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snpm_pp.m
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snpm_pp.m
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function snpm_pp(CWD,varargin)
% SnPM post processing and results display
% FORMAT snpm_pp(CWD)
%
% CWD - Directory containing SnPM results files
%
% If CWD is not specified then user is prompted to locate results file SnPM.mat
%_______________________________________________________________________
%
% snpm_pp is the PostProcessing function for the SnPM nonParametric
% statistical analysis. SnPM statistical analyses are split into three
% stages; Setup, Compute & Assess. This is the third stage.
% Nonparametric randomisation distributions are read in from MatLab
% *.mat files, with which the observed statistic image is assessed
% according to user defined parameters. It is the SnPM equivalent of
% the "Results" section of SPM, albeit with reduced features.
%
% Voxel level corrected p-values are computed from the permutation
% distribution of the maximal statistic. If suprathreshold cluster
% statistics were collected in the computation stage (and the large
% SnPM_ST.mat file hasn't been deleted!), then assessment by
% suprathreshold cluster size is also available, using a user-specified
% primary threshold.
%
% Instructions:
%=======================================================================
%
% You are prompted for the following:
%
% (1) ResultsDir: If the results directory wasn't specified on the command
% line, you are prompted to locate the SnPM results file SnPM.mat.
% The directory in which this file resides is taken to be the
% results directory, which must contain *all* the files listed
% below ("SnPM files required").
%
% Results (spm.ps & any requested image files) are written in the
% present working directory, *not* the directory containing the
% results of the SnPM computations.
%
% ----------------
%
% (2) +/-: Having located and loaded the results files, you are asked to
% chose between "Positive or negative effects?". SnPM, like SPM,
% only implements single tailed tests. Choose "+ve" if you wish to
% assess the statistic image for large values, indicating evidence
% against the null hypothesis in favour of a positive alternative
% (activation, or positive slope in a covariate analysis).
%
% Choose "-ve" to assess the negative contrast, i.e. to look for
% evidence against the null hypothesis in favour of a negative
% alternative (de-activation, or a negative slope in a covariate
% analysis). The "-ve" option negates the statistic image and
% contrast, acting as if the negative of the actual contrast was
% entered.
%
% A two-sided test may be constructed by doing two separate
% analyses, one for each tail, at half the chosen significance
% level, doubling the resulting p-values.
% ( Strictly speaking, this is not equivalent to a rigorous two-sided )
% ( non-parametric test using the permutation distribution of the )
% ( absolute maximum statistic, but it'll do! )
%
% ----------------
%
% (3) WriteFiles: All image files have been written by snpm_cp, and there
% is only one file that may be written here: Users are asked 'write
% filtered statistic image?'.
% ----------------
%
% Next come parameters for the assessment of the statistic image...
%
% (4) alpha: (p-value for filtering)
% First, you are asked 'Use corrected threshold?' You can choose to set
% the threshold as FWE-corrected('FWE'), FDR-corrected('FDR') or
% uncorrected('None'). A FWE threshold controls the chance of one or
% more false positives; a FDR threshold controls the expected
% fraction of false positives among the voxels detected.
%
% Then you specify the threshold, the statistical significance
% level at which you wish to assess the evidence against the null
% hypothesis. In SPM this is called "filtering by corrected
% p-value". SnPM will only show you voxels (& suprathreshold
% regions if you choose) that are significant based on the method you
% choose at level \alpha. I.e. only voxels (& regions) with
% corrected (or uncorrected) p-value less than \alpha are shown to
% you.
%
% Setting \alpha to 1 will show you all voxels with a positive statistic.
%
%
% (5) SpatEx: If you collected supra-threshold cluster statistics during
% the SnPM computation phase, you are offered the option to assess
% the statistic image by supra-threshold cluster size (spatial
% extent).
%
% 5a) ST_Ut: If you chose to asses spatial extent, you are now prompted
% for the primary threshold. This is the threshold applied to the
% statistic image for the identification of supra-threshold
% clusters.
%
% The acceptable range is limited. SnPM has to collect
% suprathreshold information for every relabelling. Rather that
% pre-specify the primary threshold, information is recorded for
% each voxel exceeding a low threshold (set in snpm_cp) for every
% permutation. From this, suprathreshold cluster statistics can be
% generated for any threshold higher than the low recording
% threshold. This presents a lower limit on the possible primary
% threshold.
%
% The upper limit (if specified) corresponds to the statistic value
% at which voxels become individually significant at the chosen
% level (\alpha). There is little point perusing a suprathreshold
% cluster analysis at a threshold at which the voxels are
% individually significant.
%
% If the statistics are t-statistics, then you can also specify the
% threshold via the upper tail probability of the t-distribution.
%
% (NB: For the moment, \alpha=1 precludes suprathreshold analysis, )
% ( since all voxels are significant at \alpha=1. )
%
%
% That's it. SnPM will now compute the appropriate significances,
% reporting its progress in the MatLab command window. Note that
% computing suprathreshold cluster size probabilities can take a long
% time, particularly for low thresholds or large numbers of
% relabellings. Eventually, the Graphics window will come up and the
% results displayed.
