= MEG visual tutorial: Single subject =
''Authors: Francois Tadel, Elizabeth Bock. ''

The aim of this tutorial is to reproduce in the Brainstorm environment the analysis described in the SPM tutorial "[[ftp://ftp.mrc-cbu.cam.ac.uk/personal/rik.henson/wakemandg_hensonrn/Publications/SPM12_manual_chapter.pdf|Multimodal, Multisubject data fusion]]". The data processed here consists in simulateneous MEG/EEG recordings of 16 subjects performing simple visual task on a large number of famous, unfamiliar and scrambled faces.

The analysis is split in two tutorial pages: the present tutorial describes the detailed analysis of one single subject and another one that the describes the batch processing and [[Tutorials/VisualGroup|group analysis of the 16 subjects]].

Note that the operations used here are not detailed, the goal of this tutorial is not to teach Brainstorm to a new inexperienced user. For in depth explanations of the interface and the theory, please refer to the introduction tutorials.

<<TableOfContents(3,2)>>

== License ==
These  data are provided freely for research purposes only (as part of their  Award of the BioMag2010 Data Competition). If you wish to publish any of  these data, please acknowledge Daniel Wakeman and Richard Henson. The  best single reference is: Wakeman DG, Henson RN, [[http://www.nature.com/articles/sdata20151|A  multi-subject, multi-modal human neuroimaging dataset]], Scientific Data (2015)

Any questions, please contact: rik.henson@mrc-cbu.cam.ac.uk

== Presentation of the experiment ==
==== Experiment ====
 * 16 subjects
 * 6 runs (sessions) of approximately 10mins for each subject
 * Presentation of series of images: familiar faces, unfamiliar faces, phase-scrambled faces
 * The subject has to judge the left-right symmetry of each stimulus
 * Total of nearly 300 trials in total for each of the 3 conditions

==== MEG acquisition ====
 * Acquisition at 1100Hz with an Elekta-Neuromag VectorView system (simultaneous MEG+EEG).
 * Recorded channels (404):
  * 102 magnetometers
  * 204 planar gradiometers
  * 70 EEG electrodes recorded with a nose reference.
 * MEG data have been "cleaned" using Signal-Space Separation as implemented in MaxFilter 2.1.
 * A  Polhemus digitizer was used to digitise three fiducial points and a  large number of other points across the scalp, which can be used to  coregister the M/EEG data with the structural MRI image.

==== Subject anatomy ====
 * MRI data acquired on a 3T Siemens TIM Trio: 1x1x1mm T1-weighted structural MRI
 * Processed with FreeSurfer 5.3

== Download and installation ==
 * '''Requirements''':  You have already followed all the introduction tutorials  and you have a  working copy of Brainstorm installed on your computer.
 * The data is hosted on this FTP site (use an FTP client such as FileZilla, not your web browser): <<BR>>ftp://ftp.mrc-cbu.cam.ac.uk/personal/rik.henson/wakemandg_hensonrn/
 * Download only the following folders (about 75Gb):
  * '''EmptyRoom''': Contains 3 sub-directories of empty-room recordings of 3-5mins acquired at roughly the same time of year (spring 2009) as the 16 subjects. The sub-directory names are Year (first 2 digits), Month (second 2 digits) and Day (third 2 digits). Inside each are 2 raw *.fif files: one for which basic SSS has been applied by maxfilter in a similar manner to the subject data above, and one (*-noSSS.fif) for which SSS has not been applied (though the data have been passed through maxfilter just to convert to float format).
  * '''Publications''': Reference publications related with this dataset.
  * '''SubXX/MEEG''': MEG and EEG recordings in FIF format and triggers definition files.
  * '''README.TXT''': License and dataset description.
 * The output of the FreeSurfer segmentation of the T1 structural images are not part of this package. You can either process them by yourself, or download the result of the segmentation on this website. Go to the [[http://neuroimage.usc.edu/bst/download.php|Download]] page, and download the file: '''sample_group_anat.zip'''<<BR>>Unzip this file in the same folder where you downloaded all the datasets.
 * Do not put these downloaded files in any of the Brainstorm  folders (program     folder or database folder). This is really  important that you always     keep your original data files in a  separate folder: the program  folder    can be deleted when updating the  software, and the contents of  the    database folder is supposed to be  manipulated only by the  program    itself.
 * Start Brainstorm (Matlab scripts or stand-alone version), see the [[Installation]] page.

