function varargout=spm_spm_ui(varargin) % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % cbuhacked version - Ian Nimmo-Smith, 3-11 June 1999 % % v0.2 handling blank lines - thanks to Joe Devlin % % [1] design and filename entry from a .csv file [done] % [2] saving design and filename details in design.csv [done] % [3] inputting covariate values from the .csv file [done] % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Setting up the general linear model for independent data % FORMATs (given in Programmers Help) %_______________________________________________________________________ % % spm_spm_ui.m configures the design matrix (describing the general % linear model), data specification, and other parameters necessary for % the statistical analysis. These parameters are saved in a % configuration file (SPMcfg.mat) in the current directory, and are % passed on to spm_spm.m which estimates the design. Inference on these % estimated parameters is then handled by the SPM results section. % % A separate program (spm_spm_fmri_ui.m) handles design configuration % for fMRI time series, though this program can be used for fMRI data % when observations can be regarded as independent. % % ---------------------------------------------------------------------- % % Various data and parameters need to be supplied to specify the design: % * the image files % * indicators of the corresponding condition/subject/group % * any covariates, nuisance variables, or design matrix partitions % * the type of global normalisation (if any) % * grand mean scaling options % * thresholds and masks defining the image volume to analyse % % The interface supports a comprehensive range of options for all these % parameters, which are described below in the order in which the % information is requested. Rather than ask for all these parameters, % spm_spm_ui.m uses a "Design Definition", a structure describing the % options and defaults appropriate for a particular analysis. Thus, % once the user has chosen a design, a subset of the following prompts % will be presented. % % ---------------- % % Design class & Design type % ========================== % % Unless a design definition is passed to spm_spm_ui.m as a parameter, % the user is prompted first to select a design class, and then to % select a design type from that class. % % The designs are split into three classes: % i) Basic stats: basic models for simple statistics % These specify designs suitable for simple voxel-by-voxel analyses. % - one-sample t-test % - two-sample t-test % - paired t-test % - one way Anova (ANalysis Of VAriance) % - simple regression (equivalent to correlation) % - multiple regression % - basic AnCova (ANalysis of COVAriance) % (essentially a two-sample t-test witha nuisance covariate) % % ii) PET models: models suitable for analysis of PET/SPECT experiments % - Single-subject: conditions & covariates % - Single-subject: covariates only % % - Multi-subj: conditions & covariates % - Multi-subj: cond x subj interaction & covariates % - Multi-subj: covariates only % - Multi-group: conditions & covariates % - Multi-group: covariates only % % - Population main effect: 2 cond's, 1 scan/cond (paired t-test) % - Dodgy population main effect: >2 cond's, 1 scan/cond % - Compare-populations: 1 scan/subject (two sample t-test) % % - The Full Monty... (asks you everything!) % % iii) SPM96 PET models: models used in SPM96 for PET/SPECT % These models are provided for backward compatibility, but as they % don't include some of the advanced modelling features, we recommend % you switch to the new (SPM99) models at the earliest opportunity. % - SPM96:Single-subject: replicated conditions % - SPM96:Single-subject: replicated conditions & covariates % - SPM96:Single-subject: covariates only % - SPM96:Multi-subject: different conditions % - SPM96:Multi-subject: replicated conditions % - SPM96:Multi-subject: different conditions & covariates % - SPM96:Multi-subject: replicated conditions & covariates % - SPM96:Multi-subject: covariates only % - SPM96:Multi-group: different conditions % - SPM96:Multi-group: replicated conditions % - SPM96:Multi-group: different conditions & covariates % - SPM96:Multi-group: replicated conditions & covariates % - SPM96:Multi-group: covariates only % - SPM96:Compare-groups: 1 scan per subject % % % NB: Random effects, generalisability, population inference... % % Note that SPM only considers a single component of variance, the % residual error variance. When there are repeated measures, all % analyses with SPM are fixed effects analyses, and inference only % extends to the particular subjects under consideration (at the times % they were imaged). % % In particular, the multi-subject and multi-group designs ignore the % variability in response from subject to subject. Since the % scan-to-scan (within-condition, within-subject variability is much % smaller than the between subject variance which is ignored), this can % lead to detection of group effects that are not representative of the % population(s) from which the subjects are drawn. This is particularly % serious for multi-group designs comparing two groups. If inference % regarding the population is required, a random effects analysis is % required. % % However, random effects analyses can be effected by appropriately % summarising the data, thereby collapsing the model such that the % residual variance for the new model contains precisely the variance % components needed for a random effects analysis. In many cases, the % fixed effects models here can be used as the first stage in such a % two-stage procedure to produce appropriate summary data, which can % then be used as raw data for a second-level analysis. For instance, % the "Multi-subj: cond x subj interaction & covariates" design can be % used to write out an image of the activation for each subject. A % simple t-test on these activation images then turns out to be % equivalent to a mixed-effects analysis with random subject and % subject by condition interaction effects, inferring for the % population based on this sample of subjects (strictly speaking the % design would have to be balanced, with equal numbers of scans per % condition per subject, and