vbmeg3_tutorial_auditory

Contents

1. Introduction

This tutorial aims to get started with VBMEG through the analysis of a sample data set using GUI. The tutorials suppose you have basic knowledge on MEG/EEG and current source imaging.

1. Scenario

A subject participated the auditory experiment in which 3.2 kHz pure tone is delivered to a subject left ear. Evoked magnetic fields were measured with Yokogawa MEG system. T1-weighted MRI was measured with Simens Trio 3T, which is used to construct an individual subject brain model.

In this tutorial, we start from importing preprocessed MEG data and T1-MRI data. MEG data was preprocessed using MEGLABORATORY (YOKOGAWA SOFTWARE) to obtain trial-average MEG data. T1-MRI was preprocessed using FreeSurfer to obtain the subject's cortical surface. In addition MEG sensor positions were coregistered to the subject T1-image using our positioning software.

2. Directory

  • Programs and input data are assumed to be stored as following:
D:\vbmeg(VBMEG program directory)
D:\data(input data directory)
│  3D.nii
│
├─FS
│  ├─bem
│  │      lh.curv.asc
│  │      lh.inflated.asc
│  │      lh.smoothwm.asc
│  │      lh.sphere.reg.asc
│  │      rh.curv.asc
│  │      rh.inflated.asc
│  │      rh.smoothwm.asc
│  │      rh.sphere.reg.asc
│  │
│  └─label
│          lh.cortex.label
│          rh.cortex.label
│
└─Yokogawa
        A008a_BC_0.5HPF_100LPF_trig437_label4.ave
        marker1.pos.mat

3. File

Tutorial data can be downloaded from data.zip

  • MEG data file(A008a_BC_0.5HPF_100LPF_trig437_label4.ave)
  • Sensor alignment file(marker1.pos.mat)
  • Structural MRI image file(3D.nii)
  • Cortical model file(lh.(curv/inflated/smoothwm).asc, rh.(curv.inflated/smoothwm).asc)

MEG Data file

  • MEG data file(Yokogawa Electric Corporation format)
  • Auditory stimuli task
  • Averaged brain activity when a 3.2 kHz pure tone is presented to the left ear

Sensor alignment file

  • Information file to align MEG sensor coordinate system with subject’s structural MRI image coordinate system
  • Created by positioning program.

Details of MRI structure image file

Details of cortical model file

2. Starting project

1. Setting a path

  • Start MATLAB and load VBMEG as follows:

setpath

2. Creating project

  1. Start project_mgr from command line.

    >> project_mgr

  2. Select [File]->[New project].

    Create new project

  3. Input project name and push the select button.

    Create new project

  4. Change current directory to "D:". When you press the "make dir" button the dialog to input the name of the directory appears. Input "Auditory" and press the OK button.

    Create new project

  5. The working directory(D:\Auditory) in the dialog that appears, and push APPLY button.

    Create new project

  6. Press OK button and close the dialog.

    Create new project

  7. Select YES in the cofirmation dialog that appears.

    Confirm

  8. project_mgr is ready.

    Ready for VBMEG

3. Brain modeling

1. Importing a cortical model

  1. Select [Data Import]->[Import cortical model].

    Launch import brain gui

  2. Press the "Set Freesurfer subject directory" to set coritcal model files.

    Set Freesufer subject directory

    In the appearing dialog, choose "D:\data\FS" and press the APPLY button.

    Set Freesurfer subject directory

    Then, freesurfer files are automatically set as below.
    FreeSurfer files

  3. Press the select button for MRI file.

    Select MRI file

    In the appearing dialog, choose "D:\data\3D.nii" and press the OK button.
    Select MRI file

    Then MRI file is set as below.
    Selectd MRI file

  4. Specifying output directory (D:\Auditory\brain)
    • Press Select button.

    Make directory for output

    • When you press the "make dir" button the dialog to input the name of the directory appears. Input "brain" and press the OK button.

    Make directory for output

    • Press the APPLY button.

    Make directory for output

    • The output file name is updated.

    Save files

  5. Press Exec button and wait for approximately 20 minutes. When the importation of cortical model is complete the results will be displayed.

    Import brain

    The following figure shows the displayed results.
    Import result

4. MEG/EEG preprocessing

1. Importing MEG data

  1. Select [Data Import]->[Import MEG data]->[Yokogawa].

    Launch import meg data gui

  2. Specify Yokogawa MEG file.

    MEG data import gui

  3. Change the extension to .ave

    MEG data import dialog

  4. Select Yokogawa MEG file and press OK.(D:\data\Yokogawa\A008aBC0.5HPF100LPFtrig437_label4.ave)

    MEG data import dialog

  5. Press select button and select sensor alignment file(D:\data\Yokogawa\marker1.pos.mat).

    MEG data import dialog

  6. Create output destination(D:\Auditory\meg).
    • Press the Select button.

    Make directory for output

    • When you press the "make dir" button the dialog to input the name of the directory appears. Input "meg" and press the OK button.