 * Select the menu File > Create new protocol. Name it "'''TutorialVisual'''" and select the options:
  * "'''No, use individual anatomy'''",
  * "'''No, use one channel file per condition'''".

== Import the anatomy ==
 * Switch to the "anatomy" view.
 * Right-click on the TutorialAuditory folder > New subject > '''Sub01'''
  * Leave the default options you set for the protocol
 * Right-click on the subject node > Import anatomy folder:
  * Set the file format: "FreeSurfer folder"
  * Select the folder: '''Anatomy/Sub01'''  (from sample_group_anat.zip)
  * Number of vertices of the cortex surface: 15000 (default value)
 * The two sets of fiducials we usually have to define interactively are here set automatically.
  * '''NAS/LPA/RPA''': The file Anatomy/Sub01/fiducials.m contains the definition of the nasion, left and right ears. The anatomical points used by the authors are the same as the ones we recommend in the Brainstorm [[CoordinateSystems|coordinates systems page]].
  * '''AC/PC/IH''': Identified automatically using the SPM affine registration with an MNI template.
  * If you want to double-check that all these points were correctly marked after importing the anatomy, right-click on the MRI > Edit MRI.

 * At   the end of the process, make sure that the file "cortex_15000V" is   selected (downsampled pial surface, that will be used for the source   estimation). If it is not, double-click on it to select it as the   default cortex surface. Do not worry about the big holes in the head surface, parts of MRI have been remove voluntarily for anonymization purposes.<<BR>><<BR>> {{attachment:anatomy_import.gif||height="384",width="613"}}

== Access the recordings ==

=== Link the recordings ===
 * Switch to the "functional data" view.
 * Right-click on the subject folder > '''Review raw file'''.
  * Select the file format: "'''MEG/EEG: Neuromag FIFF (*.fif)'''"
  * Select all the FIF files in: ''''''Sub01'''/MEEG''' <<BR>><<BR>>SCREEN CAPTURE
 * Events:  SCREEN CAPTURES
 * Refine registration now? '''NO'''
  * The head points that are available in the FIF files contain all the points that were digitized during the MEG acquisition, including the ones corresponding to the parts of the face that have been removed from the MRI. If we run the fitting algorithm, all the points around the nose will not match any close points on the head surface, leading to a wrong result.
  * We will import a set of head points from which the face points have been removed and will run the automatic registration on those new points.
 *

=== Multiple runs and head position ===
 * The  two AEF runs 01 and 02 were acquired successively, the position of the  subject's head in the MEG helmet was estimated twice, once at the  beginning of each run. The subject might have moved between the two  runs. To evaluate visually the displacement between the two runs, select  at the same time all the channel files you want to compare (the ones  for run 01 and 02), right-click > Display sensors > MEG.

  {{http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory?action=AttachFile&do=get&target=raw3.gif|raw3.gif|height="220",width="441",class="attachment"}}
 * Typically,  we would like to group the trials coming from multiple runs by  experimental conditions. However, because of the subject's movements  between runs, it's not possible to directly compare the sensor values  between runs because they probably do not capture the brain activity  coming from the same regions of the brain.
 * You have three options if you consider grouping information from multiple runs:
  * '''Method 1''':  Process all the runs separately and average between runs at the source  level: The more accurate option, but requires a lot more work,  computation time and storage.
  * '''Method 2''':  Ignore movements between runs: This can be acceptable for commodity if  the displacements are really minimal, less accurate but much faster to  process and easier to manipulate.
  * '''Method 3''':  Co-register properly the runs using the process Standardize >  Co-register MEG runs: Can be a good option for displacements under 2cm.
 Warning: This method has not be been fully evaluated on our side, to use  at your own risk. Also, it does not work correctly if you have  different SSP  projectors calculated for multiple runs.
 * In this tutorial, we will illustrate only method 1: runs are not co-registered.

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