also only two conditions per subject). For % further details, see spm_RandFX.man. % % ---------------- % % Selecting image files & indicating conditions % ============================================= % % You may now be prompted to specify how many studies, subjects and % conditions you have, and then will be promted to select the scans. % % The data should all have the same orientation and image and voxel size. % % File selection is handled by spm_get.m - the help for which describes % efficient use of the interface. % % You may be asked to indicate the conditions for a set of scans, with % a prompt like "[12] Enter conditions? (2)". For this particular % example you need to indicate for 12 scans the corresponding % condition, in this case from 2 conditions. Enter a vector of % indicators, like '0 1 0 1...', or a string of indicators, like % '010101010101' or '121212121212', or 'rararararara'. (This % "conditions" input is handled by spm_input.m, where comprehensive % help can be found.) % % ---------------- % % Covariate & nuisance variable entry % =================================== % % * If applicable, you'll be asked to specify covariates and nuisance % variables. Unlike SPM94/5/6, where the design was partitioned into % effects of interest and nuisance effects for the computation of % adjusted data and the F-statistic (which was used to thresh out % voxels where there appeared to be no effects of interest), SPM99 does % not partition the design in this way. The only remaining distinction % between effects of interest (including covariates) and nuisance % effects is their location in the design matrix, which we have % retained for continuity. Pre-specified design matrix partitions can % be entered. (The number of covariates / nuisance variables specified, % is actually the number of times you are prompted for entry, not the % number of resulting design matrix columns.) You will be given the % opportunity to name the covariate. % % * Factor by covariate interactions: For covariate vectors, you may be % offered a choice of interaction options. (This was called "covariate % specific fits" in SPM95/6.) The full list of possible options is: % - % - with replication % - with condition (across group) % - with subject (across group) % - with group % - with condition (within group) % - with subject (within group) % % * Covariate centering: At this stage may also be offered "covariate % centering" options. The default is usually that appropriate for the % interaction chosen, and ensures that main effects of the interacting % factor aren't affected by the covariate. You are advised to choose % the default, unless you have other modelling considerations. The full % list of possible options is: % - around overall mean % - around replication means % - around condition means (across group) % - around subject means (across group) % - around group means % - around condition means (within group) % - around subject means (within group) % - % % ---------------- % % Global options % ============== % % Depending on the design configuration, you may be offered a selection % of global normalisation and scaling options: % % * Method of global flow calculation % - SPM96:Compare-groups: 1 scan per subject % - None (assumming no other options requiring the global value chosen) % - User defined (enter your own vector of global values) % - SPM standard: mean voxel value (within per image fullmean/8 mask) % % * Grand mean scaling : Scaling of the overall grand mean simply % scales all the data by a common factor such that the mean of all the % global values is the value specified. For qualitative data, this puts % the data into an intuitively accessible scale without altering the % statistics. When proportional scaling global normalisation is used % (see below), each image is seperately scaled such that it's global % value is that specified (in which case the grand mean is also % implicitly scaled to that value). When using AnCova or no global % normalisation, with data from different subjects or sessions, an % intermediate situation may be appropriate, and you may be given the % option to scale group, session or subject grand means seperately. The % full list of possible options is: % - scaling of overall grand mean % - caling of replication grand means % - caling of condition grand means (across group) % - caling of subject grand means (across group) % - caling of group grand means % - caling of condition (within group) grand means % - caling of subject (within group) grand means % - implicit in PropSca global normalisation) % - no grand Mean scaling>' % % * Global normalisation option : Global nuisance effects are usually % accounted for either by scaling the images so that they all have the % same global value (proportional scaling), or by including the global % covariate as a nuisance effect in the general linear model (AnCova). % Much has been written on which to use, and when. Basically, since % proportional scaling also scales the variance term, it is appropriate % for situations where the global measurement predominantly reflects % gain or sensitivity. Where variance is constant across the range of % global values, linear modelling in an AnCova approach has more % flexibility, since the model is not restricted to a simple % proportional regression. % % Considering AnCova global normalisation, since subjects are unlikely % to have the same relationship between global and local measurements, % a subject-specific AnCova ("AnCova by subject"), fitting a different % slope and intercept for each subject, would be preferred to the % single common slope of a straight AnCova. (Assumming there's enough % scans per subject to estimate such an effect.) This is basically an % interaction of the global covariate with the subject factor. You may % be offered various AnCova options, corresponding to interactions with % various factors according to the design definition: The full list of % possible options is: % - AnCova % - AnCova by replication % - AnCova by condition (across group) % - AnCova by subject (across group) % - AnCova by group % - AnCova by condition (within group) % - AnCova by subject (within group) % - Proportional scaling % - % % Since differences