Home > marsbar > @mardo_99 > fill.m

fill

PURPOSE ^

fills missing entries from SPM FMRI design matrix

SYNOPSIS ^

function D = fill(D, actions)

DESCRIPTION ^

 fills missing entries from SPM FMRI design matrix 
 FORMAT D = fill(D, actions)
 
 D          - mardo object containing spm design
 actions    - string or cell array of strings with actions:
            'defaults' - fills empty parts of design with defaults
            (in fact this is always done)
            'filter'  - asks for and fills filter, autocorrelation 
            'for_estimation - fills ready for estimation -> 'filter'
                  if there is no filter
            'images'  - asks for and fills with images, mask, scaling

 Returns
 D         - returned mardo SPM design

 Copied/pasted then rearranged from SPM99 spm_fmri_spm_ui
 Matthew Brett - 17/11/01 - MRS2TH

 $Id$

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 function D = fill(D, actions)
0002 % fills missing entries from SPM FMRI design matrix
0003 % FORMAT D = fill(D, actions)
0004 %
0005 % D          - mardo object containing spm design
0006 % actions    - string or cell array of strings with actions:
0007 %            'defaults' - fills empty parts of design with defaults
0008 %            (in fact this is always done)
0009 %            'filter'  - asks for and fills filter, autocorrelation
0010 %            'for_estimation - fills ready for estimation -> 'filter'
0011 %                  if there is no filter
0012 %            'images'  - asks for and fills with images, mask, scaling
0013 %
0014 % Returns
0015 % D         - returned mardo SPM design
0016 %
0017 % Copied/pasted then rearranged from SPM99 spm_fmri_spm_ui
0018 % Matthew Brett - 17/11/01 - MRS2TH
0019 %
0020 % $Id$
0021 
0022 if nargin < 2
0023   actions = '';
0024 end
0025 if ~is_fmri(D), return, end
0026 if isempty(actions), actions = {'defaults'}; end
0027 if ischar(actions), actions = {actions}; end
0028 fe = find(ismember(actions, 'for_estimation'));
0029 if ~isempty(fe)
0030   A = [];
0031   if is_fmri(D)
0032     if ~has_filter(D), A = {'filter'}; end
0033   end
0034   actions(fe) = [];
0035   actions = [actions(1:fe(1)-1) A actions(fe(1):end)];
0036 end
0037 actions = [{'defaults'}, actions];
0038 
0039 % Get design, put into some useful variables
0040 spmD = des_struct(D);
0041 xX = spmD.xX;
0042 if isfield(spmD, 'Sess')
0043   have_sess = 1;
0044   Sess = spmD.Sess;
0045 else
0046   have_sess = 0;
0047 end
0048 
0049 % get file indices
0050 %---------------------------------------------------------------
0051 row = block_rows(D);
0052 nsess  = length(row);
0053 nscan  = zeros(1,nsess);
0054 for  i = 1:nsess
0055   nscan(i) = length(row{i});
0056 end
0057 
0058 done_list = {};
0059 for a = 1:length(actions)
0060   if ismember(actions{a}, done_list), continue, end
0061   done_list = [actions(a) done_list];
0062   switch lower(actions{a})
0063    case 'defaults'
0064 
0065     % prepare various default settings, offer to design
0066     xM = [];             % masking
0067     xGX = [];            % globals
0068     sGXcalc  = 'none';   % global calculation description
0069     sGMsca   = 'none';   % grand mean scaling description
0070     Global   = 'None';   % proportional scaling or no
0071  
0072     BFstr = ''; DSstr = ''; ntr = [];
0073     if have_sess
0074       BFstr  = Sess{1}.BFstr;
0075       DSstr  = Sess{1}.DSstr;
0076       if ~length(xX.iC)
0077     DSstr = 'Block [session] effects only';
0078       end
0079       
0080       % Number of trial types per session
0081       for i = 1:length(Sess)
0082     ntr(i) = length(Sess{i}.name);
0083       end
0084     end
0085     
0086     xsDes = struct( 'Design',            DSstr,...
0087             'Basis_functions',        BFstr,...
0088             'Number_of_sessions',    sprintf('%d',nsess),...
0089             'Conditions_per_session',    sprintf('%-3d',ntr),...
0090             'Global_calculation',    sGXcalc,...
0091             'Grand_mean_scaling',    sGMsca,...
0092             'Global_normalisation',    Global);
0093 
0094     if isfield(spmD, 'xsDes')
0095       xsDes = mars_struct('fillafromb', spmD.xsDes, xsDes);
0096     end
0097     
0098     spmD.xsDes = xsDes;
0099     spmD = mars_struct('merge', spmD, ...
0100                struct('xGX', xGX,...
0101                   'xM',  xM));
0102                   
0103    case 'images'
0104     [Finter,Fgraph,CmdLine] = spm('FnUIsetup','fMRI stats model setup',0);
0105     % select images into matrix P
0106     P = '';
0107     if nsess < 16
0108       for i = 1:nsess
0109     str = sprintf('select scans for session %0.0f',i);
0110     q = spm_get(nscan(i),mars_veropts('get_img_ext'),str);
0111     P   = strvcat(P,q);
0112       end
0113     else
0114       str   = sprintf('select scans for this study');
0115       P     = spm_get(sum(nscan),mars_veropts('get_img_ext'),str);
0116     end
0117     
0118     % Assemble other design parameters
0119     %=======================================================================