    Make directory for output

    • Press the APPLY button.

    Make directory for output

    • The output file name is updated.

    Save files

  7. Press Exec button. The MEG data import will be completed in approximately 10 seconds.

    Import MEG data

    <<MATLAB Command window>>
    Import result

2. View MEG

meg_file = 'D:\Auditory\meg\A008a_BC_0.5HPF_100LPF_trig437_label4.meg.mat';

job_plot_meg(meg_file);

View MEG

5. Source imaging

1. Calculating leadfield

  1. Select [Analysis]->[Calculate leadfield].

    Launch leadfield calculation gui

  2. Specify MEG file(D:\Auditory\meg\A008a_BC_0.5HPF_100LPF_trig437_label4.meg.mat).

    meg file

  3. Specify Cortical model file(D:\Auditory\brain\3D.brain.mat).

    cortical model file

  4. Create output destination(D:\Auditory\leadfield).
    • Push Select button.

    Make directory for output

    • When you press the "make dir" button the dialog to input the name of the directory appears. Input "leadfield" and press the OK button.

    Make directory for output

    • Press APPLY button.

    Make directory for output

    • The output file name is updated.

    Save files

  5. Press Exec button. The leadfield calculation will be completed in 20 to 30 seconds.

    Calculate leadfield

    <<MATLAB Command window>>
    Calculate leadfield

2. Estimating current variance

  1. Select [Analysis]->[Estimate current variance].

    Launch VB estimation gui

  2. Specify Cortical model file(D:\Auditory\brain\3D.brain.mat).

    Cortical model file

  3. Specify Leadfield file(D:\Auditory\leadfield\A008a_BC_0.5HPF_100LPF_trig437_label4.basis.mat).

    Leadfield file

  4. Specify MEG file(D:\Auditory\meg\A008a_BC_0.5HPF_100LPF_trig437_label4.meg.mat).

    MEG file

  5. Specify Cortical area file(D:\Auditory\brain\3D.area.mat).

    Cortical area file

  6. Specify Cortical activity file(D:\Auditory\brain\3D.act.mat).

    Cortical activity file

  7. Set the Variance magnification parameter to 10.

    Variance magnification parameter

  8. Set the Prior weight parameter to 0.018

    Prior weight parameter

  9. Set the Dipole reduction ratio to 0.2.

    Dipole reduction ratio

  10. Create output destination(D:\Auditory\bayes).
    • Press Selectbutton.

    Make directory for output

    • When you press the "make dir" button the dialog to input the name of the directory appears. Input "bayes" and press the OK button.

    Make directory for output

    • Press the APPLY button.

    Make directory for output

  11. Input the name of the output file.

    auditory_left[ENTER] will make it possible to add the extension:.bayes.mat automatically to the file name.
    Save files

  12. Press "Exec" button and the estimate will be complete in approximately 20 minutes.

    Current Variance Estimation

    <<MATLAB Command Window>>

    Noise model: full covariance
    Load MEG data for noise estimate [D:\Auditory\.\meg\A008a_BC_0.5HPF_100LPF_trig437_label4.meg.mat]
    Session 1: -500.0 - -0.8[ms]
    Number of basis files (= Number of sessions): 1
    Area ID: Cortex
    Number of vertices: 9336
    --- Reduce cortex
    max face ratio=0.200000 
    edge index number 30000 
    mesh simplificaton in progress ... 
     
     
    Edges collected: 15000 
    Edges proccessed: 4011 
    Edges collapsed: 4000 
     
    Edges not collapsed due to topological constrians: 10 
    Edge not collapsed due to cost computation constrians: 0 
    Edge not collapsed due to placement computation constrians: 0 
     
    Finished... 
    12000 edges removed. 
    3000 final edges. 
    max face ratio=0.200000 
    edge index number 29994 
    mesh simplificaton in progress ... 
     
     
    Edges collected: 14997 
    Edges proccessed: 4015 
    Edges collapsed: 4000 
     
    Edges not collapsed due to topological constrians: 14 
    Edge not collapsed due to cost computation constrians: 0 
    Edge not collapsed due to placement computation constrians: 0 
     
    Finished... 
    12000 edges removed. 
    2997 final edges. 
    Number of reduced vertices: 1840
    --- Spatial smoothing filter calculation
    R = 6.00e-03, Rmax= 1.20e-02
    Number of vertices = 1840
    Number of vertices in expanded area = 9568
    Basis file for session 1: D:\Auditory\.\leadfield\A008a_BC_0.5HPF_100LPF_trig437_label4.basis.mat
    Number of basis files (= Number of sessions): 1
    Area ID: Cortex
    Number of vertices: 9336
    --- Reduce cortex
    max face ratio=0.200000 
    edge index number 30000 
    mesh simplificaton in progress ... 
     
