Creating Paired Differences

If you analyze 2-time point longitudinal data, you will eventually observe that it is easier to create and analyze difference data (e.g. data from time 2 minus data from time 1). However, if you’re not a scripting guru this might be an annoying task.

This PairDiff.m script will take an even number of images and produce a set of paired difference images, even minus odd.

function PairDiff(Imgs,BaseNm)
% PairDiff(Imgs,BaseNm)
% Imgs   - Matrix of (even-number of) filenames
% BaseNm - Basename for difference images
% Create pairwise difference for a set of images (img2-img1, img4-img3, etc),
% named according to BaseNm (BaseNm_0001, BaseNm_0002, BaseNm_0003, etc).
% $Id: PairDiff.m,v 1.2 2012/02/16 21:36:41 nichols Exp $

if nargin<1 | isempty(Imgs)
  Imgs = spm_select(Inf,'image','Select n*2 images');
if nargin<2 | isempty(BaseNm)
  BaseNm = spm_input('Enter difference basename','+1','s','Diff');
V = spm_vol(Imgs);
n = length(V);
if rem(n,2)~=0
  error('Must specify an even number of images')

V1 = V(1);
V1  = rmfield(V1,{'fname','descrip','n','private'});
V1.dt = [spm_type('float32') spm_platform('bigend')];

for i=1:n/2
  V1.fname = sprintf('%s_%04d.img',BaseNm,i);
  V1.descrip = sprintf('Difference %d',i);
  Vo(i) = spm_create_vol(V1);

% Do the differencing

fprintf('Creating differenced data ')

for i=1:2:n

  img1 = spm_read_vols(V(i));
  img2 = spm_read_vols(V(i+1));

  img = img2-img1;



After I created this I realized that Ged Ridgway also has a similar script, make_diffs.m , that takes two lists of images (baseline, followup) and does the same thing, though with perhaps more intuitive filenames.

SPM8 Gem 1: Zero NaN’s with the zeronan.m script

Follow-up to SPM99 Gem 3: NaNing zero values from the NISOx blog (formerly Neuroimaging Statistics Tips & Tools)

This was the topic of SPM99 Gem 3, converting NaN’s to zeros. For SPM8, see the following script zeronan.m that will zero NaN’s for you.


function ofNm = zeronan(ifNm,val)
% FORMAT ofNm = zeronan(ifNm,val)
% ifNm  - Input filename(s)
% val   - Value to set NaN's to (defaults to zero)
% Output:
% ofNm  - Cell array of output filenames.
% Images have NaN's replaced with zeros, and new images, prefixed with a
% 'z', are created.
% Based on zeronan.m,v 1.3 2005/10/26 21:58:55 nichols Exp 
% Thomas Nichols, 1 April 2011

if nargin<2, val = 0; end
if nargin0'); end

if ~iscell(ifNm)
  ifNm = cellstr(ifNm)';
  ifNm = ifNm(:)';

OtfNm = {};

for fNm = ifNm

  fNm = fNm{:};

  OfNm = ['z' fNm];
  [pth,nm,xt,vol] = spm_fileparts(fNm);
  OfNm = fullfile(pth,['z' nm xt]);

  % Code snippet from John Ashburner...
  VI       = spm_vol(fNm);
  VO       = VI;
  VO.fname = OfNm;
  VO       = spm_create_vol(VO);
  for i=1:VI.dim(3),
    img      = spm_slice_vol(VI,spm_matrix([0 0 i]),VI.dim(1:2),0);
    tmp      = find(isnan(img));
    img(tmp) = val;
    VO       = spm_write_plane(VO,img,i);

  OtfNm = {OtfNm{:} OfNm};


if nargout>0
  ofNm = OtfNm;

SPM5 Gem 6: Corrected cluster size threshold

This is SPM5 version of SPM2 Gem 13.

This is a script that will tell you the corrected cluster size threshold for given cluster-defining threshold: CorrClusTh.m

The usage is pretty self explanatory:

 Find the corrected cluster size threshold for a given alpha
 function [k,Pc] =CorrClusTh(SPM,u,alpha,guess)
 SPM   - SPM data structure
 u     - Cluster defining threshold
         If less than zero, u is taken to be uncorrected P-value
 alpha - FWE-corrected level (defaults to 0.05)
 guess - Set to NaN to use a Newton-Rhapson search (default)
         Or provide a explicit list (e.g. 1:1000) of cluster sizes to
         search over.
         If guess is a (non-NaN) scalar nothing happens, except the the
         corrected P-value of guess is printed. 

 Finds the corrected cluster size (spatial extent) threshold for a given
 cluster defining threshold u and FWE-corrected level alpha. 

To find the 0.05 (default alpha) corrected cluster size threshold for a 0.01 cluster-defining threshold:

>> load SPM
>> CorrClusTh(SPM,0.01)
  For a cluster-defining threshold of 2.4671 the level 0.05 corrected
  cluster size threshold is 7860 and has size (corrected P-value) 0.0499926

Notice that, due to the discreteness of cluster sizes you cannot get an exact 0.05 threshold.

The function uses an automated search which may sometimes fail. If you specify a 4th argument you can manually specify the cluster sizes to search over:

>> CorrClusTh(SPM,0.01,0.05,6000:7000)

  WARNING: Within the range of cluster sizes searched (6000...7000)
  a corrected P-value <= alpha was not found (smallest P: 0.0819589)

  Try increasing the range or an automatic search.

Ooops… bad range.

Lastly, you can use it as a look up for a specific cluster size threshold. For example, how much over the 0.05 level would a cluster size of 7859 be?

>> CorrClusTh(SPM,0.01,0.05,7859)
  For a cluster-defining threshold of 2.4671 a cluster size threshold of
  7859 has corrected P-value 0.050021

Just a pinch!