Query: MEG EEG

title: FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data
abstract: This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user friendly high level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.

title: Brainstorm: A User Friendly Application for MEG/EEG Analysis
abstract: Brainstorm is a collaborative open source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best established and cutting edge methods through a simple and intuitive graphical user interface (GUI)

title: Human Neocortical Neurosolver (HNN) a new software tool for interpreting the cellular and network origin of human MEG/EEG data
abstract: Magneto and electro encephalography (MEG/EEG) non invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular and circuit level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https:/hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNN's core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signal's origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNN's ability to associate signals across scales makes it a unique tool for translational neuroscience research.

title: MEG/EEG Group Analysis With Brainstorm
abstract: Brainstorm is a free, open source Matlab and Java application for multimodal electrophysiology data analytics and source imaging [primarily MEG, EEG and depth recordings, and integration with MRI and functional near infrared spectroscopy (fNIRS) We also provide a free, platform independent executable version to users without a commercial Matlab license. Brainstorm has a rich and intuitive graphical user interface, which facilitates learning and augments productivity for a wider range of neuroscience users with little or no knowledge of scientific coding and scripting. Yet, it can also be used as a powerful scripting tool for reproducible and shareable batch processing of (large) data volumes. This article describes these Brainstorm interactive and scripted features via illustration through the complete analysis of group data from 16 participants in a MEG vision study.

title: Cluster based permutation tests of MEG/EEG data do not establish significance of effect latency or location.
abstract: Cluster based permutation tests are gaining an almost universal acceptance as inferential procedures in cognitive neuroscience. They elegantly handle the multiple comparisons problem in high dimensional magnetoencephalographic and EEG data. Unfortunately, the power of this procedure comes hand in hand with the allure for unwarranted interpretations of the inferential output, the most prominent of which is the overestimation of the temporal, spatial, and frequency precision of statistical claims. This leads researchers to statements about the onset or offset of a certain effect that is not supported by the permutation test. In this article, we outline problems and common pitfalls of using and interpreting cluster based permutation tests. We illustrate these with simulated data in order to promote a more intuitive understanding of the method. We hope that raising awareness about these issues will be beneficial to common scientific practices, while at the same time increasing the popularity of cluster based permutation procedures.

title: Human Neocortical Neurosolver (HNN) A new software tool for interpreting the cellular and network origin of human MEG/EEG data
abstract: Magneto and electro encephalography (MEG/EEG) non invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular and circuit level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https:/hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNN's core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signal's origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNN's ability to associate signals across scales makes it a unique tool for translational neuroscience research.

title: Ghost interactions in MEG/EEG source space: A note of caution on inter areal coupling measures
abstract: &NA; When combined with source modeling, magneto (MEG) and electroencephalography (EEG) can be used to study long range interactions among cortical processes non invasively. Estimation of such inter areal connectivity is nevertheless hindered by instantaneous field spread and volume conduction, which artificially introduce linear correlations and impair source separability in cortical current estimates. To overcome the inflating effects of linear source mixing inherent to standard interaction measures, alternative phase and amplitude correlation based connectivity measures, such as imaginary coherence and orthogonalized amplitude correlation have been proposed. Being by definition insensitive to zero lag correlations, these techniques have become increasingly popular in the identification of correlations that cannot be attributed to field spread or volume conduction. We show here, however, that while these measures are immune to the direct effects of linear mixing, they may still reveal large numbers of spurious false positive connections through field spread in the vicinity of true interactions. This fundamental problem affects both region of interest based analyses and all to all connectome mappings. Most importantly, beyond defining and illustrating the problem of spurious, or "ghost" interactions, we provide a rigorous quantification of this effect through extensive simulations. Additionally, we further show that signal mixing also significantly limits the separability of neuronal phase and amplitude correlations. We conclude that spurious correlations must be carefully considered in connectivity analyses in MEG/EEG source space even when using measures that are immune to zero lag correlations. HighlightsReliable estimation of neuronal coupling with MEG and EEG is challenged by signal mixing.A number of coupling techniques attempt to overcome this limitation by excluding zero lag interactions.Our simulations illustrate that such interaction metrics will still yield false positives.Spurious, or "ghost" interactions are generally detected between sources in the vicinity of true interacting sources.Signal mixing also severely affects the mutual separability of phase and amplitude correlations.