%
% - Results
%=======================================================================
%
% The format of the results page is similar to that of SPM:
%
% A Maximum Intensity Projection (MIP) of the statistic image is shown
% top left: Only voxels significant (corrected) at the chosen level
% \alpha are shown. (If suprathreshold cluster size is being assessed,
% then clusters are shown if they have significant size *or* if they
% contain voxels themselves significant at the voxel level.) The MIP is
% labelled SnPM{t} or SnPM{Pseudo-t}, the latter indicating that
% variance smoothing was carried out.
%
% On the top right a graphical representation of the Design matrix is
% shown, with the contrast illustrated above.
%
% The lower half of the output contains the table of p-values and
% statistics, and the footnote of analysis parameters. As with SPM, the
% MIP is tabulated by clusters of voxels, showing the maximum voxel
% within each cluster, along with at most three other local maxima
% within the cluster. The table has the following columns:
%
%
% At Cluster Level
%
% * Pcorrected: If "spatial extent" has been assessed, FWE-corrected
% p-values for region size will be shown. The corrected P-value for
% the suprathreshold cluster size is the probability (conditional on
% the data) of the experiment giving a suprathreshold cluster of size
% as or more extreme anywhere in the statistic image. This is the
% proportion of the permutation distribution of the maximal
% suprathreshold cluster size exceeding (or equalling) the observed
% size of the current cluster.
%
% * k: The size (in voxels) of the cluster.
%
%
% At Voxel Level
%
% * PFWE-corr: FWE-corrected non-parametric P-values. This is the
% probability of the experiment giving a voxel statistic this extreme
% anywhere in the statistic image. This is the proportion of the
% permutation distribution of the maximal statistic exceeding (or
% equalling) the observed statistic value.
%
% * PFDR-corr: FDR-corrected non-parametric P-values. This is the
% smallest FDR level for which the voxel would be significant. A level
% q FDR threshold ensures that, on average, no more than q*100% of the
% suprathreshold voxels will be false positives.
%
% * t / Pseudo-t: The statistic value.
%
% * Puncorrected: uncorrected non-parametric P-values. This is the
% result of a non-parametric permutation test completed at this
% individual voxel. This P-value is is the proportion of the
% permutation distribution (at *this* voxel) exceeding (or
% equalling) the observed statistic value.
%
%
% * {x,y,z} mm: Locations of local maxima.
%
% The SnPM parameters footnote contains the following information:
%
% * Primary threshold: If assessing "spatial extent", the primary
% threshold used for identification of suprathreshold clusters is
% printed. If using t-statistics (as opposed to Pseudo-t's), the
% corresponding upper tail probability is also given.
%
% * Critical STCS: The critical suprathreshold cluster size. This is
% size above which suprathreshold clusters have significant size at
% level \alpha It is computed as the 100(1-alpha)%-ile of the
% permutation distribution of the maximal suprathreshold cluster
% size. Only shown when assessing "spatial extent".
%
% * alpha: The test level specified.
%
% * Critical threshold: The critical statistic level. This is the value
% above which voxels are significant (corrected) at level \alpha. It
% is computed as the 100(1-alpha)%-ile of the permutation
% distribution of the maximal statistic.
%
% * df: The degrees of freedom of the t-statistic. This is printed even
% if
% variance smoothing is used, as a guide.
%
% * Volume & voxel dimensions:
%
% * Design: Description of the design
%
% * Perms: Description of the exchangability and permutations used.
%
%
%
% - SnPM files required:
%=======================================================================
% snpm_pp loads parameters and results from the following files, which
% must all be in the same directory:
% SnPMcfg.mat - SnPM design configuration
% SnPM.mat - SnPM analysis & permutation distribution
% SnPMt.mat - Pointlist of (Pseudo) t-statisic for actual labelling
% XYZ.mat - Co-ordinates of pointlist
% SnPM_ST.mat (*) - Suprathreshold cluster statistics (if required)
%
% Further details of the actual variables required from these files are
% given in the main body of snpm_pp
%
% (*) The SnPM_ST.mat file containing the suprathreshold cluster
% information for each of the relabellings can be very large, and is
% only needed if a suprathreshold cluster size test is required. If
% such an analysis is not required, but suprathreshold cluster stats
% were collected, then this file may be deleted, without compromising
% further voxel-level analyses.
%
%_______________________________________________________________________
% Copyright (C) 2013 The University of Warwick
% Id: snpm_pp.m SnPM13 2013/10/12
% Andrew Holmes, Thomas Nichols, Jun Ding, Darren Gitelman
%-----------------------------functions-called------------------------
% spm_DesMtx
% spm_Tcdf
% spm_clf
% spm_clusters
% spm_figure
% spm_select
% spm_hwrite
% spm_input
% spm_invTcdf
% spm_max
% snpm_mip
% snpm_pp
% spm_str_manip
% spm_t2z
% spm_type
% spm_xyz2e
%-----------------------------functions-called------------------------
%-Variable "decoder" - Following files/variables are required:
%=======================================================================
% NB: Mat files contain additional variables beyond those required here.