between subjects may be due to gain and sensitivity % effects, AnCova by subject could be combined with "grand mean scaling % by subject" to obtain a combination of between subject proportional % scaling and within subject AnCova. % % * Global centering: Lastly, for some designs using AnCova, you will % be offered a choice of centering options for the global covariate. As % with covariate centering, this is only relevant if you have a % particular interest in the parameter estimates. Usually, the default % of a centering corresponding to the AnCova used is chosen. The full % list of possible options is: % - around overall mean % - around replication means % - around condition means (across group) % - around subject means (across group) % - around group means % - around condition means (within group) % - around subject means (within group) % - % - around user specified value % - (as implied by AnCova) % - GM (The grand mean scaled value) % - (redundant: not doing AnCova) % % % % Note that this is a logical ordering for the global options, which is % not the order used by the interface due to algorithm constraints. The % interface asks for the options in this order: % - Global normalisation % - Grand mean scaling options % (if not using proportional scaling global normalisation) % - Value for grand mean scaling proportional scaling GloNorm % (if appropriate) % - Global centering options % - Value for global centering (if "user-defined" chosen) % - Method of calculation % % ---------------- % % Masking options % =============== % % The mask specifies the voxels within the image volume which are to be % assessed. SPM supports three methods of masking. The volume analysed % is the intersection of all masks: % % i) Threshold masking : "Analysis threshold" % - images are thresholded at a given value and only voxels at % which all images exceed the threshold are included in the % analysis. % - The threshold can be absolute, or a proportion of the global % value (after scaling), or "-Inf" for no threshold masking. % - (This was called "Grey matter threshold" in SPM94/5/6) % % ii) Implicit masking % - An "implicit mask" is a mask implied by a particular voxel % value. Voxels with this mask value are excluded from the % analysis. % - For image data-types with a representation of NaN % (see spm_type.m), NaN's is the implicit mask value, (and % NaN's are always masked out). % - For image data-types without a representation of NaN, zero is % the mask value, and the user can choose whether zero voxels % should be masked out or not. % % iii) Explicit masking % - Explicit masks are other images containing (implicit) masks % that are to be applied to the current analysis. % - All voxels with value NaN (for image data-types with a % representation of NaN), or zero (for other data types) are % excluded from the analysis. % - Explicit mask images can have any orientation and voxel/image % size. Nearest neighbour interpolation of a mask image is used if % the voxel centers of the input images do not coincide with that % of the mask image. % % ---------------------------------------------------------------------- % % Variables saved in the SPMcfg.mat file % D - design definition structure % (See definition in main body of spm_spm_ui.m) % VY - nScan x 1 struct array of memory mapped input images % (see spm_vol for definition of the map structure) % % xX - structure describing design matrix % xX.I - nScan x 4 matrix of factor level indicators % I(n,i) is the level of factor i corresponding to image n % xX.sF - 1x4 cellstr containing the names of the four factors % D.sF{i} is the name of factor i % xX.X - desgin matrix % xX.iH - vector of H partition (condition effects) indices, % identifying columns of X correspoding to H % xX.iC - vector of C partition (covariates of interest) indices % xX.iB - vector of B partition (block effects) indices % xX.iG - vector of G partition (nuisance variables) indices % xX.Xnames - p x 1 cellstr of effect names corresponding to columns % of the design matrix % % xC - structure array of covariate details % xC(i).rc - raw (as entered) i-th covariate % xC(i).rcname - name of this covariate (string) % xC(i).c - covariate as appears in design matrix (after any scaling, % centering of interactions) % xC(i).cname - cellstr containing names for effects corresponding to % columns of xC(i).c % xC(i).iCC - covariate centering option % xC(i).iCFI - covariate by factor interaction option % xC(i).type - covariate type: 1=interest, 2=nuisance, 3=global % xC(i).cols - columns of design matrix corresponding to xC(i).c % xC(i).descrip - cellstr containing a description of the covariate % % xGX - structure describing global options and values % xGX.iGXcalc - global calculation option used % xGX.sGXcalc - string describing global calculation used % xGX.rg - raw globals (before scaling and such like) % xGX.iGMsca - grand mean scaling option % xGX.sGMsca - string describing grand mean scaling % xGX.GM - value for grand mean (/proportional) scaling % xGX.gSF - global scaling factor (applied to xGX.rg) % xGX.iGC - global covariate centering option % xGX.sGC - string describing global covariate centering option % xGX.gc - center for global covariate % xGX.iGloNorm - Global normalisation option % xGX.sGloNorm - string describing global normalisation option % % xM - structure describing masking options % xM.T - Threshold masking value (-Inf=>None, % complex=>proportional (i.e. times global), real=>absolute ) % xM.TH - nScan x 1 vector of analysis thresholds, one per image % xM.I - Implicit masking (0=>none, 1=>implicit zero/NaN mask) % xM.VM - struct array of explicit mask images % (empty if no explicit masks) % xM.xs - structure describing masking options % (format is same as for xsDes described below) % % xsDes - structure of strings describing the design: % Fieldnames are essentially topic strings (use "_"'s for % spaces), and the field values should be strings or cellstr's % of information regarding that topic. spm_DesRep.m % uses this structure to produce a printed description % of the design, displaying the fieldnames (with "_"'s % converted to spaces) in bold as topics, with % the corresponding text to the right % % F_iX0 - Design matrix