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swe_list.m
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swe_list.m
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function varargout = swe_list(varargin)
% Display an analysis of an SwE parametric map
% =========================================================================
% FORMAT TabDat = swe_list('List',xSwE,hReg,[Num,Dis,Str])
% Summary list of local maxima for entire volume of interest
% xSwE - Structure containing data (format as below)
% hReg - Handle of caller (not used)
% Num - number of maxima per cluster [3]
% Dis - distance among clusters {mm} [8]
% Str - header string
%
% FORMAT TabDat = swe_list('ListCluster',xSwE,hReg,[Num,Dis,Str])
% List of local maxima for a single suprathreshold cluster
% xSwE - Structure containing data (format as below)
% hReg - Handle of caller (not used)
% Num - number of maxima per cluster [3]
% Dis - distance among clusters {mm} [8]
% Str - header string
%
% FORMAT swe_list('TxtList',TabDat,c)
% Prints a tab-delimited text version of the table
% TabDat - Structure containing table data (format as below)
% c - Column of table data to start text table at
% (E.g. c=3 doesn't print set-level results contained in columns 1
% & 2)
%
% FORMAT swe_list('SetCoords',xyz,hAx,hReg)
% Highlighting of table co-ordinates (used by results section registry)
% xyz - 3-vector of new co-ordinate
% hAx - table axis (the registry object for tables)
% hReg - Handle of caller (not used)
% -------------------------------------------------------------------------
% xSwE - structure containing SPM, distribution & filtering details
% - required fields are:
% .Z - minimum of n Statistics {filtered on u and k}
% .n - number of conjoint tests
% .STAT - distribution {Z, T, X or F}
% .df - degrees of freedom [df{interest}, df{residual}]
% .u - height threshold
% .k - extent threshold {voxels}
% .XYZ - location of voxels {voxel coords}
% .S - search Volume {voxels}
% .R - search Volume {resels}
% .FWHM - smoothness {voxels}
% .M - voxels - > mm matrix
% .VOX - voxel dimensions {mm}
% .DIM - image dimensions {voxels}
% .units - space units
% .VRpv - filehandle - Resels per voxel
% .Ps - uncorrected P values in searched volume (for voxel FDR)
% .Pp - uncorrected P values of peaks (for peak FDR)
% .Pc - uncorrected P values of cluster extents (for cluster FDR)
% .uc - 0.05 critical thresholds for FWEp, FDRp, FWEc, FDRc
% Note: .Ps, .Pp, .Pc and .uc may be specified as empty, i.e. []
% .thresDesc - description of height threshold (string)
%
% (see spm_getSPM.m for further details of xSwE structures)
%
% hReg - Handle of results section XYZ registry (see swe_results_ui.m)
%
% Num - number of maxima per cluster [3]
% Dis - distance among clusters {mm} [8]
% Str - header string
%
% TabDat - Structure containing table data
% - fields are
% .tit - table title (string)
% .hdr - table header (2x11 cell array)
% .fmt - fprintf format strings for table data (1x11 cell array)
% .str - table filtering note (string)
% .ftr - table footnote information (5x2 cell array)
% .dat - table data (Nx11 cell array)
%
% ----------------
%
% FORMAT swe_list('TxtList',TabDat,c)
% Prints a tab-delimited text version of the table
% TabDat - Structure containing table data (format as above)
% c - Column of table data to start text table at
% (E.g. c=3 doesn't print set-level results contained in columns 1 & 2)
% ----------------
%
% FORMAT swe_list('SetCoords',xyz,hAx,hReg)
% Highlighting of table co-ordinates (used by results section registry)
% xyz - 3-vector of new co-ordinate
% hAx - table axis (the registry object for tables)
% hReg - Handle of caller (not used)
%__________________________________________________________________________
%
% swe_list characterizes SwE data (thresholded at u and k) in terms of
% excursion sets (a collection of face, edge and vertex connected subsets
% or clusters). The corrected significance of the results are based on
% set, cluster and voxel-level inferences using distributional
% approximations from the Theory of Gaussian Fields and/or common bootstrap
% methods. These distributions assume that the SwE map is a reasonable
% lattice approximation of a continuous random field with known component
% field smoothness.
%
% The p values are based on the probability of obtaining c, or more,
% clusters of k, or more, resels above u, in the volume S analysed =
% P(u,k,c). For specified thresholds u, k, the set-level inference is
% based on the observed number of clusters C, = P(u,k,C). For each
% cluster of size K the cluster-level inference is based on P(u,K,1)
% and for each voxel (or selected maxima) of height U, in that cluster,
% the voxel-level inference is based on P(U,0,1). All three levels of
% inference are supported with a tabular presentation of the p values
% and the underlying statistic:
%
% Set-level - c = number of suprathreshold clusters
% - P = prob(c or more clusters in the search volume)
%
% Cluster-level - k = number of voxels in this cluster
% - Pc = prob(k or more voxels in the search volume)
% - Pu = prob(k or more voxels in a cluster)
% - Qc = lowest FDR bound for which this cluster would be
% declared positive
%
% Peak-level - T/F = Statistic upon which the SwE data is based
% - Ze = The equivalent Z score - prob(Z > Ze) = prob(t > T)
% - Pc = prob(Ze or higher in the search volume)
% - Qp = lowest FDR bound for which this peak would be
% declared positive
% - Pu = prob(Ze or higher at that voxel)
%
% Voxel-level - Qu = Expd(Prop of false positives among voxels >= Ze)
%
% x,y,z (mm) - Coordinates of the voxel
%
% Note: For wild bootstrap settings the following will not be available:
% - Cluster-level: Pu and Qc
% - Set-level: P
%
% The table is grouped by regions and sorted on the Ze-variate of the
% primary maxima. Ze-variates (based on the uncorrected p value) are the
% Z score equivalent of the statistic. Volumes are expressed in voxels.
