4.5 Article

Statistical Inter-stimulus Interval Window Estimation for Transient Neuromodulation via Paired Mechanical and Brain Stimulation

期刊

FRONTIERS IN NEUROROBOTICS
卷 14, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fnbot.2020.00001

关键词

brain stimulation; transcranial magnetic stimulation; mechanical stimulation; motor evoked potential; statistical estimation

资金

  1. National Science Foundation Center for Compact and Efficient Fluid Power [EEC 0540834]
  2. Georgia Tech Neural Engineering Center Seed Grant
  3. Georgia Tech Institute for Robotics and Intelligent Machines Seed Grant
  4. National Science Foundation Research Traineeship Program [NRT 1545287]

向作者/读者索取更多资源

For achieving motor recovery in individuals with sensorimotor deficits, augmented activation of the appropriate sensorimotor system, and facilitated induction of neural plasticity are essential. An emerging procedure that combines peripheral nerve stimulation and its associative stimulation with central brain stimulation is known to enhance the excitability of the motor cortex. In order to effectively apply this paired stimulation technique, timing between central and peripheral stimuli must be individually adjusted. There is a small range of effective timings between two stimuli, or the inter-stimulus interval window (ISI-W). Properties of ISI-W from neuromodulation in response to mechanical stimulation (Mstim) of muscles have been understudied because of the absence of a versatile and reliable mechanical stimulator. This paper adopted a combination of transcranial magnetic stimulation (TMS) and Mstim by using a high-precision robotic mechanical stimulator. A pneumatically operated robotic tendon tapping device was applied. A low-friction linear cylinder achieved high stimulation precision in time and low electromagnetic artifacts in physiological measurements. This paper describes a procedure to effectively estimate an individual ISI-W from the transiently enhanced motor evoked potential (MEP) with a reduced number of paired Mstim and sub-threshold TMS trials by applying statistical sampling and regression technique. This paper applied a total of four parametric and non-parametric statistical regression methods for ISI-W estimation. The developed procedure helps to reduce time for individually adjusting effective ISI, reducing physical burden on the subject.

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