columns of effects of no interest % (For F-test) % (Used in spm_spm.m for filtering data saved for plotting) % % SPMid - String identifying SPM and program versions %_______________________________________________________________________ % 99/06/08 Andrew Holmes @(#)spm_spm_ui.m 2.26 SCCSid = '2.26'; %======================================================================= % - FORMAT specifications for programers %======================================================================= %( This is a multi function function, the first argument is an action ) %( string, specifying the particular action function to take. ) % % FORMAT spm_spm_ui('CFG',D) % Configure design % D - design definition structure - see format definition below % (If D is a struct array, then the user is asked to choose from the % design definitions in the array. If D is not specified as an % argument, then user is asked to choose from the standard internal % definitions) % % FORMAT [P,I] = spm_spm_ui('Files&Indices',DsF,Dn,DbaTime) % PET/SPECT file & factor level input % DsF - 1x4 cellstr of factor names (ie D.sF) % Dn - 1x4 vector indicating the number of levels (ie D.n) % DbaTime - ask for F3 images in time order, with F2 levels input by user? % P - nScan x 1 cellsrt of image filenames % I - nScan x 4 matrix of factor level indices % % FORMAT D = spm_spm_ui('DesDefs_Stats') % Design definitions for simple statistics % D - struct array of design definitions (see definition below) % % FORMAT D = spm_spm_ui('DesDefs_PET') % Design definitions for PET/SPECT models % D - struct array of design definitions (see definition below) % % FORMAT D = spm_spm_ui('DesDefs_PET96') % Design definitions for SPM96 PET/SPECT models % D - struct array of design definitions (see definition below) %======================================================================= % Design definitions specification for programers & power users %======================================================================= % Within spm_spm_ui.m, a definition structure, D, determines the % various options, defaults and specifications appropriate for a % particular design. Usually one uses one of the pre-specified % definitions chosen from the menu, which are specified in the function % actions at the end of the program (spm_spm_ui('DesDefs_Stats'), % spm_spm_ui('DesDefs_PET'), spm_spm_ui('DesDefs_PET96')). For % customised use of spm_spm_ui.m, the design definition structure is % shown by the following example: % % D = struct(... % 'DesName','The Full Monty...',... % 'n',[Inf Inf Inf Inf], 'sF',{{'repl','cond','subj','group'}},... % 'Hform', 'I(:,[4,2]),''-'',{''stud'',''cond''}',... % 'Bform', 'I(:,[4,3]),''-'',{''stud'',''subj''}',... % 'nC',[Inf,Inf],'iCC',{{[1:8],[1:8]}},'iCFI',{{[1:7],[1:7]}},... % 'iGXcalc',[1,2,3],'iGMsca',[1:7],'GM',50,... % 'iGloNorm',[1:9],'iGC',[1:11],... % 'M_',struct('T',[-Inf,0,0.8*sqrt(-1)],'I',Inf,'X',Inf),... % 'b',struct('aTime',1)); % % ( Note that the struct command expands cell arrays to give multiple ) % ( records, so if you want a cell array as a field value, you have to ) % ( embed it within another cell, hence the double "{{"'s. ) % % D.Desname - a string naming the design % % In general, spm_spm_ui.m accomodates four factors. Usually these are % 'group', 'subject', 'condition' & 'replication', but to allow for a % flexible interface these are dynamically named for different designs, % and are referred to as Factor4, Factor3, Factor2, and Factor1 % respectively. The first part of the D definition dictates the names % and number of factor levels (i.e. number of subjects etc.) relevant % for this design, and also how the H (condition) and B (block) % partitions of the design matrix should be constructed. % % D.n - a 1x4 vector, indicating the number of levels. D.n(i) % for i in [1:4] is the number of levels for factor i. % Specify D.n(i) as 1 to ignore this factor level, % otherwise the number of levels can be pre-specified as a % given number, or given as Inf to allow the user to % choose the number of levels. % % D.sF - a 1x4 cellstr containing the names of the four % factors. D.sF{i} is the name of factor i. % % D.b.aTime - a binary indicator specifying whether images within F3 % level (subject) are selected in time order. For time % order (D.b.aTime=1), F2 levels are indicated by a user % input "condition" string (input handled by spm_input's % 'c' type). When (D.b.aTime=0), images for each F3 are % selected by F2 (condition). The latter was the mode of % SPM95 and SPM96. (SPM94 and SPMclassic didn't do % replications of conditions.) % % Once the user has entered the images and indicated the factor levels, % a nScan x 4 matrix, I, of indicator variables is constructed % specifying for each scan the relevant level of each of the four % factors. I(n,i) is the level of factor i corresponding to image n. % This I matrix of factor indicators is then used to construct the H % and B forms of the design matrix according to the prescripton in the % design definition D: % % D.Hform - a string specifying the form of the H partition of the % design matrix. The string is evaluated as an argument % string for spm_DesMtx, which builds design matrix % partitions from indicator vectors. % (eval(['[H,Hnames] = spm_DesMtx(',D.Hform,');'])) % % D.BForm - a string specifying the form of the G partition. % % ( Note that a constant H partition is dropped if the B partition can ) % ( model the constant effect. ) % % The next part of the design definition defines covariate options. % Covariates are split into covariates (of interest) and nuisance % variables. The covariates of interest and nuisance variables are put % into the C & G partitions of the design matrox (the final design % matrix is [H,C,B,G], where global nuisance covariates are appended to % G). In SPM94/5/6 the design matrix was partitioned into effects of % interest [H,C] and effects of no interest [B,G], with an F-test for % no effects of interest and adjusted data (for effects of no interest) % following from these partitions. SPM99 is more freestyle, with % adjustments and F-tests specified by contrasts. However, the concept % of effects of interest and of no interest has been maintained for % continuity, and spm_spm_ui.m