%
% Clicking on values in the table returns the value to the MATLAB
% workspace. In addition, clicking on the co-ordinates jumps the
% results section cursor to that location. The table has a context menu
% (obtained by right-clicking in the background of the table),
% providing options to print the current table as a text table, or to
% extract the table data to the MATLAB workspace.
%
%_________________________________________________________________________
% Version Info: $Format:%ci$ $Format:%h$
%==========================================================================
switch lower(varargin{1}), case 'list' %-List
%==========================================================================
% FORMAT TabDat = swe_list('List',xSwE,hReg,[Num,Dis,Str])
%-Parse arguments
%----------------------------------------------------------------------
if nargin < 2, error('Not enough input arguments.'); end
if nargin < 3, hReg = []; else hReg = varargin{3}; end
xSwE = varargin{2};
%-Extract results table and display it
%----------------------------------------------------------------------
spm('Pointer','Watch')
TabDat = swe_list('Table',xSwE,varargin{4:end});
swe_list('Display',TabDat,hReg);
spm('Pointer','Arrow')
%-Return TabDat structure
%----------------------------------------------------------------------
varargout = { TabDat };
%==========================================================================
case 'table' %-Table
%==========================================================================
% FORMAT TabDat = swe_list('table',xSwE,[Num,Dis,Str])
%-Parse arguments
%----------------------------------------------------------------------
if nargin < 2, error('Not enough input arguments.'); end
xSwE = varargin{2};
%-Get number of maxima per cluster to be reported
%----------------------------------------------------------------------
if length(varargin) > 2, Num = varargin{3}; else Num = spm_get_defaults('stats.results.volume.nbmax'); end
%-Get minimum distance among clusters (mm) to be reported
%----------------------------------------------------------------------
if length(varargin) > 3, Dis = varargin{4}; else Dis = spm_get_defaults('stats.results.volume.distmin'); end
%-Get header string
%----------------------------------------------------------------------
if length(varargin) > 4 && ~isempty(varargin{5})
Title = varargin{5};
else
if xSwE.STAT ~= 'P'
Title = 'p-values adjusted for search volume';
else
Title = 'Posterior Probabilities';
end
end
%-Extract data from xSwE
%----------------------------------------------------------------------
isCifti = xSwE.isCifti;
S = xSwE.S;
VOX = xSwE.VOX;
DIM = xSwE.DIM;
M = xSwE.M;
XYZ = xSwE.XYZ;
Z = xSwE.Z;
% VRpv = xSwE.VRpv;
n = xSwE.n;
STAT = xSwE.STAT;
switch STAT
case 'T'
STATe = 'Z';
case 'F'
STATe = 'X';
end
u = xSwE.u;
k = xSwE.k;
try, uc = xSwE.uc; end
try, QPs = xSwE.Ps; end
try, QPp = xSwE.Pp; end
try, QPc = xSwE.Pc; end
% For WB analyses we have already calculated the information for the
% table and footer. We just need to read it in.
if xSwE.WB
VspmUncP = swe_data_read(xSwE.VspmUncP);
VspmFDRP = swe_data_read(xSwE.VspmFDRP);
VspmFWEP = swe_data_read(xSwE.VspmFWEP);
% If the user didn't originally select clusterwise inference,
% clusterwise FWEP values will not have been calculated.
if isfield(xSwE, 'VspmFWEP_clus')
VspmFWEP_clus = swe_data_read(xSwE.VspmFWEP_clus);
else
VspmFWEP_clus = [];
end
if isfield(xSwE, 'VspmFWEP_clusnorm')
VspmFWEP_clusnorm = swe_data_read(xSwE.VspmFWEP_clusnorm);
else
VspmFWEP_clusnorm = [];
end
end
% if STAT~='P'
% R = full(xSwE.R);
% FWHM = full(xSwE.FWHM);
% end
try
units = xSwE.units;
catch
units = {'mm' 'mm' 'mm'};
end
units{1} = [units{1} ' '];
units{2} = [units{2} ' '];
if ~spm_mesh_detect(xSwE.Vspm)
DIM = DIM > 1; % non-empty dimensions
strDataType = 'voxels';
else
DIM = true(1,3);
strDataType = 'vertices';
end
D = sum(DIM); % highest dimension
VOX = VOX(DIM); % scaling
% if STAT ~= 'P'
% FWHM = FWHM(DIM); % Full width at half max
% FWmm = FWHM.*VOX; % FWHM {units}
% V2R = 1/prod(FWHM); % voxels to resels
% k = k*V2R; % extent threshold in resels
% R = R(1:(D + 1)); % eliminate null resel counts
% end
try, QPs = sort(QPs(:)); end % Needed for voxel FDR
try, QPp = sort(QPp(:)); end % Needed for peak FDR
try, QPc = sort(QPc(:)); end % Needed for cluster FDR
% Choose between voxel-wise and topological FDR
%----------------------------------------------------------------------
topoFDR = false; %to be checked
%-Tolerance for p-value underflow, when computing equivalent Z's
%----------------------------------------------------------------------
tol = eps*10;
%-Table Headers
%----------------------------------------------------------------------
TabDat.tit = Title;
% If we are doing a clusterwise/voxelwise analysis the header is the
% normal SPM header.
if isCifti
additionalField = {'brain structure','label','label'};
nColTable = 12;
else
additionalField = {};
nColTable = 11;
end
if ~isfield(xSwE, 'TFCEanaly') || ~xSwE.TFCEanaly
TabDat.hdr = {...
