Insight & Inference for DVARS: 3-part variance decomposition, gives units & p-values to DVARS measures as well as spatiotemporal rs-fMRI diagnostic.
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README.md

Introduction

DOI

Series of scripts, mainly in MATLAB and BASH, to support DVARS inference and DSE variance decomposition proposed in

Afyouni S. & Nichols T.E, Insight and Inference for DVARS, 2017 http://www.biorxiv.org/content/early/2017/04/06/125021.1

The toolbox can be used to:

  • generate DVARS and five standardised variants of the measure.
  • generate p-values for spikes to facilitate the decision whether a spike is corrupted and should be scrubbed.
  • explain variance of 4D fMRI images via three variance components: fast (D-var), slow (S-var) and edge (E-var).
  • explain what share of the whole variance each component occupies.

All materials presented in Afyouni&Nichols (2017) can be reproduced using codes presented in DVARS_Paper17.

Configuration

Dependencies

  • MATLAB2013b (or later) statistical toolbox [required].
  • '/Nifti_Util' for neuroimaging analysis. This folder contains selective function from [optional].
  • FSL 5.0.9 (or later) to produce DSE variance images [optional].

Octave

Both DVARS inference and DSE decomposition methods are also available for Octave via scripts under Octave/. Note that you require statistics package to run the script in Octave:

pkg install -forge io
pkg install -forge statistics

We have only tested the script on Octave 4.4.1.

fMRI Diagnostics

Here we show the proposed diagnostic figure and table can be used to examine the quality of the fMRI datasets. We start with basic example, on minimally pre-processed HCP subject 115320, which can be used to perform DVARS inference, DSE variance decomposition and eventually fMRI diagnostic.

Firstly, load an fMRI image into your Matlab using Nifti_Util tool (or any other external toolbox) available.

Path_to_Nifti='~/your/nifti/file/118730/rfMRI_REST1_LR.nii.gz';
V1 = load_untouch_nii(Path_to_Nifti);
V2 = V1.img;
X0 = size(V2,1); Y0 = size(V2,2); Z0 = size(V2,3); T0 = size(V2,4);
I0 = prod([X0,Y0,Z0]);
Y  = reshape(V2,[I0,T0]); clear V2 V1;

DVARS Inference

DVARSCalc.m allows you to estimate the DVARS-related statistics (e.g. D-var, p-values for spikes and standardised variants of the fast variance including Δ%D-var for practical significance).

[DVARS,DVARS_Stat]=DVARSCalc(Y,'scale',1/100,'TransPower',1/3,'RDVARS','verbose',1);

For detailed description of each input argument, type help DVARSCalc. DVARSCalc prints out a log as

-Input is a Matrix.
-Extra-cranial areas removed: 224998x1200
-Intensity Scaled by 0.01.
-Data centred. Untouched Grand Mean: 9999.2939, Post-norm Grand Mean: 99.9927
-Robust estimate of autocorrelation...
--voxel: 100000
--voxel: 200000
--AC robust estimate was failed on 1 voxels.
-Settings:
--Test Method:          X2
--ExpVal method:        median
--VarEst method:        hIQRd
--Power Transformation: 0.33333

Settings: TestMethod=X2  I=224998  T=1200
----Expected Values----------------------------------
    sig2bar    sig2median    median    sigbar2     xbar
    _______    __________    ______    _______    ______

    26.577     19.723        26.488    23.095     27.028

----Variances----------------------------------------
      S2       IQRd      hIQRd
    ______    ______    _______

    6.1229    1.1031    0.84496

which includes Variance and Expected Values tables shows the results of different methods of estimating the first two moments discussed in Afyouni & Nichols (2017). For mass analysis you can turn off the log by ...,verbose,0,....

DVARS_Stat is a structure containing test statistics and standardised D-var measures. For example, use DVARS_Stat.RDVARS for Relative DVARS or Stat.DeltapDvar for Δ%D-var.

DSE Variance Decomposition

Using DSEvars.m decomposes the image variance (A-var) into three components; fast (D), slow (S) and edge (E) variance.

[V,DSE_Stat]=DSEvars(Y,'scale',1/100);

For detailed description of each input argument, type help DSEvars. DSEvars prints out a log as:

-Input is a Matrix.
-Extra-cranial areas removed: 224998x1200
-Intensity Scaled by 0.01.
-Data centred. Untouched Grand Mean: 9999.2939, Post-norm Grand Mean: 99.9927, Post demean: 1.9908e-07
-Variance images will NOT be saved:
-- Either destination directory was not set OR the input is not a nifti.
----------------------
Sum-of-Mean-Squared (SMS) Table
                  Avar     Dvar    Svar     Evar
                  _____    ____    _____    ____

    Whole         30188    8101    22049    37  
    Global          206       4      199     1  
    non-Global    29981    8096    21849    35  

------------DSE ANOVA Table
                 MS          RMS       Percentage_of_whole    Relative_to_iid
              _________    ________    ___________________    _______________

    Avar         25.157      5.0157          100                    1        
    Dvar         6.7514      2.5984       26.837              0.53719        
    Svar         18.374      4.2865       73.039                1.462        
    Evar       0.031147     0.17648      0.12381               1.4857        
    g_Avar      0.17212     0.41487      0.68418               1539.4        
    g_Dvar     0.003973    0.063032     0.015793               71.126        
    g_Svar       0.1665     0.40805      0.66185               2980.8        
    g_Evar    0.0016433    0.040538    0.0065323                17637        

----------------------

Which includes the DSE ANOVA table. Similarly, log can be turned off by ...,verbose,0,....

V is a structure containing all the variance components and DSE_Stat is a structure containing columns of the ANOVA table as well as Δ%D-var.

Visualisation

We suggest to summarise all the results via a simple visualisation of the DSE variance components and significant DVARS data-points as proposed in paper (see Fig. 3, Fig. 4 and Fig. 5). Using V and DVARS_Stat, this can easily be done as below:

fMRIDiag_plot(V,DVARS_Stat)

Although by passing more input arguments, a richer diagnostic figure can be produced. For example, to add the BOLD intensity image, you can use

fMRIDiag_plot(V,DVARS_Stat,'BOLD',Y)

and also, the displacement information, such as Frame-wise Displacement (FD), can be added to the this figure.

%--Movement Parameters--------------------------------
%replace this path with the path to the text files with movement regressors
%of the image on your machine
%Note that MovPartextImport only works safe with the HCP files, you have to
%insert the movement regressors mannually (just drag the text file into the
%workspace!) to the Matlab.

MovPar=MovPartextImport(['~/115320/MNINonLinear/Results/rfMRI_REST1_LR/Movement_Regressors.txt']);
[FDts,FD_Stat]=FDCalc(MovPar);
%--------------------------------

fMRIDiag_plot(V,DVARS_Stat,'BOLD',Y,'FD',FDts,'AbsMov',[FD_Stat.AbsRot FD_Stat.AbsTrans])

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