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

Abstract

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.

Long-term modulation of cardiac activity induced by inhibitory control over emotional memories

“…. EEG analyses were performed using Version 0.18.1 of the MNE library 75,76 implemented in Python 3.6, following recent recommendations [77][78][79] . The EEG pipeline relies on automated steps, in order to promote the sharing of results and subsequent replication.…”

Software and resources for experiments and data analysis of MEG and EEG data

“…One is to morph each individual's brain onto a common template and then do the averages across the morphed brains. This strategy is followed in Andersen (2018a). Another is to warp the sources of the source model of each individual, such that his or her sources are aligned to a source model based on a template brain.…”

Does the heart forget? Modulation of cardiac activity induced by inhibitory control over emotional memories

“…Preprocessing. EEG analyses were performed using the version 0.15 of the MNE library (Gramfort, 2013;Gramfort et al, 2014) implemented in python 3.6 and followed recent recommended analysis steps (Bigdely-Shamlo, Mullen, Kothe, Su, & Robbins, 2015;Andersen, 2018;Jas et al, 2018). With the exception of bad channel removal and EOG selection, which was performed by visual inspection, the EEG pipeline relies on automated steps in order to improve both sharing of the results and further replication.…”