'set', 'p', '\itp';...
'set', 'c', '\itc';...
'cluster', 'p(FWE-corr)', '\itp\rm_{FWE-corr}';...
% 'cluster', 'p(FDR-corr)', '\itq\rm_{FDR-corr}';...
'cluster', 'equivk', '\itk\rm_E';...
'cluster', 'equivkArea', '\it{area}';...
'cluster', 'equivkZ', '\itk\rm_{Z}';...
% 'cluster', 'p(unc)', '\itp\rm_{uncorr}';...
'peak', 'p(FWE-corr)', '\itp\rm_{FWE-corr}';...
'peak', 'p(FDR-corr)', '\itq\rm_{FDR-corr}';...
'peak', STATe, sprintf('\\it%c',STATe);...
'peak', 'p(unc)', '\itp\rm_{uncorr}';...
' ', 'x,y,z {mm}', [units{:}];...
additionalField{:} }';
% Otherwise we need a TFCE section in the table instead of a cluster
% level section.
else
TabDat.hdr = {...
'set', 'p', '\itp';...
'set', 'c', '\itc';...
'TFCE', 'p(FWE-corr)', '\itp\rm_{FWE-corr}';...
'TFCE', '', '';...
'TFCE', 'equivk', '\itk\rm_E';...
'TFCE', '', '';...
'peak', 'p(FWE-corr)', '\itp\rm_{FWE-corr}';...
'peak', 'p(FDR-corr)', '\itq\rm_{FDR-corr}';...
'peak', STATe, sprintf('\\it%c',STATe);...
'peak', 'p(unc)', '\itp\rm_{uncorr}';...
' ', 'x,y,z {mm}', [units{:}];...
additionalField{:} }';
end
%-Coordinate Precisions
%----------------------------------------------------------------------
if isempty(xSwE.XYZmm) || isCifti % empty results or cifti
xyzfmt = '%3.0f %3.0f %3.0f';
voxfmt = '%1.1f %1.1f %1.1f';
elseif ~any(strcmp(units{3},{'mm',''})) % 2D data
xyzfmt = '%3.0f %3.0f %3.0f';
voxfmt = '%1.1f %1.1f %1.1f';
else % 3D data, work out best precision based on voxel sizes and FOV
xyzsgn = min(xSwE.XYZmm(DIM,:),[],2) < 0;
xyzexp = max(floor(log10(max(abs(xSwE.XYZmm(DIM,:)),[],2)))+(max(abs(xSwE.XYZmm(DIM,:)),[],2) >= 1),0);
voxexp = floor(log10(abs(VOX')))+(abs(VOX') >= 1);
xyzdec = max(-voxexp,0);
voxdec = max(-voxexp,1);
xyzwdt = xyzsgn+xyzexp+(xyzdec>0)+xyzdec;
voxwdt = max(voxexp,0)+(voxdec>0)+voxdec;
tmpfmt = cell(size(xyzwdt));
for i = 1:numel(xyzwdt)
tmpfmt{i} = sprintf('%%%d.%df ', xyzwdt(i), xyzdec(i));
end
xyzfmt = [tmpfmt{:}];
tmpfmt = cell(size(voxwdt));
for i = 1:numel(voxwdt)
tmpfmt{i} = sprintf('%%%d.%df ', voxwdt(i), voxdec(i));
end
voxfmt = [tmpfmt{:}];
end
TabDat.fmt = {'%-0.3f','%g',... %-Set
'%0.3f', '%0.0f','%0.2f','%0.3f',... %-Cluster
'%0.3f', '%0.3f', '%6.2f', '%0.3f',... %-Peak
xyzfmt, '%s'}; %-XYZ
%-Table filtering note
%----------------------------------------------------------------------
if isinf(Num)
TabDat.str = sprintf('table shows all local maxima > %.1fmm apart',Dis);
else
TabDat.str = sprintf(['table shows %d local maxima ',...
'more than %.1fmm apart'],Num,Dis);
end
%-Footnote with SPM parameters
%----------------------------------------------------------------------
if strcmp(STAT, 'T')
Pz = 1-spm_Ncdf(u);
eSTAT = 'Z';
else
Pz = 1-spm_Xcdf(u, 1);
eSTAT = 'X';
end
% Create footer for display.
TabDat.ftr = cell(6,2);
% Number of `extra` lines inserted that don't have to be present in
% every display
exlns = 0;
% detect whether the WB was done based on Z/X or on T/F using the version number (2.1.1 was the last using T/F)
if swe_compareVersions(swe('ver'), '2.1.1', '>')
displaySTAT = eSTAT;
else
displaySTAT = STAT;
end
if xSwE.WB
% Record thresholds.
