You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: cat_stat_cva_ui.m
    path of file: ./repos/ExploreASL/External/SPMmodified/toolbox/cat12
    
    the code of the file until where you have to start completion: function [CVA] = cat_stat_cva_ui(action,varargin)
% VOI extraction of adjusted data and CVA
% FORMAT [CVA] = cat_stat_cva_ui('specify',xSPM,SPM,CVA)
%
% xSPM   - structure containing specific SPM details
%     xSPM.Ic  - indice of contrast (in SPM.xCon)
% SPM    - structure containing generic analysis details
%
% CVA.contrast -  contrast name
% CVA.name     -  CVA name
% CVA.c        -  contrast weights
% CVA.X        -  contrast subspace
% CVA.Y        -  whitened and adjusted data
% CVA.X0       -  null space of contrast

    
function [CVA] = cat_stat_cva_ui(action,varargin)
% VOI extraction of adjusted data and CVA
% FORMAT [CVA] = cat_stat_cva_ui('specify',xSPM,SPM,CVA)
%
% xSPM   - structure containing specific SPM details
%     xSPM.Ic  - indice of contrast (in SPM.xCon)
% SPM    - structure containing generic analysis details
%
% CVA.contrast -  contrast name
% CVA.name     -  CVA name
% CVA.c        -  contrast weights
% CVA.X        -  contrast subspace
% CVA.Y        -  whitened and adjusted data
% CVA.X0       -  null space of contrast
%
% CVA.XYZ      -  locations of voxels (mm)
% CVA.xyz      -  seed voxel location (mm)
% CVA.VOX      -  dimension of voxels (mm)
%
% CVA.V        -  canonical vectors  (data)
% CVA.v        -  canonical variates (data)
% CVA.W        -  canonical vectors  (design)
% CVA.w        -  canonical variates (design)
% CVA.C        -  canonical contrast (design)
%
% CVA.chi      -  Chi-squared statistics testing D >= i
% CVA.df       -  d.f.
% CVA.p        -  p-values
%
% also saved in CVA_*.mat in the SPM working directory
%
% FORMAT [CVA] = cat_stat_cva_ui('results',CVA)
% Display the results of a CVA analysis
%__________________________________________________________________________
%
% This routine allows one to make inferences about effects that are
% distributed in a multivariate fashion or pattern over voxels. It uses
% conventional canonical variates (CVA) analysis (also know as canonical
% correlation analysis, ManCova and linear discriminant analysis).  CVA is
% a complement to MVB, in that the predictor variables remain the design
% matrix and the response variable is the imaging data in the usual way.
% However, the multivariate aspect of this model allows one to test for
% designed effects that are distributed over voxels and thereby increase
% the sensitivity of the analysis.
%
% Because there is only one test, there is no multiple comparison problem.
% The results are shown in term of the maximum intensity projection of the
% (positive) canonical image or vector and the canonical variates based on
% (maximally) correlated mixtures of the explanatory variables and data.
%
% CVA uses the generalised eigenvalue solution to the treatment and
% residual sum of squares and products of a general linear model. The
% eigenvalues (i.e., canonical values), after transformation, have a
% chi-squared distribution and allow one to test the null hypothesis that
% the mapping is D or more dimensional. This inference is shown as a bar
% plot of p-values.  The first p-value is formally identical to that
% obtained using Wilks' Lambda and tests for the significance of any
% mapping.
%
% This routine uses the current contrast to define the subspace of interest
% and treats the remaining design as uninteresting. Conventional results
% for the canonical values are used after the data (and design matrix) have
% been whitened; using the appropriate ReML estimate of non-sphericity.
%
% CVA can be used for decoding because the model employed by CVA does not
% care about the direction of the mapping (hence canonical correlation
% analysis). However, one cannot test for mappings between nonlinear
% mixtures of regional activity and some experimental variable (this is
% what the MVB was introduced for).
%
% References:
%
% Characterizing dynamic brain responses with fMRI: a multivariate
% approach. Friston KJ, Frith CD, Frackowiak RS, Turner R. NeuroImage. 1995
% Jun;2(2):166-72.
%
% A multivariate analysis of evoked responses in EEG and MEG data. Friston
% KJ, Stephan KM, Heather JD, Frith CD, Ioannides AA, Liu LC, Rugg MD,
% Vieth J, Keber H, Hunter K, Frackowiak RS. NeuroImage. 1996 Jun;
% 3(3):167-174.
%__________________________________________________________________________
% Copyright (C) 2008-2014 Wellcome Trust Centre for Neuroimaging

%
% $Id: cat_stat_cva_ui.m 1279 2018-02-15 08:46:37Z gaser $
%
% modified version of
% Karl Friston
% spm_cva_ui.m 6242 2014-10-14 11:16:02Z guillaume 


%-Get figure handles
%--------------------------------------------------------------------------
Finter = spm_figure('FindWin','Interactive');
try, spm_results_ui('Clear'); end