     
    Edges collected: 15000 
    Edges proccessed: 4011 
    Edges collapsed: 4000 
     
    Edges not collapsed due to topological constrians: 10 
    Edge not collapsed due to cost computation constrians: 0 
    Edge not collapsed due to placement computation constrians: 0 
     
    Finished... 
    12000 edges removed. 
    3000 final edges. 
    max face ratio=0.200000 
    edge index number 29994 
    mesh simplificaton in progress ... 
     
     
    Edges collected: 14997 
    Edges proccessed: 4015 
    Edges collapsed: 4000 
     
    Edges not collapsed due to topological constrians: 14 
    Edge not collapsed due to cost computation constrians: 0 
    Edge not collapsed due to placement computation constrians: 0 
     
    Finished... 
    12000 edges removed. 
    2997 final edges. 
    Number of reduced vertices: 1840
    --- Spatial smoothing filter calculation
    R = 6.00e-03, Rmax= 1.20e-02
    Number of vertices = 1840
    Number of vertices in expanded area = 9568
    Basis file for session 1: D:\Auditory\.\leadfield\A008a_BC_0.5HPF_100LPF_trig437_label4.basis.mat
      Number of sessions          : 1
      Time window                 : -500.0 - 499.2 [ms]
      MEG data file (session  1)  : D:\Auditory\.\meg\A008a_BC_0.5HPF_100LPF_trig437_label4.meg.mat
      Number of sensors           : 400
      Number of trials            : 1
    --- Check variable consistency is OK
    Noise model: spherical
    Load MEG data for noise estimate [D:\Auditory\.\meg\A008a_BC_0.5HPF_100LPF_trig437_label4.meg.mat]
    Session 1: -500.0 - -0.8[ms]
      Number of sessions          : 1
      Time window                 : -500.0 - -0.8 [ms]
      MEG data file (session  1)  : D:\Auditory\.\meg\A008a_BC_0.5HPF_100LPF_trig437_label4.meg.mat
      Number of sensors           : 400
      Number of trials            : 1
    --- Initial VB-update iteration = 100
    --- Total update iteration      = 100
    Sensor noise variance is estimated
    Background current variance is estimated
    Set prior fMRI activity pattern

    --- New VBMEG estimation program ---

    --- Initial VB-update iteration = 1000
    --- Total update iteration      = 1000
    Tn=  1, Iter=  50, FE=16184522.733988, Error=3.111705e-01
    Tn=  1, Iter= 100, FE=16184692.961707, Error=3.119022e-01
    Tn=  1, Iter= 150, FE=16184715.548904, Error=3.119734e-01
    Tn=  1, Iter= 200, FE=16184723.515984, Error=3.119921e-01
    Tn=  1, Iter= 250, FE=16184724.259743, Error=3.119959e-01
    Tn=  1, Iter= 300, FE=16184724.376362, Error=3.119950e-01
    Tn=  1, Iter= 350, FE=16184724.399412, Error=3.119937e-01
    Tn=  1, Iter= 400, FE=16184724.405922, Error=3.119926e-01
    Tn=  1, Iter= 450, FE=16184724.408611, Error=3.119917e-01
    Tn=  1, Iter= 500, FE=16184724.409974, Error=3.119910e-01
    Tn=  1, Iter= 550, FE=16184724.410716, Error=3.119905e-01
    Tn=  1, Iter= 600, FE=16184724.411129, Error=3.119902e-01
    Tn=  1, Iter= 650, FE=16184724.411361, Error=3.119899e-01
    Tn=  1, Iter= 700, FE=16184724.411491, Error=3.119897e-01
    Tn=  1, Iter= 750, FE=16184724.411564, Error=3.119895e-01
    Tn=  1, Iter= 800, FE=16184724.411605, Error=3.119894e-01
    Tn=  1, Iter= 850, FE=16184724.411628, Error=3.119893e-01
    Tn=  1, Iter= 900, FE=16184724.411642, Error=3.119892e-01
    Tn=  1, Iter= 950, FE=16184724.411649, Error=3.119892e-01
    Tn=  1, Iter=1000, FE=16184724.411653, Error=3.119891e-01
    Alpha is scaled back by bsnorm
    ----- Save estimation result in D:\Auditory\.\bayes\audirory_left.bayes.mat

3. Calculating current

  1. Select [Analysis]->[Estimate current].

    Launch Cortical current calculation gui

  2. Specify the current variance file(D:\Auditory\bayes\auditory_left.bayes.mat).

    The MEG file is set automatically.
    Current variance file

  3. Specify the output destination(D:\Auditory\current).
    • Press Select button.

    Make directory for output

    • When you press the "make dir" button the dialog to input the name of the directory appears. Input "current" and press the OK button.