% See function that wrote each file for full definitions.
% SnPM design configuration file: SnPMcfg.mat
%-----------------------------------------------------------------------
% - saved by snpm_ui
% H condition partition of DesMtx for correctly labeled data
% C covariate partition of DesMtx for correctly labeled data
% B block partition of DesMtx for correctly labeled data
% G confound partition of DesMtx for correctly labeled data
% HCBGnames string matrix of column names of [H C B G]
% PiCond Permuted conditions matrix, one labelling per row, actual
% labelling on first row
% sPiCond String describing permutations in PiCond
% bhPerms Flag indicating use of "half permutations" trick
% CONT Contrast (only one)
% bVarSm Flag for variance smoothing (Pseudo t-statistics)
% sVarSm Sring describing variance Smoothing (empty if bVarSm=0)
% bST Flag for collection of superthreshold info
% sDesign Description of PlugIn design
%
% SnPM analysis & permutation distribution file: SnPM.mat
%-----------------------------------------------------------------------
% - saved by snpm_cp
% S - Volume analyzed (in voxels)
% V Image file handles (see spm_vol)
% df Residual degrees of freedom of raw t-statistic
% MaxT 2xnPerm matrix of [max;min] t-statistics per perm
% ST_Ut Threshold above which suprathreshold info was collected.
% Voxel locations, t and perm are saved in SnPM_ST.mat for
% t's greater than ST_Ut. ST_Ut=Inf if not saving STCdata
%
% Pointlist of (Pseudo) t-statisic for actual labelling:SnPMt.mat
%-----------------------------------------------------------------------
% - saved by snpm_cp
% SnPMt - 1xS matrix of voxel statistics (t or pseudo-t)
%
% Co-ordinates of pointlist: XYZ.mat
%-----------------------------------------------------------------------
% - saved by snpm_cp
% XYZ - 3xS matrix of co-ordinates [x;y;z] of voxels on SnPMt
%
% Suprathreshold cluster statistics: SnPM_ST.mat
%-----------------------------------------------------------------------
% - saved by snpm_cp
% SnPM_ST - Suprathreshold cluster statistics, see snpm_cp.m
% NB: This file is only required for suprathreshold cluster size analysis
%-Setup
%=======================================================================
global defaults
if isempty(defaults), spm_defaults; end
global SnPMdefs
if isempty(SnPMdefs), snpm_defaults; end
MLver=version;MLver=MLver(1);
fprintf('\nSnPM: snpm_pp\n'),fprintf('%c','='*ones(1,72)),fprintf('\n')
%-Initialise variables & constants
%-----------------------------------------------------------------------
tol = 1e-4; % Tolerance for comparing real numbers
% Two reals with abs(a-b)<tol are considered equal
% ( Reals have to be compared for equality when )
% ( computing FWE-corrected p-values )
Dis = SnPMdefs.Results_distmin; % mm distance between sub regions
Num = SnPMdefs.Results_nbmax; % Maximum number of sub-regions
units = {'mm' 'mm' 'mm'};
%-SetUp figure window
%-----------------------------------------------------------------------
Finter = spm_figure('FindWin','Interactive');
Fgraph = spm_figure('FindWin','Graphics');
if isempty(Fgraph), Fgraph=spm_figure('Create','Graphics'); end
spm_clf(Finter), spm_clf(Fgraph)
set(Finter,'Name','SnPM PostProcess');
%-Get Data
%=======================================================================
% Get analysis directory
if nargin==0
tmp = spm_select(1,'SnPM.mat','Select SnPM.mat for analysis...');
CWD = spm_str_manip(tmp,'hd');
end
if nargin>1
job=varargin{1};
BATCH=true;
else
BATCH=false;
end
%-Skip reports in BATCH; will create them later
if BATCH
Report = {job.Report};
else
% Only can specify multiple reports outside of BATCH mode; otherwise the results will fly by,
% one after another without pausing
Report = {'FWEreport','FDRreport','MIPtable'};
end
%-Load Config file & SnPM permutation data
load(fullfile(CWD,'SnPMcfg'))
load(fullfile(CWD,'SnPM'))
load(fullfile(CWD,'SnPMucp'))
%-Ask whether positive or negative effects be analysed
%-----------------------------------------------------------------------
if BATCH
if STAT == 'T'
bNeg = job.Tsign==-1;
else
bNeg = 0;
end
else
if STAT == 'T'
bNeg = spm_input('Positive or negative effects?',1,'b','+ve|-ve',[0,1],1);
else
bNeg = 0;
str = 'Positive effects';
spm_input('F-statistic, so only:','+1','b',str,1);
end
end
%-Take MaxT for increases or decreases according to bNeg
MaxT = MaxT(:,bNeg+1);
nPerm = size(PiCond,1); %nPerm is consistent with the one in snpm_cp
nPermReal = size(MaxT,1); %different with nPerm when bhPerms==1
[StMaxT, iStMaxT] = sort(MaxT);
%-Load statistic image
%-----------------------------------------------------------------------
load(fullfile(CWD,'SnPMt'))
load(fullfile(CWD,'XYZ'))
XYZ0=XYZ;
%-Negate if looking at negative contrast
%-----------------------------------------------------------------------
if bNeg
SnPMt = -SnPMt;
CONT = -CONT;
end
%-Get ORIGIN, etc
DIM = [V(1).dim(1) V(1).dim(2) V(1).dim(3)];
M=V(1).mat(1:3, 1:3);
VOX=sqrt(diag(M'*M))';
MAT = V(1).mat;
IMAT = inv(MAT);
ORIGIN = IMAT(1:3,4);
% Template vol structure
Vs0 = V(1);
% Vs0 = struct('fname', '',...
