Accurate motor mapping in awake common marmosets using micro-electrocorticographical stimulation and stochastic threshold estimation

Akito Kosugi, Mitsuaki Takemi, Banty Tia, Elisa Castagnola, Alberto Ansaldo, Kenta Sato, Friedemann Awiszus, Kazuhiko Seki, Davide Ricci, Luciano Fadiga, Atsushi Iriki, Junichi Ushiba

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Objective. Motor map has been widely used as an indicator of motor skills and learning, cortical injury, plasticity, and functional recovery. Cortical stimulation mapping using epidural electrodes is recently adopted for animal studies. However, several technical limitations still remain. Test-retest reliability of epidural cortical stimulation (ECS) mapping has not been examined in detail. Many previous studies defined evoked movements and motor thresholds by visual inspection, and thus, lacked quantitative measurements. A reliable and quantitative motor map is important to elucidate the mechanisms of motor cortical reorganization. The objective of the current study was to perform reliable ECS mapping of motor representations based on the motor thresholds, which were stochastically estimated by motor evoked potentials and chronically implanted micro-electrocorticographical (μECoG) electrode arrays, in common marmosets. Approach. ECS was applied using the implanted μECoG electrode arrays in three adult common marmosets under awake conditions. Motor evoked potentials were recorded through electromyographical electrodes implanted in upper limb muscles. The motor threshold was calculated through a modified maximum likelihood threshold-hunting algorithm fitted with the recorded data from marmosets. Further, a computer simulation confirmed reliability of the algorithm. Main results. Computer simulation suggested that the modified maximum likelihood threshold-hunting algorithm enabled to estimate motor threshold with acceptable precision. In vivo ECS mapping showed high test-retest reliability with respect to the excitability and location of the cortical forelimb motor representations. Significance. Using implanted μECoG electrode arrays and a modified motor threshold-hunting algorithm, we were able to achieve reliable motor mapping in common marmosets with the ECS system.

Original languageEnglish
Article number036019
JournalJournal of Neural Engineering
Volume15
Issue number3
DOIs
Publication statusPublished - 2018 Apr 6

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Callithrix
Implanted Electrodes
Motor Evoked Potentials
Reproducibility of Results
Computer Simulation
Electrodes
Motor Skills
Forelimb
Upper Extremity
Learning
Bioelectric potentials
Muscles
Wounds and Injuries
Maximum likelihood
Computer simulation

Keywords

  • adaptive threshold hunting
  • cortical stimulation mapping
  • ECoG
  • motor representation
  • motor threshold
  • test-retest reliability

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience

Cite this

Accurate motor mapping in awake common marmosets using micro-electrocorticographical stimulation and stochastic threshold estimation. / Kosugi, Akito; Takemi, Mitsuaki; Tia, Banty; Castagnola, Elisa; Ansaldo, Alberto; Sato, Kenta; Awiszus, Friedemann; Seki, Kazuhiko; Ricci, Davide; Fadiga, Luciano; Iriki, Atsushi; Ushiba, Junichi.

In: Journal of Neural Engineering, Vol. 15, No. 3, 036019, 06.04.2018.

Research output: Contribution to journalArticle

Kosugi, A, Takemi, M, Tia, B, Castagnola, E, Ansaldo, A, Sato, K, Awiszus, F, Seki, K, Ricci, D, Fadiga, L, Iriki, A & Ushiba, J 2018, 'Accurate motor mapping in awake common marmosets using micro-electrocorticographical stimulation and stochastic threshold estimation', Journal of Neural Engineering, vol. 15, no. 3, 036019. https://doi.org/10.1088/1741-2552/aab307
Kosugi, Akito ; Takemi, Mitsuaki ; Tia, Banty ; Castagnola, Elisa ; Ansaldo, Alberto ; Sato, Kenta ; Awiszus, Friedemann ; Seki, Kazuhiko ; Ricci, Davide ; Fadiga, Luciano ; Iriki, Atsushi ; Ushiba, Junichi. / Accurate motor mapping in awake common marmosets using micro-electrocorticographical stimulation and stochastic threshold estimation. In: Journal of Neural Engineering. 2018 ; Vol. 15, No. 3.
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AU - Awiszus, Friedemann

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