Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation
Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation
复制标题
DOI：
10.3389/fnins.2018.00006
复制
发表时间：
2018-01-22
影响因子：
4.3
通讯作者：
Andersen, Lau M.
中科院分区：
文献类型：
--
作者：
Andersen, Lau M.
脑磁图数据分析管道的一个重要目标是，它允许研究人员花费最大的精力进行统计比较，以回答研究人员的问题，同时又在管道的复杂性和机械上花费最少的精力。我在这里提出了一组函数和脚本，允许建立一个清晰的，可重复的结构，用于将原始数据和处理后的数据分离到文件夹和文件中，这样就可以花费最少的精力：（1）仔细检查正确的输入是否进入正确的函数;（2）确保输出和中间步骤可以被有意义地访问;（3）有效地跨主题组应用操作;（4）如果需要改变任何中间步骤，则重新处理数据。应用脚本只需要对Python语言有一般的了解。数据分析是从Elekta Neuromag系统获得的一组20名健康参与者对右手食指触觉刺激的神经反应。提供了两项分析：从单独的传感器空间表示分别到跨组传感器空间表示和跨组源空间表示。第一次分析所涵盖的处理步骤是过滤原始数据，在数据中找到感兴趣的事件，对数据进行计时，找到并去除与眨眼和心跳相关的独立分量，通过对计时数据进行平均来计算参与者的个体诱发反应，并计算参与者的总平均传感器空间表示。第二个分析从参与者的个体诱发反应开始，包括：估计噪声协方差，创建正向模型，创建逆运算符，使用最小范数程序估计皮质表面上的分布式源活动，将这些估计变形到共同的皮质模板上，并计算与基线统计学上不同的活动模式。为了估计源活动，基于磁共振成像的对象的解剖结构的处理是必要的。这里涵盖了必要的步骤：导入磁共振图像，分割大脑，估计不同组织层之间的边界，制作高分辨率头皮表面以促进配准，创建源空间并为每个受试者创建体积导体。
An important aim of an analysis pipeline for magnetoencephalographic data is that it allows for the researcher spending maximal effort on making the statistical comparisons that will answer the questions of the researcher, while in turn spending minimal effort on the intricacies and machinery of the pipeline. I here present a set of functions and scripts that allow for setting up a clear, reproducible structure for separating raw and processed data into folders and files such that minimal effort can be spend on: (1) double-checking that the right input goes into the right functions; (2) making sure that output and intermediate steps can be accessed meaningfully; (3) applying operations efficiently across groups of subjects; (4) re-processing data if changes to any intermediate step are desirable. Applying the scripts requires only general knowledge about the Python language. The data analyses are neural responses to tactile stimulations of the right index finger in a group of 20 healthy participants acquired from an Elekta Neuromag System. Two analyses are presented: going from individual sensor space representations to, respectively, an across-group sensor space representation and an across-group source space representation. The processing steps covered for the first analysis are filtering the raw data, finding events of interest in the data, epoching data, finding and removing independent components related to eye blinks and heart beats, calculating participants' individual evoked responses by averaging over epoched data and calculating a grand average sensor space representation over participants. The second analysis starts from the participants' individual evoked responses and covers: estimating noise covariance, creating a forward model, creating an inverse operator, estimating distributed source activity on the cortical surface using a minimum norm procedure, morphing those estimates onto a common cortical template and calculating the patterns of activity that are statistically different from baseline. To estimate source activity, processing of the anatomy of subjects based on magnetic resonance imaging is necessary. The necessary steps are covered here: importing magnetic resonance images, segmenting the brain, estimating boundaries between different tissue layers, making fine-resolution scalp surfaces for facilitating co-registration, creating source spaces and creating volume conductors for each subject.