% 'dim', [DIM,spm_type('float')],...
% 'mat', MAT,...
% 'pinfo', [1 0 0]',...
% 'descrip', '');
% Process Nonparmaetric P-values
if bNeg
% Here, nPermReal has already been doubled if bhPerms=1
SnPMucp = 1+1/nPermReal-SnPMucp;
end
sSnPMucp = sort(SnPMucp);
%-Write out filtered statistic image? (Get's done later)
%-----------------------------------------------------------------------
if BATCH
if isfield(job.WriteFiltImg,'WF_no')
WrtFlt=0;
else
WrtFlt=1;
WrtFltFn=job.WriteFiltImg.name;
if isempty(spm_str_manip(WrtFltFn, 'e'))
WrtFltFn = [WrtFltFn '.nii'];
end
end
else
WrtFlt = spm_input('Write filtered statistic img?','+1','y/n',[1,0],2);
if WrtFlt
WrtFltFn = 'SnPMt_filtered';
WrtFltFn=spm_input('Filename ?','+1','s',WrtFltFn);
WrtFltFn = [WrtFltFn, '.img'];
end
end
%-Get inference parameters
%=======================================================================
% Map of options from Batch System
% [Root] > .Thr
% Voxel-Level > .Vox
% . Significance > .VoxSig
% . . Uncorrected Nonparametric P | Uncorrected T or F | FDR Corrected | FWE Corrected
% > .Pth .TFth .FDRth .FWEth
% Cluster-Level > .Clus
% . Cluster size statistic > .ClusSize
% . . Cluster-Forming Threshold > .CFth
% . . Significance Level > .ClusSig
% . . . Uncorrected k | FWE Corrected
% > .Cth .FWEthC
%-Get corrected threshold
%-----------------------------------------------------------------------
u = NaN; % Statistic Image threshold
alpha_ucp = NaN; % Uncorrected P-value image threshold
C_MaxT = NaN; % Statistic image threshold set by alph_FWE
C_STCS = NaN; % Cluster size threshold (set directly by uncorrected
% threshold or by alph_FWE)
alph_FWE = NaN; % FWE rate of a specified u threshold
alph_FDR = NaN; % FDR rate of a specified alpha_ucp
if BATCH
bSpatEx = isfield(job.Thr,'Clus');
if ~bSpatEx
% Voxel-wise inference
tmp = fieldnames(job.Thr.Vox.VoxSig);
switch tmp{1}
case 'Pth'
alpha_ucp = BoundCheck(job.Thr.Vox.VoxSig.Pth,[0 1],'Invalid Uncorrected P');
alph_FDR = snpm_P_FDR(alpha_ucp,[],'P',[],sSnPMucp');
case 'TFth'
u = BoundCheck(job.Thr.Vox.VoxSig.TFth,[0 Inf],'Negative Threshold!');
alph_FWE = sum(MaxT > u -tol) / nPermReal;
case 'FDRth'
alph_FDR = BoundCheck(job.Thr.Vox.VoxSig.FDRth,[0 1],'Invalid FDR level');
alpha_ucp = snpm_uc_FDR(alph_FDR,[],'P',[],sSnPMucp');
case 'FWEth'
alph_FWE = BoundCheck(job.Thr.Vox.VoxSig.FWEth,[0 1],'Invalid FWE level');
iFWE = ceil((1-alph_FWE)*nPermReal);
if alph_FWE<1
C_MaxT=StMaxT(iFWE);
else
C_MaxT = 0;
end
u = C_MaxT;
end
else
% Cluster-wise inference
if exist(fullfile(CWD,'SnPM_ST.mat'))~=2 && exist(fullfile(CWD,'STCS.mat'))~=2
error(['SnPM:NoClusterInfo', 'ERROR: Cluster-wise inference requested, but no cluster information saved.\n',...