% For voxel-wise FDR and unc. thresholding, we cannot display a score threshold as it varies per voxel
td = regexp(xSwE.thresDesc,'p\D+?(?<u>[\.\d]+) \((?<thresDesc>\S+)\)','names');
if xSwE.infType == 0 % voxel-wise
if strcmp(td.thresDesc, 'FWE')
TabDat.ftr{1,1} = ['Threshold: Height ' displaySTAT ' > %0.2f, p <= %0.3f (FWE); Extent k >= %0.0f ' strDataType '.'];
TabDat.ftr{1,2} = [u, str2num(td.u), k];
elseif strcmp(td.thresDesc, 'FDR')
TabDat.ftr{1,1} = ['Threshold: p <= %0.3f (FDR); Extent k >= %0.0f ' strDataType '.'];
TabDat.ftr{1,2} = [str2num(td.u), k];
elseif strcmp(td.thresDesc, 'unc.')
TabDat.ftr{1,1} = ['Threshold: p <= %0.3f (unc.); Extent k >= %0.0f ' strDataType '.'];
TabDat.ftr{1,2} = [str2num(td.u), k];
else
error('Unknown inference type detected')
end
elseif xSwE.infType == 1 % cluster-wise
if strcmp(xSwE.clustWise, 'FWE')
if strcmpi(xSwE.clusterSizeType, 'Box-Cox norm. k_{Z}')
TabDat.ftr{1,1} = ['Threshold: Height ' eSTAT ' > %0.2f, p < %0.3f (unc.); k_{Z} > %0.3f, p <= %0.3f (FWE).'];
TabDat.ftr{1,2} = [u, str2num(td.u), k, xSwE.fwep_c];
elseif strcmpi(xSwE.clusterSizeType, 'classic k_E')
TabDat.ftr{1,1} = ['Threshold: Height ' eSTAT ' > %0.2f, p < %0.3f (unc.); Extent k > %0.0f ' strDataType ', p <= %0.3f (FWE).'];
TabDat.ftr{1,2} = [u, str2num(td.u), k, xSwE.fwep_c];
else
error('Unknow type of cluster statistics');
end
elseif strcmp(xSwE.clustWise, 'Uncorr')
TabDat.ftr{1,1} = ['Threshold: p <= %0.3f (unc.); Extent k >= %0.0f ' strDataType '.'];
TabDat.ftr{1,2} = [str2num(td.u), k];
else
error('Unknown inference type detected')
end
elseif xSwE.infType == 2 % TFCE
TabDat.ftr{1,1} = 'Threshold: TFCE %s';
TabDat.ftr{1,2} = xSwE.thresDesc;
else
error('Unknown inference type detected')
end
% We need the P uncorrected P values to be in the correct form to
% use spm_uc_FDR.
% Make sure to load only the in-mask data to avoid loading zero values
Ts = swe_data_read(xSwE.VspmUncP, 'xyz', xSwE.XYZ_inMask);
Ts(isnan(Ts)) = [];
Ts = 10.^-Ts;
Ts = sort(Ts(:));
% Obtain the FDR p 0.05 value.
FDRp_05 = swe_uc_FDR(0.05,Inf,'P',n,Ts);
clear Ts
% Record FWE/FDR/clus FWE p values. (No clus FWE for voxelwise and
% TFCE analyses)
if xSwE.infType == 1 && strcmp(xSwE.clustWise, 'FWE')
TabDat.ftr{2,1} = ...
['vox ' displaySTAT '(5%% FWE): %0.3f, vox P(5%% FDR): %0.3f, clus k(5%% FWE): %0.0f '];
TabDat.ftr{2,2} = [xSwE.Pfv, FDRp_05, xSwE.Pfc];
else
TabDat.ftr{2,1} = ...
['vox ' displaySTAT '(5%% FWE): %0.3f, vox P(5%% FDR): %0.3f'];
TabDat.ftr{2,2} = [xSwE.Pfv, FDRp_05];
end
else
% Record height thresholds.
TabDat.ftr{1,1} = ...
['Threshold: Height ' eSTAT ' = %0.2f, p = %0.3f; Extent k = %0.0f ' strDataType '.'];
TabDat.ftr{1,2} = [u,Pz,k];
% Record FDR p value.
TabDat.ftr{2,1} = ...
