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NIRS data analysis (GLM and visualization)

June 12th, 2009

[last updated: 2013/09/23]

Also check out NIRS data analysis (time series)

Environment requirement

  1. MatLab
  2. SPM 5 or 8
  3. xjView 8
    xjview can be downloaded for free from http://www.alivelearn.net/xjview/
    (If you are inside CIBSR, xjview  is located in /fs/fmrihome/fMRItools/Xjview)
    Add xjview to path by addpath(genpath('/fs/fmrihome/fMRItools/Xjview'))
  4. nirs2img function in this article can be obtained in the page http://www.alivelearn.net/?p=1574. It is located in the nirs folder.
  5. NFRI toolbox [Link updated on 2013/08/13] (for standard brain registration)
    Download from http://www.jichi.ac.jp/brainlab/tools.html and save it in a directory whose name contains no space (e.g. not in something like c:\program files\…).

Preparation

  1. convert NIRS data file to csv format using ETG4000 program.
  2. copy the 3D positioning data (00X.pos). If you didn’t measure 3D positioning data, jump to step 5
  3. use NFRI toolbox (Pepe Dan, Japan. http://www.jichi.ac.jp/brainlab/tools.html) to get the MNI coordinates of each probe.  Detailed information on how to use this toolbox can be found in its manual.
    1. Convert 00?.pos file to csv file using
      pos2csv
    2. Convert the 3D positioning data into MNI space coordinate using
      nfri_mni_estimation
    3. You will get a xls file containing the positions. There are several sheets in that file and you should use the sheet called “WShatC”, which contains the positions of cortical surface.
  4. Find channel positions based on probe positions using probe2channel.m
    probe2channel(probe, config)
  5. If you don’t have 3D positioning data, you may use the template channel positions located in
    load xjview/nirs_data_sample/templateMNI.mat
    .
    You will find 6 variables in MatLab workspace. They are channelMNI3x11  channelMNI3x5   channelMNI4x4   probeMNI3x11    probeMNI3x5     probeMNI4x4. They are all Nx3 matrix.

Read data and do GLM

  1. use readHitachData.m to read the data file (csv format). Type help readHitachData to see how to use it. Note if your input is two files (for 4×4 and 3×5 configurations), this script will automatically concatenate the data.
    [hbo,hbr,mark] = readHitachData({’XC_tap_MES_Probe1.csv’,'XC_tap_MES_Probe2.csv’});
  2. Prepare event onset timing, duration etc from the mark data, or external data you have, for later GLM analysis (step 3). The format is:
    • onset: onset timing of every event. a cell array. Each element is a numeric vector for one event type. Unit: second
    • duration: duration of every event. same with onset, except the meaning of numbers are duration. If the event is punctuated event, use 0 as duration. Unit: second
    • modulation (optional): modulation of event. For the same type of event you may have different intensities. For example, your event is flash of 5 levels of intensities. You can use modulation to modulate the intensity.  Format is exactly same with onset.
  3. GLM analysis using glm. Type help glm for more info.
    [beta, T, pvalue] = glm(hbdata, onset, duration, modulation);
  4. You may want to save the data for future use.
  5. (if you want to view the result in a standard brain) Convert the values (T or beta or contrasts) to an image file by nirs2img. Try help nirs2img to get more information.
    nirs2img(imgFileName, mni, value, doInterp, doXjview)

Visualization

  1. plotTopoMap will plot data on a plane. The data can be T or beta or other values. Type help plotTopoMap for more info. Here is an example (note the data is smoothed by spline):
    plotTopoMap(randn(24,1), '4x4');
  2. nirs2img will convert your data to an image file which can be visuzlied by many fMRI functional image programs (such as xjview). Here is an example of visualizing the image by xjview. Note, after xjview window launches, you need to check “render view”, and then you may choose between new or old style.
    nirs2img('nirs_test.img', mni, value, 1, 1);
  3. You can also visualize the result with NFRI’s nfri_mni_plot (in NFRI toolbox). You need to prepare the the plot data in excel format beforehand. More information can be found in Readme.doc in NFRI toolbox.

Group analysis

  1. For each individual subject, perform GLM and save the beta values for each condition and subject.
  2. Do contrast  on each subject. Contrasts are simply difference of beta values. For example, contrast between 1st condition and 2nd condition is simply c = beta(:,1) - beta(:,2); Then save the contrast in an image file using nirs2img for each subject. You get a bunch of contrast images (one for each subject)
  3. Perform T test on the contrast images using onesampleT.m, or you can use SPM to do one sample T test if you prefer. You will get a T test image file.
  4. Visualize the T test image with xjview (or SPM)
Author: Xu Cui Categories: matlab, nirs Tags:
About the author:

Xu Cui is a human brain research scientist in Stanford University. He lives in the Bay Area in the United States. Check out PaperBox and BizGenius he runs.

 

He was born in He'nan province, China. He received education in Beijing University(BS), University of Tennessee (Knoxville) (MS), Baylor College of Medicine (PhD) and Stanford University (PostDoc). Read more ...
  1. lee
    August 31st, 2009 at 18:26 | #1

    Dear Xu,

    I have been reading your writing on NIRS_SPM for a while. Thank you for contributing :)

    Just a short qns: do you know if there is any Help Forum on NIRS-SPM? I have been trying to use the program but I encountered some annoying errors which I couldnt solve.

    Hope you may point me to the right direction. Thanks in advance!

    - Lee from Singapore

  2. Sabin
    February 22nd, 2010 at 09:40 | #2

    Hi Xu,
    I have been following your writings on NIRS_SPM. Your contribution have been a great help for me.
    I have been trying to learn this NIRS_SPM, but it keeps on giving me error. Is there any other material that I can use to learn NIRS_SPM apart from its user manual OR is there any help forum for NIRS_SPM.
    Hope to get help from you.
    Thanks in advance.
    Sabin

  3. February 22nd, 2010 at 09:49 | #3

    I haven’t been using NIRS_SPM for a while. I don’t know if there is any other resources but I do know the authors are very helpful. You might want to contact them directly.

  4. Lin
    March 9th, 2010 at 02:01 | #4

    Why can’t find this three function code (glm.m, nirs2img.m, onesampleT.m) in environment requirement your suggestion?

  5. Leanne Hirshfield
    August 3rd, 2011 at 13:26 | #5

    Hi Xu,
    I just downloaded the code described in this section and want to play around with an ETG dataset that I did not create position data for. I’m trying to find the template position files that are referenced in:

    “If you don’t have 3D positioning data, you may use the template channel positions located in
    load xjview/nirs_data_sample/templateMNI.mat”

    But I’m having trouble. I don’t have a folder called xjview, just the xjview.m. I can’t seem to find templateMNI.mat. This may be just an issue I’m having w/ matlab, as I haven’t worked with .mat files before. I’ve only worked with .m files. Can you be more specific about how to find these template datafiles? Thanks–I’m really excited to play around with the code you have posted here:)!!

  6. August 3rd, 2011 at 14:55 | #6

    @Leanne Hirshfield
    That file is not in the public package of xjview.
    An email has been sent to you.

  7. Gang Xu
    February 17th, 2013 at 06:16 | #7

    Can you send me a copy of this three files? glm.m, nirs2img.m, and onesampleT.m. Thank you very much.

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