'Re-configure analysis changing "Cluster inference" to "Yes" and re-run.\n'])
end
%%% Sort out the cluster-forming threshold
if pU_ST_Ut==-1 % No threshold was set in snpm_ui.
if isnan(job.Thr.Clus.ClusSize.CFth)
error('SnPM:NoClusterFormingThresh', 'ERROR: Cluster-forming threshold set to NaN in results with "slow" cluster inference method used in compoutation. \nRe-run results specifying a cluster-forming threshold.\n')
end
% Save original ST_Ut
ST_Ut_0 = ST_Ut;
CFth=job.Thr.Clus.ClusSize.CFth;
if (CFth<=0)
error('SnPM:InvalidClusterFormingThresh', 'ERROR: Cluster-forming threshold must be strictly positive.\nRe-run results with a cluster-forming threshold greater than 0.\n')
end
if bVarSm
%-If using pseudo-statistics then can't use (uncorrected)
% upper tail p-values to specify primary threshold
pCFth = NaN;
if (CFth<1)
warning('snpm_pp:pseudoTFormingThresholdP',...
['Pseudo-T cluster-forming threshold defined by '...
'P-value using Gaussian approximation P=' num2str(pU_ST_Ut)...
' -> Z=' num2str(ST_Ut) '; actual Pseudo-T threshold '...
'unknown but may be higher than ' num2str(ST_Ut) '.']);
pCFth = CFth;
CFth = spm_invNcdf(1-CFth);
% error(sprintf('ERROR: Cluster-forming threshold specified as a P-value (%g), but uncorrected P-values are unavailable for the pseudo t (smoothed variance t-test). \nRe-run results with a cluster-forming threshold greater than 1.\n',ST_Ut))
end
if (CFth < ST_Ut)%(CFth>=ST_Ut-tol)
if isnan(pCFth)
error('SnPM:InvalidClusterFormingThresh', sprintf('ERROR: Cluster-forming threshold of %0.2f specified, but statistic image information only saved for %0.2f and greater. \nRe-run results with a cluster-forming threshold of %0.2f or higher. (Alternatively, increase SnPMdefs.STprop in snpm_defaults.m, re-start SnPM, and re-compute analysis.)\n',CFth,ST_Ut,ST_Ut))
else
error('SnPM:InvalidClusterFormingThresh', sprintf('ERROR: Cluster-forming threshold of P=%0.4f (T=%0.2f) specified, but statistic image information only saved for %0.2f and greater. \nRe-run results with a cluster-forming P-value threshold of %0.2f or lower. (Alternatively, increase SnPMdefs.STalpha in snpm_defaults.m, re-start SnPM, and re-compute analysis.)\n',pCFth,CFth,ST_Ut,p_ST_Ut))
end
end
else
%-Statistic image is t with df degrees of freedom
p_ST_Ut = STalpha;
if (CFth < 1)
pCFth = CFth;
CFth = spm_invTcdf(1-CFth,df);
else
pCFth = NaN;
if (abs(CFth-ST_Ut)<=tol)
CFth=ST_Ut; % If tmp is very close to ST_Ut, set tmp equal to ST_Ut.
end
end
if (CFth < ST_Ut) %(CFth>=ST_Ut-tol)
if isnan(pCFth) % statistic-value cluster-forming threshold
error('SnPM:InvalidClusterFormingThresh', sprintf('ERROR: Cluster-forming threshold of %0.2f specified, but statistic image information only saved for %0.2f and greater. \nRe-run results with a cluster-forming threshold of %0.2f or higher. (Alternatively, increase SnPMdefs.STalpha in snpm_defaults.m, re-start SnPM, and re-compute analysis.)\n',CFth,ST_Ut,ST_Ut))
else
error('SnPM:InvalidClusterFormingThresh', sprintf('ERROR: Cluster-forming threshold of P=%0.4f (T=%0.2f) specified, but statistic image information only saved for %0.2f and greater. \nRe-run results with a cluster-forming P-value threshold of %0.2f or lower. (Alternatively, increase SnPMdefs.STalpha in snpm_defaults.m, re-start SnPM, and re-compute analysis.)\n',pCFth,CFth,ST_Ut,p_ST_Ut))
end
end
end
if (abs(CFth-ST_Ut)<=tol)
CFth = ST_Ut; % If tmp is very close to ST_Ut, set tmp equal to ST_Ut.
end
ST_Ut = CFth;
else % Threshold *was* set in snpm_ui.
if ~isnan(job.Thr.Clus.ClusSize.CFth)
error('SnPM:InvalidClusterFormingThresh', sprintf('ERROR: Cluster-forming threshold of T=%0.2f was already set during analysis configuration; hence, in results, cluster-forming threshold must be left as "NaN".\nRe-run results with cluster-forming threshold set to NaN.\n',ST_Ut))
end
end
u=ST_Ut; % Flag use of a statistic-value threshold
% Inference details...
tmp = fieldnames(job.Thr.Clus.ClusSize.ClusSig);
switch tmp{1}
case 'Cth'
C_STCS = job.Thr.Clus.ClusSize.ClusSig.Cth;
case 'PthC'
alpha_ucp = BoundCheck(job.Thr.Clus.ClusSize.ClusSig.PthC,[0 1],'Invalid uncorrected P(k)');
case 'FWEthC'
alph_FWE = BoundCheck(job.Thr.Clus.ClusSize.ClusSig.FWEthC,[0 1],'Invalid FWE level (cluster-level inference)');
iFWE = ceil((1-alph_FWE)*nPermReal);
end
end % END: Cluster-wise inference
else % GUI, interative inference specification
str_img =[STAT,'|P'];
switch spm_input('Results for which img?','+1','b',str_img,[],1)
case 'P' % Use the P-image
bSpatEx = 0; % Cluster-wise inference won't be performed anyway.