'vox P(5%% FDR): %0.3f';
TabDat.ftr{2,2} = swe_uc_FDR(0.05,Inf,'P',n,sort(xSwE.Ps)');
end
if xSwE.infType == 1 && strcmp(xSwE.clustWise, 'FWE') && isfield(xSwE, 'boxcoxInfo')
TabDat.ftr{(3+exlns),1} = 'k_{Z} = 0.6745 (k_{\\lambda} - Q2(k_{\\lambda}^{H0})) / (Q3(k_{\\lambda}^{H0})-Q2(k_{\\lambda}^{H0}))';
% TabDat.ftr{(3+exlns),1} = 'Null cluster sizes in surfaces: \\lambda_S=%0.2f , \\lambda_V =%0.2f';
% TabDat.ftr{(3+exlns),1} = 'Box-Cox(Surf): \lambda=%0.2f, mean=%0.2f, std=%0.2f, median=%0.2f, 2(Q3-Q2)=%0.2f';
TabDat.ftr{(3+exlns),2} = [];
if isfield(xSwE.boxcoxInfo, 'surfaces') && isfield(xSwE.boxcoxInfo, 'volume')
tmpSurf = xSwE.boxcoxInfo.surfaces;
tmpVol = xSwE.boxcoxInfo.volume;
TabDat.ftr{(4+exlns),1} = 'Box-Cox parameters for cluster sizes under H0: \\lambda(Surfaces)=%0.2f, \\lambda(Volume)=%0.2f';
TabDat.ftr{(4+exlns),2} = [tmpSurf.lambda, tmpVol.lambda];
elseif isfield(xSwE.boxcoxInfo, 'surfaces')
tmpSurf = xSwE.boxcoxInfo.surfaces;
TabDat.ftr{(4+exlns),1} = 'Box-Cox parameters for cluster sizes under H0: \\lambda(Surfaces)=%0.2f';
TabDat.ftr{(4+exlns),2} = [tmpSurf.lambda];
elseif isfield(xSwE.boxcoxInfo, 'volume')
tmpVol = xSwE.boxcoxInfo.volume;
TabDat.ftr{(4+exlns),1} = 'Box-Cox parameters for cluster sizes under H0: \\lambda(Volume)=%0.2f';
TabDat.ftr{(4+exlns),2} = [tmpVol.lambda];
else
error('Unknown Box-Cox Info!')
end
exlns = exlns + 2;
end
% If we have groups display group details in ftr.
if isfield(xSwE, 'nSubj_g')
% Record number of subjects per group.
nSubjString = 'Number of subjects: ';
for i = 1:length(xSwE.nSubj_g)
nSubjString = [nSubjString '%0.0f'];
if i ~= length(xSwE.nSubj_g)
nSubjString = [nSubjString ', '];
else
nSubjString = [nSubjString '; '];
end
end
% Record visits per group.
nVisitsString = 'Number of visits ([Min Max]): ';
nVisitsNumbers = [];
for i = 1:length(xSwE.max_nVis_g)
nVisitsString = [nVisitsString '[%0.0f %0.0f]'];
if i ~= length(xSwE.max_nVis_g)
nVisitsString = [nVisitsString ', '];
end
nVisitsNumbers = [nVisitsNumbers xSwE.min_nVis_g(i) xSwE.max_nVis_g(i)];
end
TabDat.ftr{3+exlns,1} = [nSubjString nVisitsString];
TabDat.ftr{3+exlns,2} = [xSwE.nSubj_g nVisitsNumbers];
exlns = exlns + 1;
end
% Retrieve edf data
if isfield(xSwE, 'Vedf')
edf = swe_data_read(xSwE.Vedf, 'xyz', xSwE.XYZ_inMask);
else
edf = xSwE.edf;
end
edf(isnan(edf)) = [];
edf_max = max(edf);
edf_min = min(edf);
edf_med = median(edf);
% Work out range of dof values
diff = abs(edf_max - edf_min);
% Work out dofType
switch xSwE.dofType
case 0
dofTypeStr = 'Naive';
case 1
dofTypeStr = 'Approx I';
case 2
dofTypeStr = 'Approx II';
case 3
dofTypeStr = 'Approx III';
otherwise
error('Unknown degrees of freedom.')
end
% Recording effective Degrees of freedom
if xSwE.dofType~=0 && diff > 10^-10
TabDat.ftr{(3+exlns),1}=['Error DF: (' dofTypeStr '): (min) %0.1f, (median) %0.1f, (max) %0.1f'];
TabDat.ftr{(3+exlns),2}=[edf_min, edf_med, edf_max];
else
TabDat.ftr{(3+exlns),1}=['Error DF: (' dofTypeStr '): %0.1f'];
TabDat.ftr{(3+exlns),2}=edf_med;
end
% Record small sample adjustments.
TabDat.ftr{(4+exlns),1}='Resid. Adj.: %s';
switch xSwE.SS
case {0, 1, 2, 3}
TabDat.ftr{(4+exlns),2} = ['Type ' num2str(xSwE.SS)];
case {4, 5}
TabDat.ftr{(4+exlns),2} = ['Type C' num2str(xSwE.SS - 2)];
otherwise
error('Unknown SS type')
end
% Record contrast degrees of freedom.
TabDat.ftr{(5+exlns),1} = 'Contrast DF: %0.0f; Number of predictors: %0.0f';
TabDat.ftr{(5+exlns),2} = [xSwE.df_Con xSwE.nPredict];
% Record volume.
if isCifti
TabDat.ftr{(6+exlns),1} = ...
['Surface(s): %0.0f vertices; Volume: %0.0f voxels'];
TabDat.ftr{(6+exlns),2} = [xSwE.S_surf, xSwE.S_vol];
elseif spm_mesh_detect(xSwE.Vspm)
TabDat.ftr{(6+exlns),1} = ...
['Surface: %0.0f ' strDataType ''];
TabDat.ftr{(6+exlns),2} = [S];
else
TabDat.ftr{(6+exlns),1} = ...
['Volume: %0.0f ' units{:} ' = %0.0f ' strDataType ''];
TabDat.ftr{(6+exlns),2} = [S*prod(VOX),S];
end
% Record voxel sizes.
if isCifti && numel(xSwE.cifti.volume) > 0
TabDat.ftr{(7+exlns),1} = ...