computes an F-contrast to test for "no % effects of interest". % % D.nC - a 1x2 vector: D.nC(1) is the number of covariates, % D.nC(2) the number of nuisance variables. Specify zero % to skip covariate entry, the actual number of % covariates, or Inf to let the user specify the number of % covariates. As with earlier versions, blocks of design % matrix can be entered. However, these are now treated as % a single covariate entity, so the number of % covariates.nuisance variables is now the number of items % you are prompted for, regardless of their dimension. (In % SPM95-6 this number was the number of covariate vectors % that could be entered.) % % D.iCC - a 1x2 cell array containing two vectors indicating the % allowable covariate centering options for this design. % These options are defined in the body of spm_spm_ui.m, % in variables sCC & CFIforms. Use negative indices to % indicate the default, if any - the largest negative % wins. % % D.iCFI - a 1x2 cell array containing two vectors indicating the % allowable covariate by factor interactions for this % design. Interactions are only offered with a factor if % it has multiple levels. The options are defined in the % body of spm_spm_ui.m, in variables sCFI & CFIforms. Use % negative indicies to indicate a default. % % The next part defines global options: % % D.iGXcalc - a vector of possible global calculation options for % this design, as listed in the body of spm_spm_ui.m in % variable sGXcalc. (If other global options are chosen, % then the "omit" option is not offered.) Again, negative % values indicate a default. % % D.iGloNorm - a vector of possible global normalisation options for % this design, as described in the body of spm_spm_ui.m in % variable sGloNorm. % % D.iGMsca - a vector of possible grand mean scaling options, as % described in the body of spm_spm_ui.m in variable % sGMsca. (Note that grand mean scaling is redundent when % using proportional scaling global flow normalisation.) % % D.iGC - a vector of possible global covariate centering % options, corresponding to the descriptions in variable % iCC given in the body of spm_spm_ui.m. This is only % relevant for AnCova type global normalisation, and even % then only if you're actually interested in constraining % the values of the parameters in some useful way. % Usually, one chooses option 10, "as implied by AnCova". % % The next component specifies masking options: % % D.M_.T - a vector defining the analysis threshold: Specify % "-Inf" as an element to offer "None" as an option. If a % real element is found, then absolute thresholding is % offered, with the first real value proffered as default % threshold. If an imaginary element is found, then % proportional thresholding if offered (i.e. the threshold % is a proportion of the image global), with the (abs of) % the first imaginary element proffered as default. % % D.M_.I - Implicit masking? 0-no, 1-yes, Inf-ask. (This is % irrelevant for image types with a representation of NaN, % since NaN is then the mask value, and NaN's are always % masked.) % % D.M.X - Explicit masking? 0-no, 1-yes, Inf-ask. % %_______________________________________________________________________ % Andrew Holmes %-Condition arguments %----------------------------------------------------------------------- if (nargin==0), Action = 'CFG'; else, Action = varargin{1}; end switch lower(Action), case 'cfg' %======================================================================= % - C O N F I G U R E D E S I G N %======================================================================= % spm_spm_ui('CFG',D) if nargin<2, D=[]; else, D=varargin{2}; end %-GUI setup %----------------------------------------------------------------------- SPMid = spm('FnBanner',mfilename,SCCSid); [Finter,Fgraph,CmdLine] = spm('FnUIsetup','Stats: Setup analysis',0); spm_help('!ContextHelp',mfilename) %-Ask about overwriting files from previous analyses... %----------------------------------------------------------------------- tmp = [ exist(fullfile('.','SPMcfg.mat'), 'file')==2 ,... exist(fullfile('.','SPM.mat'), 'file')==2 ]; if any(tmp) str = { ' SPMstats configuration (SPMcfg.mat)',... ' SPMstats results files (inc. SPM.mat)'}; str = { 'Current directory contains existing SPMstats files:',... str{tmp},['(pwd = ',pwd,')'],' ',... 'Continuing will overwrite existing files!'}; if spm_input(str,1,'bd','stop|continue',[1,0],1,mfilename); fprintf('%-40s: %30s\n\n',... 'Abort... (existing SPMstats files)',spm('time')) spm_clf(Finter) return end end %-Option definitions %----------------------------------------------------------------------- %-Generic factor names sF = {'sF1','sF2','sF3','sF4'}; %-Covariate by factor interaction options sCFI = {'';... %-1 'with sF1';'with sF2';'with sF3';'with sF4';... %-2:5 'with sF2 (within sF4)';'with sF3 (within sF4)'}; %-6,7 %-DesMtx argument components for covariate by factor interaction options % (Used for CFI's Covariate Centering (CC), GMscale & Global normalisation) CFIforms = { '[]', 'C', '{}';... %-1 'I(:,1)', 'FxC', '{D.sF{1}}';... %-2 'I(:,2)', 'FxC', '{D.sF{2}}';... %-3 'I(:,3)', 'FxC', '{D.sF{3}}';... %-4 'I(:,4)', 'FxC', '{D.sF{4}}';... %-5 'I(:,[4,2])', 'FxC', '{D.sF{4},D.sF{2}}';... %-6 'I(:,[4,3])', 'FxC', '{D.sF{4},D.sF{3}}' }; %-7 %-Centre (mean correction) options for covariates & globals (CC) % (options 9-12 are for centering of global when using AnCova GloNorm) (GC) sCC = { 'around overall mean';... %-1 'around sF1 means';... %-2 'around sF2 means';... %-3 'around sF3 means';... %-4 'around sF4 means';... %-5 'around sF2 (within sF4) means';... %-6 'around sF3 (within sF4) means';... %-7 '';... %-8 'around user specified value';... %-9 '(as implied by AnCova)';... %-10 'GM';... %-11 '(redundant: not doing AnCova)'}'; %-12 %-DesMtx I forms for covariate centering options CCforms = {'ones(nScan,1)',CFIforms{2:end,1},''}'; %-Global normalization options (options 1-7 match CFIforms) (GloNorm) sGloNorm = { 'AnCova';... %-1 'AnCova by sF1';... %-2 'AnCova by sF2';... %-3 'AnCova by sF3';... %-4 'AnCova by sF4';... %-5 'AnCova by sF2 (within sF4)';... %-6 'AnCova by sF3 (within sF4)';... %-7 'proportional scaling';... %-8 ''}; %-9 %-Grand mean scaling options (GMsca) sGMsca = { 'scaling of overall grand mean';... %-1 'scaling of sF1 grand means';... %-2 'scaling of sF2 grand means';... %-3 'scaling of sF3 grand means';... %-4 'scaling of sF4 grand means';... %-5 'scaling of sF2 (within sF4) grand means';... %-6 'scaling of sF3 (within sF4) grand means';... %-7 '(implicit in PropSca global normalisation)';... %-8 '' }; %-9 %-NB: Grand mean scaling by subject is redundent for proportional scaling %-Global calculation options (GXcalc) sGXcalc = { 'omit';... %-1 'user specified';... %-2 'mean voxel value (within per image fullmean/8 mask)'}; %-3 %======================================================================= %-D E S I G N P A R A M E T E R S %======================================================================= %-Get design type %----------------------------------------------------------------------- D = [D, struct(... 