str = 'FDR|None';
switch spm_input('Use corrected threshold?','+1','b',str,[],1)
case 'FDR' % False discovery rate
%---------------------------------------------------------------
alph_FDR = spm_input('FDR-Corrected p value threshold','+0','r',0.05,1,[0,1]);
alpha_ucp = snpm_uc_FDR(alph_FDR,[],'P',[],sSnPMucp');
otherwise %-Uncorrected: no adjustment
%%%% Now ask: Threshold statistic image or Uncorr P-value Image ?
%%%% If stats image, maybe do ST, no FDR-level of thresh;
%%%% If P-value image, no ST, can find FDR-level of thresh
% p for conjunctions is p of the conjunction SPM
%---------------------------------------------------------------
alpha_ucp = spm_input('Uncorrected p value threshold','+0','r',0.01,1,[0,1]);
alph_FDR = snpm_P_FDR(alpha_ucp,[],'P',[],sSnPMucp');
end
case STAT % Use the T-image
%-Ask whether SupraThreshold cluster size test required
%-----------------------------------------------------------------------
%-To have cluster size inference, need
% 1. Spatial extent information was collected (bST=1),
% 2. SnPM_ST.mat or STCS.mat file exists
bSpatEx = bST & (exist(fullfile(CWD,'SnPM_ST.mat'))==2|exist(fullfile(CWD,'STCS.mat'))==2);
if bSpatEx
str = 'Voxelwise|Clusterwise';
bSpatEx = spm_input('Inference method?','+1','b',str,[1 2],1)==2;
else
str = 'Voxelwise';
spm_input('Inference method:','+1','b',str,1);
end
if ~bSpatEx % Voxel-wise inference
str = 'FWE|None';
switch spm_input('Voxelwise: Use corrected thresh?','+1','b',str,[],1)
case 'FWE' % family-wise false positive rate
%---------------------------------------------------------------
alph_FWE = spm_input('FWE-Corrected p value threshold','+0','r',0.05,1,[0,1]);
iFWE=ceil((1-alph_FWE)*nPermReal);
if alph_FWE<1
C_MaxT=StMaxT(iFWE);
else
C_MaxT = 0;
end
u = C_MaxT;
otherwise %-NB: no adjustment
%%%% Now ask: Threshold statistic image or Uncorr P-value Image ?
%%%% If stats image, maybe do ST, no FDR-level of thresh;
%%%% If P-value image, no ST, can find FDR-level of thresh
% p for conjunctions is p of the conjunction SPM
%---------------------------------------------------------------
if bVarSm, str = 'pseudo t'; else, str = sprintf('t_{%d}',df); end
u = spm_input(['threshold (',str,')'],'+0','r',0.01,1,[0,Inf]);
alph_FWE = sum(MaxT > u -tol) / nPermReal;
end
else % Cluster-wise inference
if pU_ST_Ut==-1 % No threshold was set in snpm_ui.
%-Get primary threshold for STC analysis if requested
%-----------------------------------------------------------------------
% Save original ST_Ut
ST_Ut_0 = ST_Ut;
%-Threshold must be greater or equal to that (ST_Ut) used to collect
% suprathreshold data in snpm_cp
%-If a test level alpha has been set, then it there's no sense in having
% the threshold greater than C_MaxT, above which voxels are individually
% significant
tmp = 0;
if bVarSm
%-If using pseudo-statistics then can't use (uncorrected)
% upper tail p-values to specify primary threshold
while ~(tmp>=ST_Ut-tol)
tmp = spm_input(sprintf(...
'Clus-def thresh(pseudo t>%4.2f)',ST_Ut),'+0');
end
if (abs(tmp-ST_Ut)<=tol)
tmp=ST_Ut; % If tmp is very close to ST_Ut, set tmp equal to ST_Ut.
end
else
%-Statistic image is t with df degrees of freedom
p_ST_Ut = STalpha;
while ~( tmp>=ST_Ut-tol || (tmp>0 && tmp<=p_ST_Ut))
tmp = spm_input(sprintf(...