['Voxel size: %1.1f %1.1f %1.1f mm mm mm'];
TabDat.ftr{(7+exlns),2} = sqrt(diag(xSwE.cifti.volume.M(1:3,1:3)'*xSwE.cifti.volume.M(1:3,1:3)))';
elseif isCifti && numel(xSwE.cifti.volume) == 0
exlns = exlns - 1;
elseif ~spm_mesh_detect(xSwE.Vspm)
TabDat.ftr{(7+exlns),1} = ...
['Voxel size: ' voxfmt units{:}];
TabDat.ftr{(7+exlns),2} = VOX;
else
exlns = exlns - 1;
end
if isfield(xSwE, 'TFCEanaly') && xSwE.TFCEanaly
TabDat.ftr{(8+exlns),1} = 'TFCE: E=%0.1f, H=%0.1f';
TabDat.ftr{(8+exlns),2} = [xSwE.TFCE.E, xSwE.TFCE.H];
exlns = exlns + 1;
end
if xSwE.WB
% Recording number of bootstraps.
TabDat.ftr{(8+exlns),1}='Bootstrap samples = %0.0f';
TabDat.ftr{(8+exlns),2}= xSwE.nB;
exlns = exlns + 1;
end
%-Characterize excursion set in terms of maxima
% (sorted on Z values and grouped by regions)
%----------------------------------------------------------------------
if isempty(Z)
TabDat.dat = cell(0,nColTable);
varargout = {TabDat};
return
end
%-Workaround in spm_max for conjunctions with negative thresholds
%----------------------------------------------------------------------
minz = abs(min(min(Z)));
Z = 1 + minz + Z;
if xSwE.infType == 1 && strcmp(xSwE.clustWise, 'FWE') && isfield(xSwE, 'boxcoxInfo')
boxcoxInfo = xSwE.boxcoxInfo;
else
boxcoxInfo = [];
end
if isCifti
[N, N_area, N_boxcox, Z, XYZ, A, L, XYZmm, brainStructureShortLabels] = ...
swe_cifti_max(Z,XYZ(1,:), xSwE.cifti, boxcoxInfo);
elseif ~spm_mesh_detect(xSwE.Vspm)
[N, N_boxcox, Z, XYZ, A, L] = swe_max(Z, XYZ, boxcoxInfo);
N_area = [];
else
[N, N_area, N_boxcox, Z, XYZ, A, L] = spm_mesh_max(Z, XYZ, xSwE.G, boxcoxInfo);
end
Z = Z - minz - 1;
%-Convert cluster sizes from voxels (N) to resels (K)
%----------------------------------------------------------------------
c = max(A); %-Number of clusters
NONSTAT = spm_get_defaults('stats.rft.nonstat');
%-Convert maxima locations from voxels to mm
%----------------------------------------------------------------------
if isCifti
% nothing as it was done in swe_cifti_max above
elseif spm_mesh_detect(xSwE.Vspm)
XYZmm = xSwE.G.vertices(XYZ(1,:),:)';
else
XYZmm = M(1:3,:)*[XYZ; ones(1,size(XYZ,2))];
end
%-Set-level p values {c} - do not display if reporting a single cluster
%----------------------------------------------------------------------
% if STAT ~= 'P'
% Pc = spm_P(c,k,u,df,STAT,R,n,S); %-Set-level p-value
% else
Pc = [];
% end
TabDat.dat = {Pc,c};
TabLin = 1;
%-Cluster and local maxima p-values & statistics
%----------------------------------------------------------------------
while numel(find(isfinite(Z)))
%-Find largest remaining local maximum
%------------------------------------------------------------------
[U,i] = max(Z); %-largest maxima
mj = find(A == A(i)); %-maxima in cluster
if ~xSwE.WB
switch STATe
case 'Z'
try
Pz = normcdf(-U);
catch
Pz = spm_Ncdf(-U);
end
case 'X'
try
Pz = 1-chi2cdf(U,1);
catch
Pz = 1-spm_Xcdf(U,1);
end
end
else
Pz = 10.^-VspmUncP(XYZ(1,i),XYZ(2,i),XYZ(3,i));
end
% If we are not running a wild bootstrap or we are doing a
% small volume correction we need to calculate the FDR P value
% and leave the other values blank.
if ~xSwE.WB || isfield(xSwE,'svc')
Pu = [];
Pk = [];
Pn = [];
Qc = [];
Qp = [];
Qu = spm_P_FDR(U,[1 1],STATe,n,QPs); % voxel FDR-corrected
ws = warning('off','SPM:outOfRangeNormal');
warning(ws);
% If we are running a wild bootstrap we only need to read in
% results we calculated earlier.
else
Pu = 10.^-VspmFWEP(XYZ(1,i),XYZ(2,i),XYZ(3,i));
Qu = 10.^-VspmFDRP(XYZ(1,i),XYZ(2,i),XYZ(3,i));
Pn = [];
Qc = [];
Qp = [];
ws = warning('off','SPM:outOfRangeNormal');
warning(ws);
if xSwE.infType == 1 && strcmp(xSwE.clustWise, 'FWE') % only for FWER clusterwise WB
if strcmpi(xSwE.clusterSizeType, 'Box-Cox norm. k_{Z}')
Pk = 10.^-VspmFWEP_clusnorm(XYZ(1,i),XYZ(2,i),XYZ(3,i));
elseif strcmpi(xSwE.clusterSizeType, 'classic k_E')
Pk = 10.^-VspmFWEP_clus(XYZ(1,i),XYZ(2,i),XYZ(3,i));
else
error('Unknow type of cluster statistics');
end
% It is possible to get the results window to display
% details about voxels that were thresholded out by the
% cluster-forming threshold used for the wild bootstrap.
