Tutorials & E-Library

This tutorial explains how to import and process CTF current phantom recordings. We decided to release this example for testing and cross-validation purposes. With these datasets, we can evaluate the equivalence of various forward models and dipole fitting methods in the case of simple recordings with single dipoles. The recordings are available in two file formats (native and FIF) to cross-validate the file readers available in Brainstorm and MNE. A similar page exists for the Elekta-Neuromag phantom.

The aim of this tutorial is to reproduce in the Brainstorm environment the analysis described in the SPM tutorial "Multimodal, Multisubject data fusion". It is part of a collective effort to document and standardize MEG/EEG group analysis, see Frontier's research topic: From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software.

The data processed here consists in simultaneous MEG/EEG recordings of 16 subjects performing a simple visual task on a large number of famous, unfamiliar and scrambled faces. This tutorial follows another page that explains how to process one single subject in details.

In the typical Brainstorm workflow, you process each subject individually: you have access to the MRI of the subject, you extract the cortex envelope from it using the software of your choice (FreeSurfer, BrainVISA, BrainSuite CAT12 or CIVET), you import the recordings and estimate the sources for each subject separately.

After importing and pre-processing all the individual information, you will probably be interested in calculating some group statistics, to extract the significant features across all the subjects in your study. in order to do this at the source level, all the source files must be using the same support cortex source (= the same source space). The problem you typically face is that the shape and number of vertices of the cortex surface varies across subject: one source index doesn't mean the same thing for all the subjects, hence it's impossible to compare them directly.

The solution is to reproject the sources of all the subjects on the default anatomy. The reprojected versions share the same source space, the cortex from ICBM152 anatomy, so they can be averaged or used to calculate any other statistics across subjects. Different methods are available in Brainstorm depending on the software you are using for the MRI segmentation. The recommended option is to use FreeSurfer and benefit from the very efficient registration system this software offers.

This tutorial extends the information provided in the connectivity tutorial regarding the formulation of (temporal and spectral) Granger causality. Moreover, an numeric example based on simulated signals is provided to verify the results obtained with GC in time and frequency domain.

The previous tutorials explained how to perform the spike sorting and the conversion to LFPs.

LFPs can be analyzed using the extensive library of methods from EEG and MEG research already present in Brainstorm. These signal processing libraries include artifact removal, time-frequency, connectivity, statistics and several other tools that are essential for analyzing electrophysiological data.

Spiking activity can be analyzed in a set of functions that is present in the Electrophysiology library, and help researchers understand their selectivity and interaction with the LFPs.

This tutorial introduces the features developed in Brainstorm to compute and view FOOOF models from a Welch’s Power Spectral Density (PSD) file.

Note: The FOOOF algorithm has recently been renamed by its authors to specparam.

This tutorial introduces dipole fitting and visualization in Brainstorm. You will need to have completed the introduction tutorials up to "Tutorial 16: Average response" before undertaking this tutorial. This tutorial explains how to fit dipoles to time series within Brainstorm.

This tutorial explains how to use DUNEuro to compute the forward model using the finite element method (FEM). The FEM methods use a realistic volume mesh of the head generated from the segmentation of the MRI. The FEM models provide more accurate results than the spherical forward models and more realistic geometry and tissue properties than the BEM methods. Its applications include accurate source localization in MEG/EEG/sEEG/ECoG and TMS/TDCS optimizations.

The scope of this page is limited to a basic example (head model with 3 layers). More advanced options for mesh generation and forward model computation are discussed in other tutorials: FEM mesh generation, FEM tensors estimation, FEM median nerve example. We assume that you have already followed the introduction tutorials, we will not discuss the general principles of forward modeling here.

In this tutorial, we describe the estimation of realistic conductivity tensors of living brain tissues using the BrainSuite software. These results are used in FEM forward modeling, as described in the tutorials: FEM with DUNEuro and FEM median nerve example.

The realistic tensors are estimated from the Diffusion-Weighted Images (DWI): Brainstorm calls the BrainSuite software to compute the diffusion tensors on each brain MRI voxel (DTI), then Effective Medium Approach (EMA) is applied to estimate the conductivity tensors for each element of a tetrahedral FEM mesh. This is particularly interesting for the modeling the anisotropy of the white matter.

BrainSuite is also used for other purposes in Brainstorm, particularly the T1 MRI segmentation, as documented in this tutorial: MRI segmentation: BrainSuite.

FEM forward modeling requires the construction of a 3D model of the head tissues. The volume of the head is divided in small geometrical elements with 4 faces (tetrahedrons) or 6 faces (hexahedrons). Each element is associated with a type of biological tissue (e.g. white matter, gray matter, CSF, skull, skin) and electrical conductivity properties.

This page lists the methods integrated with Brainstorm to generate 3D meshes of the head. For a generic introduction to FEM in Brainstorm, refer to the tutorials: Realistic head model: FEM with DUNEuro and FEM median nerve example.

The statistical analysis is still limited in Brainstorm, but you can easily export your source maps and run your tests with an external application. This tutorial explains how to export source results to SPM8 or SPM12. The screen captures are done using SPM8, but everything is applicable to SPM12 as well. It is based on the median nerve tutorial dataset that was used in the tutorials of the section ?Processing continuous recordings. If you have followed these tutorials, this dataset should be available in your database in the protocol TutorialRaw.

SPM8 has an important limitation with respect with SPM12: it cannot work on surfaces. This tutorial illustrates how to export all the source files to full volumes. If you are interested in processing the source information at the surface level, upgrade to SPM12 and follow the tutorial Export surface source maps to SPM12.

The statistical analysis is still limited in Brainstorm, but you can easily export your source maps and run your tests with an external application. This tutorial explains how to export source maps to SPM12. It is based on the older introduction tutorials (CTF median nerve stimulation, protocol TutorialRaw), but it is easy to translate it to the new tutorials (auditory oddball).

SPM12 can work with source activations represented either as volumes, as illustrated in the tutorial Export to SPM8, or as surfaces, as presented in this tutorial.