title: Hyperedge bundling: A practical solution to spurious interactions in MEG/EEG source connectivity analyses
abstract: &NA; Inter areal functional connectivity (FC) neuronal synchronization in particular, is thought to constitute a key systems level mechanism for coordination of neuronal processing and communication between brain regions. Evidence to support this hypothesis has been gained largely using invasive electrophysiological approaches. In humans, neuronal activity can be non invasively recorded only with magneto and electroencephalography (MEG/EEG) which have been used to assess FC networks with high temporal resolution and whole scalp coverage. However, even in source reconstructed MEG/EEG data, signal mixing, or "source leakage" is a significant confounder for FC analyses and network localization. Signal mixing leads to two distinct kinds of false positive observations: artificial interactions (AI) caused directly by mixing and spurious interactions (SI) arising indirectly from the spread of signals from true interacting sources to nearby false loci. To date, several interaction metrics have been developed to solve the AI problem, but the SI problem has remained largely intractable in MEG/EEG all to all source connectivity studies. Here, we advance a novel approach for correcting SIs in FC analyses using source reconstructed MEG/EEG data. Our approach is to bundle observed FC connections into hyperedges by their adjacency in signal mixing. Using realistic simulations, we show here that bundling yields hyperedges with good separability of true positives and little loss in the true positive rate. Hyperedge bundling thus significantly decreases graph noise by minimizing the false positive to true positive ratio. Finally, we demonstrate the advantage of edge bundling in the visualization of large scale cortical networks with real MEG data. We propose that hypergraphs yielded by bundling represent well the set of true cortical interactions that are detectable and dissociable in MEG/EEG connectivity analysis.

title: A Reproducible MEG/EEG Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices
abstract: Cognitive neuroscience questions are commonly tested with experiments that involve a cohort of subjects. The cohort can consist of a handful of subjects for small studies to hundreds or thousands of subjects in open datasets. While there exist various online resources to get started with the analysis of magnetoencephalography (MEG) or electroencephalography (EEG) data, such educational materials are usually restricted to the analysis of a single subject. This is in part because data from larger group studies are harder to share, but also analyses of such data often require subject specific decisions which are hard to document. This work presents the results obtained by the reanalysis of an open dataset from Wakeman and Henson (2015) using the MNE software package. The analysis covers preprocessing steps, quality assurance steps, sensor space analysis of evoked responses, source localization, and statistics in both sensor and source space. Results with possible alternative strategies are presented and discussed at different stages such as the use of high pass filtering versus baseline correction, tSSS vs. SSS, the use of a minimum norm inverse vs. LCMV beamformer, and the use of univariate or multivariate statistics. This aims to provide a comparative study of different stages of M/EEG analysis pipeline on the same dataset, with open access to all of the scripts necessary to reproduce this analysis.

title: Estimating the Timing of Cognitive Operations With MEG/EEG Latency Measures: A Primer, a Brief Tutorial, and an Implementation of Various Methods
abstract: The major advantage of MEG/EEG over other neuroimaging methods is its high temporal resolution. Examining the latency of well studied components can provide a window into the dynamics of cognitive operations beyond traditional response time (RT) measurements. While RTs reflect the cumulative duration of all time consuming cognitive operations involved in a task, component latencies can partition this time into cognitively meaningful sub steps. Surprisingly, most MEG/EEG studies neglect this advantage and restrict analyses to component amplitudes without considering latencies. The major reasons for this neglect might be that, first, the most easily accessible latency measure (peak latency) is often unreliable and that, second, more complex measures are difficult to conceive, implement, and parametrize. The present article illustrates the key advantages and disadvantages of the three main types of latency measures (peak latency, onset latency, and percent area latency) introduces a MATLAB function that extracts all these measures and is compatible with common analysis tools, discusses the most important parameter choices for different research questions and components of interest, and demonstrates its use by various group analyses on one planar gradiometer pair of the publicly available Wakeman and Henson (2015) data. The introduced function can extract from group data not only single subject latencies, but also grand average and jackknife latencies. Furthermore, it gives the choice between different approaches to automatically set baselines and anchor points for latency estimation, approaches that were partly developed by me and that capitalize on the informational richness of MEG/EEG data. Although the function comes with a wide range of customization parameters, the default parameters are set so that even beginners get reasonable results. Graphical depictions of latency estimates, baselines, and anchor points overlaid on individual averages further support learning, understanding and trouble shooting. Once extracted, latency estimates can be submitted to any analysis also available for (averaged) RTs, including tests for mean differences, correlational approaches and cognitive modeling.