% These regions will have NaN for the cluster FWE P-value
% when they should have one. So the below is necessary:
Pk(isnan(Pk)) = 1;
elseif xSwE.TFCEanaly
% Get coordinates of all voxels in the current cluster.
currentClus = find(A == A(i));
XYZ_clus = XYZ(:, currentClus);
% Read in all TFCE FWE P values in this cluser
tfp = 10.^-swe_data_read(xSwE.VspmTFCEFWEP, 'xyz', XYZ_clus);
% Record the minimum TFCE FWE P value in said cluster.
Pk = min(tfp);
else
Pk = [];
end
end
if i > numel(N_area) % means that this is for volume or there is no area info
N_area_tmp = [];
else
N_area_tmp = N_area(i);
end
if i > numel(N_boxcox)
N_boxcox_tmp = [];
else
N_boxcox_tmp = N_boxcox(i);
end
[TabDat.dat{TabLin,3:11}] = deal(Pk, N(i), N_area_tmp, N_boxcox_tmp,Pu,Qu,U,Pz,XYZmm(:,i));
if isCifti
[TabDat.dat{TabLin, 12}] = char(brainStructureShortLabels(i));
end
TabLin = TabLin + 1;
%-Print Num secondary maxima (> Dis mm apart)
%------------------------------------------------------------------
[l,q] = sort(-Z(mj)); % sort on Z value
D = i;
for i = 1:length(q)
d = mj(q(i));
if min(sqrt(sum((XYZmm(:,D)-repmat(XYZmm(:,d),1,size(D,2))).^2)))>Dis
if length(D) < Num
% voxel-level p values {Z}
%------------------------------------------------------
% if STAT ~= 'P'
% Pz = spm_P(1,0,Z(d),df,STAT,1,n,S);
% Pu = spm_P(1,0,Z(d),df,STAT,R,n,S);
% if topoFDR
% Qp = spm_P_peakFDR(Z(d),df,STAT,R,n,u,QPp);
% Qu = [];
% else
% Qu = spm_P_FDR(Z(d),df,STATe,n,QPs);
% Qp = [];
% end
% if Pz < tol
% Ze = Inf;
% else
% Ze = swe_invNcdf(1 - Pz);
% end
% else
% If we are not running a wild bootstrap or if we are
% doing a small volume correction we need to calculate
% the FDR P value and leave the other values blank.
if ~xSwE.WB || isfield(xSwE,'svc')
Pz = spm_Ncdf(-Z(d));
Pu = [];
Qu = [];
Qp = [];
Qu = spm_P_FDR(Z(d),[1 1],STATe,n,QPs); % voxel FDR-corrected
ws = warning('off','SPM:outOfRangeNormal');
Ze = swe_invNcdf(Z(d));
warning(ws);
% end
% If we are running a wild bootstrap we only need to read in
% results we calculated earlier.
else
Pz = 10.^-VspmUncP(XYZ(1,d),XYZ(2,d),XYZ(3,d));
Pu = 10.^-VspmFWEP(XYZ(1,d),XYZ(2,d),XYZ(3,d));
Qu = 10.^-VspmFDRP(XYZ(1,d),XYZ(2,d),XYZ(3,d));
ws = warning('off','SPM:outOfRangeNormal');
Ze = swe_invNcdf(Z(d));
warning(ws);
end
D = [D d];
if topoFDR
[TabDat.dat{TabLin,7:11}] = ...
deal(Pu,Qp,Z(d),Pz,XYZmm(:,d));
else
[TabDat.dat{TabLin,7:11}] = ...
deal(Pu,Qu,Z(d),Pz,XYZmm(:,d));
end
TabLin = TabLin+1;
end
end
end
Z(mj) = NaN; % Set local maxima to NaN
end
varargout = {TabDat};
%======================================================================
case 'display' %-Display table in Graphics window
%======================================================================
% FORMAT swe_list('display',TabDat,hReg)
%-Parse arguments
%----------------------------------------------------------------------
if nargin < 2, error('Not enough input arguments.');
else TabDat = varargin{2}; end
if nargin < 3, hReg = []; else hReg = varargin{3}; end
isCifti = (size(TabDat.hdr,2) == 12);
if isCifti
scalingFactor = 0.9;
nColTable = 12;
else
scalingFactor = 1;
nColTable = 11;
end
%-Get current location (to highlight selected voxel in table)
%----------------------------------------------------------------------
xyzmm = swe_results_ui('GetCoords');
%-Setup Graphics panel
%----------------------------------------------------------------------
Fgraph = spm_figure('FindWin','Satellite');
if ~isempty(Fgraph)
spm_figure('Focus', Fgraph);
ht = 0.85; bot = 0.14;
else
Fgraph = spm_figure('GetWin','Graphics');
ht = 0.4; bot = 0.1;
end
swe_results_ui('Clear',Fgraph)
FS = spm('FontSizes'); %-Scaled font sizes
PF = spm_platform('fonts'); %-Font names (for this platform)
%-Table axes & Title
%----------------------------------------------------------------------
hAx = axes('Parent',Fgraph,...