'Clus-def thresh(p<=%4.2fIt>=%4.2f)',p_ST_Ut,ST_Ut),'+0','r',ST_Ut,1);
end
clear p_ST_Ut
if (tmp < 1)
tmp = spm_invTcdf(1-tmp,df);
else
if (abs(tmp-ST_Ut)<=tol)
tmp=ST_Ut; % If tmp is very close to ST_Ut, set tmp equal to ST_Ut.
end
end
end
ST_Ut = tmp;
end
u=ST_Ut; % Flag use of a statistic-value threshold
str = 'FWE|Uncorr';
switch spm_input('Clusterwise: Use corrected thresh?','+1','b',str,[],1)
case 'FWE' % family-wise false positive rate
%---------------------------------------------------------------
alph_FWE = spm_input('FWE-Corrected p value threshold','+0','r',0.05,1,[0,1]);
iFWE=ceil((1-alph_FWE)*nPermReal);
case 'Uncorr' %Uncorrected cluster size threshold
str = 'ClusterSize|P-value';
switch spm_input('Define uncorrected','+1','b',str,[],1)
case 'ClusterSize'
C_STCS = spm_input('Uncorr cluster size threshold','+0','w',0,1);
case 'P-value'
alpha_ucp = spm_input('Uncorrected p value threshold','+0','r',0.01,1,[0,1]);
end
end
end
end
end
% Workflow for statistical Inference:
%
% Results for T or P
%
% 1) P
%
% case 'FDR': alph_FDR is set by spm_input; alpha_ucp is calculated from
% alph_FDR and is used as the threshold.
% case 'None': alpha_ucp is set by spm_input; alph_FDR is calculated from
% alpha_ucp. alpha_ucp is used as the threshold.
%
% 2) T
%
% a) Voxel-wise
% case 'FWE': alph_FWE is set by spm_input; u=C_MaxT; iFWE is
% calculated. u is used as the threshold.
% case 'None': u is set by spm_input; alph_FWE is calculated from u.
% u is used as the threshold.
%
% b) Cluster-wise
% If cluster defining threshold not set, ask for pU_ST_Ut
% case 'FWE': alph_FWE is set by spm_input; iFWE is calculated from
% alph_FWE. C_STCS is calculated from iFWE and is used
% as the threshold.
% case 'Uncorr':
% i) 'ClusterSize', C_STCS is defined directly and used as
% the threshold;
% ii) 'P-value', alpha_ucp is set by spm_input. C_STCS is
% calculated from alpha_ucp and used as the threshold.
%
%=======================================================================
%- C O M P U T A T I O N
%=======================================================================
set(Finter,'Pointer','Watch')
%-Calculate distribution of Maximum Suprathreshold Cluster size
%-Calculate critical Suprathreshold Cluster Size
%=======================================================================
if bSpatEx
fprintf('Working on spatial extent...\n');
%-Compute suprathreshold voxels - check there are some
%---------------------------------------------------------------
fprintf('\tComputing suprathreshold voxels...');
Q = find(SnPMt > ST_Ut);
SnPMt = SnPMt(Q);
XYZ = XYZ(:,Q);
if isempty(Q)
set(Finter,'Pointer','Arrow')
figure(Fgraph)
axis off
text(0,0.97,CWD,'Fontsize',16,'FontWeight','Bold');
str=sprintf('No voxels above threshold %4.2f\n',ST_Ut);
text(0,0.93,str);
fprintf(['WARNING: ' str])
if length(strmatch('FWEreport',Report))>0
ShowDist(MaxT,C_MaxT,alph_FWE,[],[],[],'max');
if ~BATCH
if spm_input('Review permutation distributions.',1,'bd',...
'Print & Continue|Continue',[1,0],1)
spm_print
end
spm_clf(Fgraph)
end
end
if length(strmatch('FDRreport',Report))>0
axis off
text(0,0.97,'Uncorrected P Permutation Distributions','Fontsize',16,'FontWeight',...
'Bold');
ShowDist(SnPMucp,alpha_ucp,alph_FDR,[],[],[],'uncor');
if ~BATCH
if spm_input('Review permutation distributions.',1,'bd',...
'Print|Done',[1,0],1)
spm_print
end
end
end
return
end
fprintf('done\n')
%-Load & condition statistics
%---------------------------------------------------------------
if pU_ST_Ut==-1 % No threshold was set in snpm_ui.
fprintf('\tLoading & conditioning SupraThreshold statistics...');
try
load(fullfile(CWD,'SnPM_ST'))
catch exception
if strcmp(exception.identifier, 'MATLAB:load:unableToReadMatFile')
warning('SnPM:SnPMSTFileNotLOaded', ...
['SnPM_ST file can not be loaded. Consider using' ...
' ''set cluster-forming threshold now (fast)'' option' ...
