagery of flexion and enhanced during imagery of extension movement. Conclusions Further we will train the participants to imagine hand extension with brain–computer interface with evaluation the neurophysiological difference between trained and untrained. Topographic and neurophysiological differences of cortical motor representations of differentiated movements during MI can be the basis of new non-invasive brain–computer interface approach.
- Published
- 2016
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