'Position',[0.025 bot 0.9 ht],...
'DefaultTextFontSize',FS(8),...
'DefaultTextInterpreter','Tex',...
'DefaultTextVerticalAlignment','Baseline',...
'Tag','SPMList',...
'Units','points',...
'Visible','off');
try
hRotate3d = rotate3d(Fgraph);
setAllowAxesRotate(hRotate3d, hAx, false);
end
AxPos = get(hAx,'Position'); set(hAx,'YLim',[0,AxPos(4)])
dy = FS(9);
y = floor(AxPos(4)) - dy;
text(0,y,['Statistics: \it\fontsize{',num2str(FS(9)),'}',TabDat.tit],...
'FontSize',FS(11),'FontWeight','Bold'); y = y - dy/2;
line([0 1],[y y],'LineWidth',3,'Color','r'), y = y - 9*dy/8;
%-Display table header
%----------------------------------------------------------------------
set(gca,'DefaultTextFontName',PF.helvetica,'DefaultTextFontSize',FS(8))
Hs = []; Hc = []; Hp = [];
h = text(0.01*scalingFactor,y, [TabDat.hdr{1,1} '-level'],'FontSize',FS(9));
h = line([0,0.11]*scalingFactor,[1,1]*(y-dy/4),'LineWidth',0.5,'Color','r');
h = text(0.02*scalingFactor,y-9*dy/8, TabDat.hdr{3,1}); Hs = [Hs,h];
h = text(0.08*scalingFactor,y-9*dy/8, TabDat.hdr{3,2}); Hs = [Hs,h];
text(0.22*scalingFactor,y, [TabDat.hdr{1,3} '-level'],'FontSize',FS(9));
line([0.14,0.44]*scalingFactor,[1,1]*(y-dy/4),'LineWidth',0.5,'Color','r');
h = text(0.15*scalingFactor,y-9*dy/8, TabDat.hdr{3,3}); Hc = [Hc,h];
h = text(0.24*scalingFactor,y-9*dy/8, TabDat.hdr{3,4}); Hc = [Hc,h];
h = text(0.31*scalingFactor,y-9*dy/8, TabDat.hdr{3,5}); Hc = [Hc,h];
h = text(0.39*scalingFactor,y-9*dy/8, TabDat.hdr{3,6}); Hc = [Hc,h];
text(0.59*scalingFactor,y, [TabDat.hdr{1,8} '-level'],'FontSize',FS(9));
line([0.48,0.80]*scalingFactor,[1,1]*(y-dy/4),'LineWidth',0.5,'Color','r');
h = text(0.49*scalingFactor,y-9*dy/8, TabDat.hdr{3,7}); Hp = [Hp,h];
h = text(0.58*scalingFactor,y-9*dy/8, TabDat.hdr{3,8}); Hp = [Hp,h];
h = text(0.67*scalingFactor,y-9*dy/8, TabDat.hdr{3,9}); Hp = [Hp,h];
h = text(0.74*scalingFactor,y-9*dy/8, TabDat.hdr{3,10}); Hp = [Hp,h];
text(0.845*scalingFactor,y - dy/2,TabDat.hdr{3,11},'Fontsize',FS(8));
if isCifti
text(0.88,y - dy/2,TabDat.hdr{1,12},'Fontsize',FS(8));
end
%-Move to next vertical position marker
%----------------------------------------------------------------------
y = y - 7*dy/4;
line([0 1],[y y],'LineWidth',1,'Color','r')
y = y - 5*dy/4;
y0 = y;
%-Table filtering note
%----------------------------------------------------------------------
text(0.5,4,TabDat.str,'HorizontalAlignment','Center',...
'FontName',PF.helvetica,'FontSize',FS(8),'FontAngle','Italic')
%-Footnote with SPM parameters (if classical inference)
%----------------------------------------------------------------------
line([0 1],[0 0],'LineWidth',1,'Color','r')
if ~isempty(TabDat.ftr)
set(gca,'DefaultTextFontName',PF.helvetica,...
'DefaultTextInterpreter','Tex','DefaultTextFontSize',FS(8))
fx = repmat([0 0.645],ceil(size(TabDat.ftr,1)/2),1);
fy = repmat((1:ceil(size(TabDat.ftr,1)/2))',1,2);
for i=1:size(TabDat.ftr,1)
text(fx(i),-fy(i)*dy,sprintf(TabDat.ftr{i,1},TabDat.ftr{i,2}),...
'UserData',TabDat.ftr{i,2},...
'ButtonDownFcn','get(gcbo,''UserData'')');
end
end
%-Characterize excursion set in terms of maxima
% (sorted on Z values and grouped by regions)
%======================================================================
if isempty(TabDat.dat)
text(0.5,y-6*dy,'no suprathreshold clusters',...
'HorizontalAlignment','Center',...
'FontAngle','Italic','FontWeight','Bold',...
'FontSize',FS(16),'Color',[1,1,1]*.5);
return
end
%-Table proper
%======================================================================
%-Column Locations