'DesName','Bespoke design via CBU CSV interface',... 'n',[Inf Inf Inf Inf], 'sF',{{'repl','condition','subject','group'}},... 'Hform', 'I(:,[4,2]),''-'',{''stud'',''cond''}',... 'Bform', 'I(:,[4,3]),''-'',{''stud'',''subj''}',... 'nC',[Inf,Inf],'iCC',{{[5:8],[5,7,8]}},'iCFI',{{[1,5,6,7],[1,5,7]}},... 'iGXcalc',[1,2,-3],'iGMsca',[-7,9],'GM',50,... 'iGloNorm',[7,8,9],'iGC',10,... 'M_',struct('T',[0,0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',1))]; if isempty(D) tmp = spm_input('Select design class...','+1','m',... {'Basic stats','Standard PET designs','SPM96 PET designs'}); switch tmp case 1, D = spm_spm_ui('DesDefs_Stats'); case 2, D = spm_spm_ui('DesDefs_PET'); case 3, D = spm_spm_ui('DesDefs_PET96'); otherwise, error('Don''t know that one!') end end %D = D(spm_input('Select design type...','+1','m',{D.DesName}')); %-Set factor names for this design %----------------------------------------------------------------------- sCC = sf_estrrep(sCC,[sF',D.sF']); sCFI = sf_estrrep(sCFI,[sF',D.sF']); sGloNorm = sf_estrrep(sGloNorm,[sF',D.sF']); sGMsca = sf_estrrep(sGMsca,[sF',D.sF']); %-Get filenames & factor indicies %----------------------------------------------------------------------- %%%%%%%%%%%%%% %start cbuhack %%%%%%%%%%%%%% cbu_title='untitled'; cbu_factors='GSCR'; cbu_factor_format='%d,%d,%d,%d,'; cbu_cov_columns=0; cbu_cov_partition={}; cbu_cov_block_labels={}; cbu_cov_format=''; cbu_covariates=[]; str = { 'Do you want to read filename/condition stuff',... 'from an ascii file?'}; cbuhack = spm_input(str,1,'bd','yes|no',[1,0],0,'CBU file input '); if cbuhack [P,I, ... cbu_title, ... cbu_factors, ... cbu_factor_format, ... cbu_cov_columns, ... cbu_cov_partition, ... cbu_cov_block_labels, ... cbu_cov_format, ... cbu_covariates] ... = cbu_csv_ui('cbuhack',D.sF,D.n,D.b.aTime); else [P,I] = spm_spm_ui('Files&Indices',D.sF,D.n,D.b.aTime); %-Files & indices end save I I; %%%%%%%%%%%% %end cbuhack %%%%%%%%%%%% nScan = length(P); %-#observations %-Additional design parameters %----------------------------------------------------------------------- bL = any(diff(I,1),1); %-Multiple factor levels? % NB: bL(2) might be thrown by user specified f1 levels % (D.b.aTime & D.n(2)>1) - assumme user is consistent? bFI = [bL(1),bL(2:3)&~bL(4),bL(4),bL([2,3])&bL(4)]; %-Allowable interactions for covariates %-Only offer interactions with multi-level factors, and % don't offer by F2|F3 if bL(4)! %-Build Condition (H) and Block (B) partitions %======================================================================= eval(['[H,Hnames] = spm_DesMtx(',D.Hform,');']) if rank(H)==nScan, error('unestimable condition effects'), end eval(['[B,Bnames] = spm_DesMtx(',D.Bform,');']) if rank(B)==nScan, error('unestimable block effects'), end %-Drop a constant H partition if B partition can model constant if size(H,2)>0 & all(H(:)==1) & (rank([H B])==rank(B)) H = []; Hnames = {}; warning('Dropping redundant constant H partition') end %-Covariate partition(s): interest (C) & nuisance (G) excluding global %======================================================================= nC = D.nC; %-Default #covariates C = {[],[]}; Cnames = {{},{}}; %-Covariate DesMtx partitions & names xC = []; %-Struct array to hold raw covariates dcname = {'CovInt','NusCov'}; %-Default root names for covariates dstr = {'covariate','nuisance variable'}; GUIpos = spm_input('!NextPos'); nc = [0,0]; for i=1:2 % 1:covariates of interest, 2:nuisance variables if ~cbuhack if isinf(nC(i)), nC(i)=spm_input(['# ',dstr{i},'s'],GUIpos,'w1');, end else if size(cbu_cov_partition,2)1, tstr = sprintf('%s^{%d}',dcname{i},nc(i)); else, tstr = dcname{i}; end if ~cbuhack cname = spm_input([str,' name?'],'+1','s',tstr); cbu_cov_block_labels{i}{nc(i)} = cname; else cname = cbu_cov_block_labels{i}{nc(i)}; end rc = c; %-Save covariate value rcname = cname; %-Save covariate name %-Interaction option? (if single covariate vector entered)? %--------------------------------------------------------------- if size(c,2) == 1 if length(D.iCFI{i})>1 %-User choice of interaction options, default is negative %-Only offer interactions for appropriate factor combinations iCFI = intersect(abs(D.iCFI{i}),find([1,bFI])); dCFI = max([1,intersect(iCFI,-D.iCFI{i}(D.iCFI{i}<0))]); iCFI = spm_input([str,': interaction?'],'+1','m',... sCFI(iCFI),iCFI,find(iCFI==dCFI)); else iCFI = abs(D.iCFI{i}); %-AutoSelect default option end else iCFI = 1; end %-Centre covariate(s)? (Default centring to correspond to CFI) % Always offer "no centering" as default for design matrix blocks %--------------------------------------------------------------- DiCC = D.iCC{i}; if size(c,2)>1, DiCC = union(DiCC,-8); end if length(DiCC)>1 %-User has a choice of centering options %-Only offer factor specific for appropriate factor combinations iCC = intersect(abs(DiCC),find([1,bFI,1]) ); %-Default is max -ve option in D, overridden by iCFI if CFI if iCFI==1, dCC=-DiCC(DiCC<0); else, dCC=iCFI; end dCC = max([1,intersect(iCC,dCC)]); iCC = spm_input([str,': centre?'],'+1','m',... sCC(iCC),iCC,find(iCC==dCC)); else iCC = abs(DiCC); %-AutoSelect default option end %-Centre within factor levels as appropriate if any(iCC==[1:7]), c = c - spm_meanby(c,eval(CCforms{iCC})); end %-Do any interaction (only for single covariate vectors) %--------------------------------------------------------------- if iCFI>1 %-(NB:iCFI=1 if size(c,2)>1) tI = [eval(CFIforms{iCFI,1}),c]; tConst = CFIforms{iCFI,2}; tFnames = [eval(CFIforms{iCFI,3}),{cname}]; [c,cname] = spm_DesMtx(tI,tConst,tFnames); elseif size(c,2)>1 %-Design matrix block [null,cname] = spm_DesMtx(c,'X',cname); else cname = {cname}; end %-Store raw covariate details in xC struct for reference %-Pack c into appropriate DesMtx partition %--------------------------------------------------------------- %-Construct description string for covariate str = {sprintf('%s: %s',str,rcname)}; if size(rc,2)>1, str = {sprintf('%s (block of %d covariates)',... str{:},size(rc,2))}; end if iCC<8, str=[str;{['used centered ',sCC{iCC}]}]; end if iCFI>1, str=[str;{['fitted as interaction ',sCFI{iCFI}]}]; end tmp = struct( 'rc',rc, 'rcname',rcname,... 