0120     spm_help('!ContextHelp',mfilename)
0121     spm_input('Global intensity normalisation...',1,'d',mfilename);
0122     
0123     % Global normalization
0124     %-----------------------------------------------------------------------
0125     str    = 'remove Global effects';
0126     Global = spm_input(str,'+1','scale|none',{'Scaling' 'None'});
0127     if ischar(Global),
0128       Global = {Global};
0129     end
0130 
0131     % get file identifiers and Global values
0132     %=======================================================================
0133     fprintf('%-40s: ','Mapping files')                                   %-#
0134     VY     = spm_vol(P);
0135     fprintf('%30s\n','...done')                                          %-#
0136     
0137     %-Check compatability of images
0138     %-----------------------------------------------------------------------
0139     [samef msg] = mars_vol_check(VY);
0140     if ~samef, disp(char(msg)),error('Cannot use images'),end;
0141     
0142     %-Compute Global variate
0143     %-------------------------------------------------------------------
0144     GM     = 100;
0145     q      = sum(nscan);
0146     g      = zeros(q,1);
0147     fprintf('%-40s: %30s','Calculating globals',' ');                     
0148     for i  = 1:q
0149       fprintf('%s%30s',repmat(sprintf('\b'),1,30),sprintf('%4d/%-4d',i,q));
0150       g(i) = spm_global(VY(i));
0151     end
0152     fprintf('%s%30s\n',repmat(sprintf('\b'),1,30),'...done')             
0153     
0154     % scale if specified (otherwise session specific grand mean scaling)
0155     %------------------------------------------------------------------
0156     gSF     = GM./g;
0157     if strcmp(Global,'None')
0158       for i = 1:nsess
0159     j      = row{i};
0160     gSF(j) = GM./mean(g(j));
0161       end
0162     end
0163     
0164     %-Apply gSF to memory-mapped scalefactors to implement scaling
0165     %---------------------------------------------------------------
0166     for  i = 1:q, VY(i).pinfo(1:2,:) = VY(i).pinfo(1:2,:)*gSF(i); end
0167     
0168     sGXcalc  = 'mean voxel value';
0169     sGMsca   = 'session specific';
0170     
0171     %-Masking structure
0172     %---------------------------------------------------------------
0173     xM     = struct('T',    ones(q,1),...
0174             'TH',    g.*gSF,...
0175             'I',    0,...
0176             'VM',    {[]},...
0177             'xs',    struct('Masking','analysis threshold'));
0178     
0179     % Global structure
0180     xGX.iGXcalc  = Global{1};
0181     xGX.sGXcalc  = sGXcalc;
0182     xGX.rg       = g;
0183     xGX.sGMsca   = sGMsca;
0184     xGX.GM       = GM;
0185     xGX.gSF      = gSF;
0186     
0187     xsDes = struct(...
0188     'Global_calculation',        sGXcalc,...
0189     'Grand_mean_scaling',        sGMsca,...
0190     'Global_normalisation',        Global);
0191       
0192     spmD.xsDes = mars_struct('ffillmerge',...
0193                  spmD.xsDes,...
0194                  xsDes);
0195     spmD = mars_struct('ffillmerge', ...
0196                spmD,...
0197                struct('xGX', xGX,...
0198                   'VY',   VY,...
0199                   'xM',   xM));
0200 
0201    case 'filter'
0202     % Get filter and autocorrelation options
0203     if ~have_sess, return, end
0204 
0205     [Finter,Fgraph,CmdLine] = spm('FnUIsetup','Set FMRI filter',0);
0206     
0207     % TR if not set (it should be)
0208     if ~isfield(xX, 'RT')
0209           spmD.xX.RT  = spm_input('Interscan interval {secs}','+1');
0210     end
0211     
0212     spm_input('High and low pass filter','+1','d',mfilename)
0213     [spmD.xX.K HFstr LFstr] = pr_get_filter(spmD.xX.RT, Sess);
0214     xsDes = struct(...
0215     'Interscan_interval',    sprintf('%0.2f',spmD.xX.RT),...
0216     'High_pass_Filter',     LFstr,...
0217     'Low_pass_Filter',      HFstr);
0218     spmD.xsDes = mars_struct('ffillmerge',...
0219                  spmD.xsDes,...
0220                  xsDes);
0221 
0222    case 'autocorr'
0223     [Finter,Fgraph,CmdLine] = spm('FnUIsetup','FMRI autocorrelation model',0);
0224     % intrinsic autocorrelations (Vi)
0225     %-----------------------------------------------------------------------
0226     str     = 'Model intrinsic correlations?';
0227     cVimenu = {'none','AR(1)'};
0228     cVi     = spm_input(str,'+1','b',cVimenu);
0229     
0230     % create Vi struct
0231     %-----------------------------------------------------------------------
0232     Vi      = speye(sum(nscan));
0233     xVi     = struct('Vi',Vi,'Form',cVi);
0234     for   i = 1:nsess
0235       xVi.row{i} = row{i};
0236     end
0237     
0238     % fill into design
0239     spmD.xX.xVi = xVi;
0240     spmD.xsDes.Intrinsic_correlations = cVi;
0241     
0242    otherwise
0243     error(['Unpredictable: ' actions{a}]);
0244   end
0245 end
0246 
0247 % put stuff into object
0248 D = des_struct(D,spmD);
0249

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