Optimal Estimation of Neural Recruitment Curves Using Fisher Information: Application to Transcranial Magnetic Stimulation
Optimal Estimation of Neural Recruitment Curves Using Fisher Information: Application to Transcranial Magnetic Stimulation
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DOI：
10.1109/tnsre.2019.2914475
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发表时间：
2019-06-01
影响因子：
4.9
通讯作者：
Peterchev, Angel V.
中科院分区：
文献类型：
--
作者：
Alavi, Seyed Mohammad Mahdi;Goetz, Stefan M.;Peterchev, Angel V.
本文提出了一种新的方法，用于快速和最佳的确定招聘（输入输出，IO）曲线参数的神经刺激。提出了一种基于Fisher信息矩阵的序贯参数估计方法，其停止准则是连续满足给定的估计容差。经颅磁刺激（TMS）诱发的模拟运动反应被用作测试床。FIM-SPE的性能，其特征在于在10 177模拟运行的各种IO参数值对应于不同的虚拟主体，与均匀采样。与均匀采样不同，FIM-SPE识别并采样IO曲线中包含有关曲线参数的最大信息的区域。对于最宽松的停止规则，FIM-SPE收敛所需的样本中位数仅为17，而均匀采样为294。对于最高可靠性停止规则，超过92%的FIM-SPE运行收敛，中位数为88个样本，而所有均匀采样运行达到1000个样本而不收敛。与均匀采样相比，FIM-SPE将估计误差降低了两倍，并且所需的刺激减少了十倍。FIM-SPE可以提高神经刺激IO曲线测定的速度和准确性。在线提供了该算法的软件实现。
This paper presents a novel method for fast and optimal determination of recruitment (input-output, IO) curve parameters in neural stimulation. A sequential parameter estimation (SPE) method was developed based on the Fisher information matrix (FIM), with a stopping rule based on successively satisfying a specified estimation tolerance. Simulated motor responses evoked by transcranial magnetic stimulation (TMS) were used as a test bed. Performance of FIM-SPE was characterized in 10 177 simulation runs for various IO parameter values corresponding to different virtual subjects, compared with uniform sampling. Unlike uniform sampling, FIM-SPE identifies and samples the areas of the IO curve that contain maximum information about the curve parameters. For the most relaxed stopping rule, the median number of samples required for convergence was only 17 for FIM-SPE versus 294 for uniform sampling. For the highest reliability stopping rule, more than 92% of the FIM-SPE runs converged, with a median of 88 samples, whereas all uniform sampling runs reached 1000 samples without converging. Compared to uniform sampling, FIM-SPE reduced estimation errors up to two-fold and required ten times fewer stimuli. FIM-SPE could improve the speed and accuracy of determination of IO curves for neural stimulation. A software implementation of the algorithm is provided online.