'c',c, 'cname',{cname},... 'iCC',iCC, 'iCFI',iCFI,... 'type',i,... 'cols',[1:size(c,2)] + ... size([H,C{1}],2) + ... size([B,C{2}],2)*(i-1),... 'descrip',{str} ); if isempty(xC), xC=tmp; else, xC=[xC,tmp]; end C{i} = [C{i},c]; Cnames{i} = [Cnames{i}; cname]; end % (while) end % (for) %%%%%%%%%%%%%%%%%%%%%%%%%% %start cbuhack save design %%%%%%%%%%%%%%%%%%%%%%%%%% csv=fopen('design.csv','w'); % save design/file data in design.csv cbu_full='GSCR'; cbu_f=''; cbu_o=''; cbu_w=''; for i = 1:4 if length(unique(I(:,5-i)))>1 cbu_f=[cbu_f,cbu_full(i)]; cbu_o=[cbu_o,'%d, ']; cbu_w=[cbu_w,'I(i,',int2str(5-i),'), ']; end end fprintf(csv,'=cbu_title=''%s'';\n=cbu_factors=''%s'';\n=cbu_cov_columns=[%s];\n', ... cbu_title,cbu_f,int2str(cbu_cov_columns)); part=''; labs=''; if size(cbu_cov_partition,2)>0 part = [part, '=cbu_cov_partition={']; labs = [labs, '=cbu_cov_block_labels={']; for i=1:size(cbu_cov_partition,2) if size(cbu_cov_partition{i},2)>0 part = [part, '{']; labs = [labs, '{']; for j=1:size(cbu_cov_partition{i},2) part = [part, sprintf('[%s],',int2str(cbu_cov_partition{i}{j}))]; labs = [labs, sprintf('''%s'',',cbu_cov_block_labels{i}{j})]; end part = [part, '}, ']; labs = [labs, '}, ']; end end part = [part, '};\n']; labs = [labs, '};\n']; end fprintf(csv,strrep(strrep(part,',}','}'),', }','}')); fprintf(csv,strrep(strrep(labs,',}','}'),', }','}')); fprintf(csv,'+\n'); for i=1:size(I,1) eval(strrep(['fprintf(csv,''',cbu_o,''',',cbu_w,');'],', )',')')); for j=1:size(cbu_covariates,2) fprintf(csv,'%g, ',cbu_covariates(i,j)); end %j fprintf(csv,'%s\n',P{i}); end %i fclose(csv); %%%%%%%%%%%%%%%%%%%%%%%% %end cbuhack save design %%%%%%%%%%%%%%%%%%%%%%%% clear c tI tConst tFnames spm_input('!SetNextPos',GUIpos); %-Unpack into C & G design matrix sub-partitions G = C{2}; Gnames = Cnames{2}; C = C{1}; Cnames = Cnames{1}; %-Options... %======================================================================= %-Global normalization options (GloNorm) %----------------------------------------------------------------------- if length(D.iGloNorm)>1 %-User choice of global normalisation options, default is negative %-Only offer factor specific for appropriate factor combinations iGloNorm = intersect(abs(D.iGloNorm),find([1,bFI,1,1])); dGloNorm = max([0,intersect(iGloNorm,-D.iGloNorm(D.iGloNorm<0))]); iGloNorm = spm_input('GloNorm: Select global normalisation','+1','m',... sGloNorm(iGloNorm),iGloNorm,find(iGloNorm==dGloNorm)); else iGloNorm = abs(D.iGloNorm); end %-Grand mean scaling options (GMsca) %----------------------------------------------------------------------- if iGloNorm==8 iGMsca=8; %-grand mean scaling implicit in PropSca GloNorm elseif length(D.iGMsca)==1 iGMsca = abs(D.iGMsca); else %-User choice of grand mean scaling options %-Only offer factor specific for appropriate factor combinations iGMsca = intersect(abs(D.iGMsca),find([1,bFI,0,1])); %-Default is max -ve option in D, overridden by iGloNorm if AnCova if iGloNorm==9, dGMsca=-D.iGMsca(D.iGMsca<0); else, dGMsca=iGloNorm; end dGMsca = max([0,intersect(iGMsca,dGMsca)]); iGMsca = spm_input('GMsca: grand mean scaling','+1','m',... sGMsca(iGMsca),iGMsca,find(iGMsca==dGMsca)); end %-Value for PropSca / GMsca (GM) %----------------------------------------------------------------------- if iGMsca==9 %-Not scaling (GMsca or PropSca) GM=0; %-Set GM to zero when not scaling else %-Ask user value of GM if iGloNorm==8 str='PropSca global mean to'; else str=[strrep(sGMsca{iGMsca},'scaling of','scale'),' to']; end GM = spm_input(str,'+1','r',D.GM,1); %-If GM is zero then don't GMsca! or PropSca GloNorm if GM==0, iGMsca=9; if iGloNorm==8, iGloNorm=9; end, end end %-Sort out description strings for GloNorm and GMsca sGloNorm = sGloNorm{iGloNorm}; sGMsca = sGMsca{iGMsca}; if iGloNorm==8 sGloNorm = sprintf('%s to %-4g',sGloNorm,GM); elseif iGMsca<8 sGMsca = sprintf('%s to %-4g',sGMsca,GM); end %-Global centering (for AnCova GloNorm) (GC) %----------------------------------------------------------------------- %-Specify the centering option for the global covariate for AnCova %-Basically, if 'GMsca'ling then should centre to GM (iGC=11). Otherwise, % should centre in similar fashion to AnCova (i.e. by the same factor(s)), % such that models are seperable (iGC=10). This is particularly important % for subject specific condition effects if then passed on to a second-level % model. (See also spm_adjmean_ui.m) SPM96 (& earlier) used to just centre % GX around its (overall) mean (iGC=1). %-This code allows more general options to be specified (but is a bit complex) %-Setting D.iGC=[-10,-11] gives the standard choices above %-If not doing AnCova then GC is irrelevant if ~any(iGloNorm==[1:7]) iGC = 12; gc = []; else %-Annotate options 10 & 11 with specific details %--------------------------------------------------------------- %-Tag '(as implied by AnCova)' with actual AnCova situation sCC{10} = [sCC{iGloNorm},' (<= ',sGloNorm,')']; %-Tag 'GM' case with actual GM & GMsca case sCC{11} = sprintf('around GM=%g (i.e. %s after grand mean scaling)',... GM,strrep(sCC{iGMsca},'around ','')); %-Constuct vector of allowable iGC %--------------------------------------------------------------- %-Weed out redundent factor combinations from pre-set allowable options iGC = intersect(abs(D.iGC),find([1,bFI,1,1,1,1])); %-Omit 'GM' option if didn't GMsca (iGMsca~=8 'cos doing AnCova) if any(iGMsca==[8,9]), iGC = setdiff(iGC,11); end %-Omit 'GM' option