Automatized set-up procedure for transcranial magnetic stimulation protocols

被引:20
作者
Harquel, S. [1 ,2 ,3 ,4 ]
Diard, J. [1 ,2 ]
Raffin, E. [1 ,3 ]
Passera, B. [1 ,2 ]
Dall'Igna, G. [3 ,5 ]
Marendaz, C. [1 ,2 ]
David, O. [1 ,3 ]
Chauvin, A. [1 ,2 ]
机构
[1] Univ Grenoble Alpes, F-38000 Grenoble, France
[2] CNRS, UMR 5105, Lab Psychol & Neurocognit, LPNC, F-38000 Grenoble, France
[3] INSERM, U1216, GIN, F-38000 Grenoble, France
[4] CNRS, INSERM, UMS 3552, IRMaGe, F-38000 Grenoble, France
[5] Ctr Hosp Univ Grenoble Alpes, Pole Psychiat & Neurol, F-38000 Grenoble, France
关键词
Robotized TMS; Hotspot hunting; Cortical excitability; MEP; Motor mapping; MOTOR-EVOKED-POTENTIALS; PRECENTRAL GYRUS; CORTEX DISTANCE; ROBOTIZED TMS; HAND AREA; BRAIN; THRESHOLD; EEG; REPRESENTATIONS; OSCILLATIONS;
D O I
10.1016/j.neuroimage.2017.04.001
中图分类号
Q189 [神经科学];
学科分类号
071006 [神经生物学];
摘要
Transcranial Magnetic Stimulation (TMS) established itself as a powerful technique for probing and treating the human brain. Major technological evolutions, such as neuronavigation and robotized systems, have continuously increased the spatial reliability and reproducibility of TMS, by minimizing the influence of human and experimental factors. However, there is still a lack of efficient set-up procedure, which prevents the automation of TMS protocols. For example, the set-up procedure for defining the stimulation intensity specific to each subject is classically done manually by experienced practitioners, by assessing the motor cortical excitability level over the motor hotspot (HS) of a targeted muscle. This is time-consuming and introduces experimental variability. Therefore, we developed a probabilistic Bayesian model (AutoHS) that automatically identifies the HS position. Using virtual and real experiments, we compared the efficacy of the manual and automated procedures. AutoHS appeared to be more reproducible, faster, and at least as reliable as classical manual procedures. By combining AutoHS with robotized TMS and automated motor threshold estimation methods, our approach constitutes the first fully automated set-up procedure for TMS protocols. The use of this procedure decreases inter-experimenter variability while facilitating the handling of TMS protocols used for research and clinical routine.
引用
收藏
页码:307 / 318
页数:12
相关论文
共 55 条
[1]
Ahdab R., 2016, BRAIN TOPOGR, P1
[2]
[Anonymous], 1998, THEORY PROBABILITY
[3]
Awiszus F, 2003, SUPPL CLIN NEUROPHYS, V56, P13
[4]
Awiszus F., 2011, TMS MOTOR THRESHOLD
[5]
qPR: An adaptive partial-report procedure based on Bayesian inference [J].
Baek, Jongsoo ;
Lesmes, Luis Andres ;
Lu, Zhong-Lin .
JOURNAL OF VISION, 2016, 16 (10) :1-23
[6]
BARKER AT, 1985, LANCET, V1, P1106
[7]
Bergmann T. O., 2016, NEUROIMAGE
[8]
Bessiere P, 2013, BAYESIAN PROGRAMMING
[9]
Bessière P, 2008, SPRINGER TRAC ADV RO, V46, P19
[10]
Combined neurostimulation and neuroimaging in cognitive neuroscience: past, present, and future [J].
Bestmann, Sven ;
Feredoes, Eva .
YEAR IN COGNITIVE NEUROSCIENCE, 2013, 1296 :11-30