' in SnPM ''Specify''.']);
end
% Rethrow exception
throw(exception)
end
%-SnPM_ST stores columns of [x;y;z;abs(t);perm] with perm negative
% where the exceedence was t < -ST_Ut_0
%-Trim statistics according to threshold ST_Ut, if ST_Ut > ST_Ut_0
tmp = find(SnPM_ST(4,:)>ST_Ut);
SnPM_ST = SnPM_ST(:,tmp);
clear tmp;
SnPM_ST_Pos = SnPM_ST(:,SnPM_ST(5,:)>0);
SnPM_ST_Neg = SnPM_ST(:,SnPM_ST(5,:)<0);
SnPM_ST_Neg(5,:) = -SnPM_ST_Neg(5,:);
fprintf('done\n')
%-Calculate distribution of Maximum SupraThreshold Cluster size
%---------------------------------------------------------------
fprintf('\tComputing dist. of max SupraThreshold cluster size: ');
STCS = snpm_STcalc('init',nPerm);
fprintf('\nPerms left: ');
for i = nPerm:-1:1
if (rem(i,10)==0)
fprintf('\b\b\b\b%-4u',i)
drawnow
end
if STAT== 'F'
loop = 1;
else
loop = 1:2;
end
for isPos= loop %1 for positive; 2 for negative
if isPos==1
SnPM_ST = SnPM_ST_Pos;
else
SnPM_ST = SnPM_ST_Neg;
end
tQ = (SnPM_ST(5,:)==i);
if any(tQ)
%-Compute cluster labellings for this perm
Locs_mm = SnPM_ST(1:3,tQ);
Locs_mm (4,:) = 1;
Locs_vox = IMAT * Locs_mm;
% Sometimes Locs_vox are not exactly integers and this raises an
% error later in the code. Here check that the values are
% integers with respect to a level of absolute tolerance (~10^-14)
% and enforce Locs_vox to be integers.
% (As in snpm_cp)
diffWithRounded = max(abs(Locs_vox(:)-round(Locs_vox(:))));
tolerance = 10^-10;
if diffWithRounded > tolerance
Locs_vox_alter = MAT\Locs_mm;
diffWithRounded_alter = max(abs(Locs_vox_alter(:)-round(Locs_vox(:))));
error('SnPM:NonIntegerLocsvox', ['''Locs_vox'' must be integers (difference is ' num2str(diffWithRounded) ...
' or ' num2str(diffWithRounded_alter) ')']);
else
Locs_vox = round(Locs_vox);
end
STCS = snpm_STcalc('update',STCS, SnPM_ST(4,tQ),...
Locs_vox(1:3,:),isPos,i,ST_Ut,df);
end
if i==1
%-Save perm 1 stats for use later - [X;Y;Z;T;perm;STCno]
tmp = spm_clusters(Locs_vox(1:3,:));
if isPos==1
STCstats_Pos = [ SnPM_ST(:,tQ); tmp];
if bNeg==0
STCstats=STCstats_Pos;
end
else
STCstats_Neg = [ SnPM_ST(:,tQ); tmp];
if bNeg==1
STCstats=STCstats_Neg;
end
end
end
end
end
fprintf('\b\b\b\bdone\n');
if bhPerms %Double the STCS variables.
STCS = snpm_STcalc('double',STCS);
end
%-Get the stats from STCS structure that will be used later
STCS_MxK = STCS.MxK(:,bNeg+1);
STCS_K=cat(1,STCS.K{:,bNeg+1});
else % A threshold was set in snpm_ui.
%-Load & condition statistics
%---------------------------------------------------------------
fprintf('\tLoading SupraThreshold statistics...');
load(fullfile(CWD,'STCS'))
STCS_MxK = STCS.MxK(:,bNeg+1);
STCS_K=cat(1,STCS.K{:,bNeg+1});
if (bNeg==0)
load(fullfile(CWD,'SnPM_pp'))
else
load(fullfile(CWD,'SnPM_pp_Neg'))
STCstats = STCstats_Neg;
end
end
%-Compute critical SupraThreshold Cluster size
if isnan(C_STCS)
if ~isnan(alph_FWE)
% STCS_MxK: a vector of maximum cluster sizes of all the permutations.
% STCS_MxK = STCS.MxK(:,bNeg+1);
[StMaxSTCS, iStMaxSTCS] = sort(STCS_MxK);
if alph_FWE < 1
C_STCS = StMaxSTCS(iFWE);
else
C_STCS = 0;
end
elseif ~isnan(alpha_ucp)
% STCS_K: a vector of all the cluster sizes of all the
% permutations.
[StKSTCS, iStKSTCS] = sort(STCS_K);
if alpha_ucp < 1
iucp=ceil((1-alpha_ucp)*length(STCS_K));
C_STCS = StKSTCS(iucp);
else
C_STCS = 0;
end
end
end
%-Check XYZ for points > ST_Ut in perm 1 matches
% XYZ computed above for SnPMt > ST_Ut
%if pU_ST_Ut==-1
% if ~all(all( SnPM_ST(1:3,SnPM_ST(5,:)==1) == XYZ ))
% error('SnPM:InvalidSTXYZ', 'ST XYZ don''t match between STCS & thresh')
% end
%else
if ~all(all( STCstats(1:3,:) == XYZ ))
error('SnPM:InvalidSTXYZ', 'ST XYZ don''t match between STCS & thresh')
end
%end
end
%-Save some time consuming results
%-----------------------------------------------------------------------
if bSpatEx && pU_ST_Ut==-1
save SnPM_pp.mat STCstats_Pos
if STAT == 'T'
save SnPM_pp_Neg.mat STCstats_Neg
end
save STCS.mat STCS
end
%-Filter data at specified corrected p-value alpha
%=======================================================================
if bSpatEx
%-Analysing spatial extent