    Make directory for output

    • Press the APPLY button.

    Make directory for output

    • The output file name is updated.

    Save files

  4. Press "Exec" button and the current calculation will be complete in 1 minute.

    Current calculation

    <<MATLAB Command Window>>

    Start current estimation
      Number of sessions          : 1
      Time window                 : -500.0 - 499.2 [ms]
      MEG data file (session  1)  : D:\Auditory\.\meg\A008a_BC_0.5HPF_100LPF_trig437_label4.meg.mat
      Number of sensors           : 400
      Number of trials            : 1
    --- Lead field matrix of focal window
    Number of basis files (= Number of sessions): 1
    Area ID: Cortex
    Number of vertices: 9336
    --- Reduce cortex
    max face ratio=0.200000 
    edge index number 30000 
    mesh simplificaton in progress ... 
     
     
    Edges collected: 15000 
    Edges proccessed: 4011 
    Edges collapsed: 4000 
     
    Edges not collapsed due to topological constrians: 10 
    Edge not collapsed due to cost computation constrians: 0 
    Edge not collapsed due to placement computation constrians: 0 
     
    Finished... 
    12000 edges removed. 
    3000 final edges. 
    max face ratio=0.200000 
    edge index number 29994 
    mesh simplificaton in progress ... 
     
     
    Edges collected: 14997 
    Edges proccessed: 4015 
    Edges collapsed: 4000 
     
    Edges not collapsed due to topological constrians: 14 
    Edge not collapsed due to cost computation constrians: 0 
    Edge not collapsed due to placement computation constrians: 0 
     
    Finished... 
    12000 edges removed. 
    2997 final edges. 
    Number of reduced vertices: 1840
    --- Spatial smoothing filter calculation
    R = 6.00e-03, Rmax= 1.20e-02
    Number of vertices = 1840
    Number of vertices in expanded area = 9568
    Basis file for session 1: D:\Auditory\.\leadfield\A008a_BC_0.5HPF_100LPF_trig437_label4.basis.mat
    Noise model: full covariance
    Load MEG data for noise estimate [D:\Auditory\.\meg\A008a_BC_0.5HPF_100LPF_trig437_label4.meg.mat]
    Session 1: -500.0 - -0.8[ms]
    --- Save estimated current: D:\Auditory\.\current\audirory_left.curr.mat

4. Viewing current

  1. Return to the project_mgr GUI, Choose "vb_job_current" and then select

    "View estimated current" from the pop-up menu that appears on the upper right.
    Launch Cortical current viewer

    Then, dialog appears as below. Press the OK button.
    Cortical current viewer

  2. The GUI to show the estimated current will be displayed. In the spatial pattern display panel on the upper left of the screen, the average current intensity distribution for the selected time of interest (TOI) will be displayed on the inflated brain model. In the current time series display panel at the bottom of the screen, the current intensity time series averaged over the vertex included in the selected region of interest (ROI) will be displayed. The default size of the radius of the ROI is 4 mm.

    Cortical current viewer

  3. The TOI can be set by in the "Time window" field. Here, the TOI is set to 80-120 msec which is the latent time of the auditory evoked field (AEF) appearing in the both hemispheres.

    Set timewindow

    The current intensity spatial distribution is updated on the spatial distribution panel along with the setting of the TOI. The TOI appears gray on the time series display panel as shown below:
    Set timewindow

  4. Press the "Left" button and you can see the cortical model from the left side. Brain activities are shown around the auditory region.

    View point left

  5. To choose a vertex (current dipole), left-double-click on a point of the cortical surface model.

    Then the green cross moves to the point specified, and the timecourse plot and vertex information is changed to the one on the selected vertex. Here, the brain activity source around the auditory region of the left brain is selected.
    Choose activity

    The selected vertex index will be shown in "Vertex index" field.
    Vertex index

    The ROI center can also be changed by directly inputing the vertex number to the "vertex index" field.
    Current time series of the selected ROI is displayed on the current time series display panel. The y-axis scale varies according to current time series.
    Select vertex

  6. In order to find the vertex with the highest current intensity, press the "Spatial peak" button. The vertex with the largest time averaged current intensity will be displayed within the ROI and the center of the ROI will be set to this vertex.

    Search spatial peak

    Simultaneously, the vertex number in the "Vertex index" field will be updated.
    Vertex index

    The updated ROI current time series will be displayed on the current time series display panel.
    Current timecourse

  7. The estimated current around the auditory region of the right brain can be shown through the same procedure.

    Select vertex

    Current timecourse