Tutorials & E-Library

Brainstorm is a collaborative, open-source application dedicated to MEG/EEG/sEEG/ECoG data analysis (visualization, processing and advanced source modeling).

Our objective is to share a comprehensive set of user-friendly tools with the scientific community using MEG/EEG as an experimental technique. For physicians and researchers, the main advantage of Brainstorm is its rich and intuitive graphic interface, which does not require any programming knowledge. We are also putting the emphasis on practical aspects of data analysis (e.g., with scripting for batch analysis and intuitive design of analysis pipelines) to promote reproducibility and productivity in MEG/EEG research. Finally, although Brainstorm is developed with Matlab (and Java), it does not require users to own a Matlab license: an executable, platform-independent (Windows, MacOS, Linux) version is made available in the downloadable package.

Since the project started by the end of the 1990's, our server has registered more than 12,000 accounts. See our reference page for a list of published studies featuring Brainstorm at work.

http://neuroimage.usc.edu/brainstorm

The goal of these introduction tutorials is to guide you through most of the features of the software. All the pages use the same example dataset. The results of one section are most of the time used in the following section, so read these pages in the correct order.
All the introduction tutorials are based on a simple auditory oddball experiment:
• One subject, two acquisition runs of 6 minutes each.
• Subject stimulated binaurally with intra-aural earphones.
• Each run contains 200 regular beeps and 40 easy deviant beeps.
• Recordings with a CTF MEG system with 275 axial gradiometers.
• Anatomy of the subject: 1.5T MRI, processed with FreeSurfer 5.3.
• More details will be given about this dataset along the process.

In this notebook we demonstrate how to fit the parameters of the Tsodyks-Markram model to normalized PSC amplitudes from in vitro somatic recording at different stimulation frequencies (10, 20, and 40 Hz). The in vitro amplitudes used here show a typical depressing PVBC-PVBC connection, extracted from raw traces open-sourced by Zsolt Kohus (KOKI).

In this notebook we demonstrate how to fit the parameters of the Tsodyks-Markram model to a given in vitro somatic recording. The in vitro trace used here shows a typical L5TTPC-L5TTPC depressing connection, kindly provided by Rodrigo Perin (EPFL).

This notebook illustrates how to setup and configure optimisations presented in

Iavarone, Elisabetta, Jane Yi, Ying Shi, Bas-Jan Zandt, Christian O'Reilly, Werner Van Geit, Christian Rössert, Henry Markram, and Sean L. Hill. "Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons.".

This notebook will explain how to set up an optimisation that uses metaparameters (parameters that control other parameters)

This notebook will demonstrate how to instantiate a cell model and evaluator that include local field potential (LFP) computation and its recording using a simulated multi electrode array (MEA).

This notebook demonstrates how to run a simulation of a simple single compartmental cell with fixed/optimized parameters in Arbor. We follow the standard BluePyOpt flow of setting up an electrophysiological experiment and export the cell model to a mixed JSON/ACC-format. We then cross-validate voltage traces obtained with Arbor with those from a Neuron simulation.

This notebook shows you how to optimise the maximal conductance of Neocortical Layer 5 Pyramidal Cell as used in Markram et al. 2015 using Arbor as the simulator.

This notebook shows you how to optimise the maximal conductance of Neocortical Layer 5 Pyramidal Cell as used in Markram et al. 2015.

This notebook shows how the parameters of a NEURON point process (in this case a synapse), can be optimised using BluePyOpt.