if same as '(as implied by AnCova)' if iGloNorm==iGMsca, iGC = setdiff(iGC,11); end %-If there's a choice, set defaults (if any), & get answer %--------------------------------------------------------------- if length(iGC)>1 dGC = max([0,intersect(iGC,-D.iGC(D.iGC<0))]); str = 'Centre global covariate'; if iGMsca<8, str = [str,' (after grand mean scaling)']; end iGC = spm_input(str,'+1','m',sCC(iGC),iGC,find(iGC==dGC)); elseif isempty(iGC) error('Configuration error: empty iGC') end %-If 'user specified' then get value %--------------------------------------------------------------- if iGC==9 gc = spm_input('Centre globals around','+0','r',D.GM,1); sCC{9} = sprintf('%s of %g',sCC{iGC},gc); else gc = 0; end end %-Thresholds & masks defining voxels to analyse (MASK) %======================================================================= GUIpos = spm_input('!NextPos'); %-Analysis threshold mask %----------------------------------------------------------------------- %-Work out available options: % -Inf=>None, complex=>proportional, real=>absolute (i.e. times global) M_T = D.M_.T; if isempty(M_T), M_T = [-Inf, 100, 0.8*sqrt(-1)]; end M_T = { 'none', M_T(min(find(isinf(M_T))));... 'absolute', M_T(min(find(isfinite(M_T)&(M_T==real(M_T)))));... 'prop''nal', M_T(min(find(isfinite(M_T)&(M_T~=real(M_T))))) }; %-Work out available options q = ~[isempty(M_T{1,2}), isempty(M_T{2,2}), isempty(M_T{3,2})]; %-If there's a choice between proportional and absolute then ask if all(q(2:3)) tmp = spm_input('Threshold masking',GUIpos,'b',M_T(q,1),find(q)); q(setdiff([1:3],tmp))=0; end %-Get mask value - note that at most one of q(2:3) is true if ~any(q) %-Oops - nothing specified! M_T = -Inf; elseif all(q==[1,0,0]) %-no threshold masking M_T = -Inf; else %-get mask value if q(1), args = {'br1','None',-Inf,abs(M_T{1+find(q(2:3)),2})}; else, args = {'r',abs(M_T{1+find(q(2:3)),2})}; end if q(2) M_T = spm_input('analysis threshold',GUIpos,args{:}); elseif q(3) M_T = spm_input('analysis thresh (prop''n of global)',GUIpos,... args{:}); if isfinite(M_T) & isreal(M_T), M_T=M_T*sqrt(-1); end else error('Shouldn''t get here!') end end %-Make a description string if isinf(M_T) xsM.Analysis_threshold = 'None (-Inf)'; elseif isreal(M_T) xsM.Analysis_threshold = sprintf('images thresholded at %6g',M_T); else xsM.Analysis_threshold = sprintf(['images thresholded at %6g ',... 'times global'],imag(M_T)); end %-Implicit masking: Ignore zero voxels in low data-types? %----------------------------------------------------------------------- % (Implicit mask is NaN in higher data-types.) type = getfield(spm_vol(P{1}),'dim')*[0,0,0,1]'; if ~spm_type(type,'nanrep') switch D.M_.I case Inf, M_I = spm_input('Implicit mask (ignore zero''s)?',... '+1','y/n',[1,0],1); %-Ask case {0,1}, M_I = D.M_.I; %-Pre-specified otherwise, error('unrecognised D.M_.I type') end if M_I, xsM.Implicit_masking = 'Yes: zero''s treated as missing'; else, xsm.Implicit_masking = 'No'; end else M_I = 1; xsM.Implicit_masking = 'Yes: NaN''s treated as missing'; end %-Explicit mask images (map them later...) %----------------------------------------------------------------------- switch(D.M_.X) case Inf, M_X = spm_input('explicit mask images?','+1','y/n',[1,0],2); case {0,1}, M_X = D.M_.X; otherwise, error('unrecognised D.M_.X type') end if M_X, M_P = spm_get(Inf,'*.img',{'select mask images'}); else, M_P = {}; end %-Global calculation (GXcalc) %======================================================================= iGXcalc = abs(D.iGXcalc); %-Only offer "omit" option if not doing any GloNorm, GMsca or PropTHRESH if ~(iGloNorm==9 & iGMsca==9 & (isinf(M_T)|isreal(M_T))) iGXcalc = intersect(iGXcalc,[2:size(sGXcalc,1)]); end if isempty(iGXcalc) error('no GXcalc options') elseif length(iGXcalc)>1 %-User choice of global calculation options, default is negative dGXcalc = max([1,intersect(iGXcalc,-D.iGXcalc(D.iGXcalc<0))]); iGXcalc = spm_input('Global calculation','+1','m',... sGXcalc(iGXcalc),iGXcalc,find(iGXcalc==dGXcalc)); else iGXcalc = abs(D.iGXcalc); end if iGXcalc==2 %-Get user specified globals g = spm_input('globals','+0','r',[],[nScan,1]); end sGXcalc = sGXcalc{iGXcalc}; %======================================================================= % - C O N F I G U R E D E S I G N %======================================================================= spm('FigName','Stats: configuring',Finter,CmdLine); spm('Pointer','Watch'); %-Images & image info: Map Y image files and check consistency of % dimensions and orientation / voxel size %======================================================================= fprintf('%-40s: ','Mapping files') %-# VY = spm_vol(char(P)); fprintf('%30s\n','...done') %-# if any(any(diff(cat(1,VY.dim),1,1),1)&[1,1,1,0]) %NB: Bombs for single image error('images do not all have the same dimensions'), end if any(any(any(diff(cat(3,VY.mat),1,3),3))) error('images do not all have same orientation & voxel size'), end %-Global values, scaling and global normalisation %======================================================================= %-Compute global values %----------------------------------------------------------------------- switch iGXcalc, case 1 %-Don't compute => no GMsca (iGMsca==9) or GloNorm (iGloNorm==9) g = []; case 2 %-User specified globals case 3 %-Compute as mean voxel value (within per image fullmean/8 mask) g = zeros(nScan,1); fprintf('%-40s: %30s','Calculating globals',' ') %-# for i = 1:nScan fprintf('%s%30s',sprintf('\b')*ones(1,30),... sprintf('%3d/%-3d',i,nScan)) %-# g(i) = spm_global(VY(i)); end fprintf('%s%30s\n',sprintf('\b')*ones(1,30),'...done') %-# otherwise error('illegal iGXcalc') end rg = g; fprintf('%-40s: ','Design configuration') %-# %-Scaling: compute global scaling factors gSF required to implement proportional % scaling global normalisation (PropSca) or grand mean scaling (GMsca), % as specified by iGMsca (& iGloNorm) %----------------------------------------------------------------------- switch iGMsca, case 8 %-Proportional scaling global normalisation if iGloNorm~=8, error('iGloNorm-iGMsca(8) mismatch for PropSca'), end gSF = GM./g; g = GM*ones(nScan,1); case {1,2,3,4,5,6,7} %-Grand mean scaling according to iGMsca gSF = GM./spm_meanby(g,eval(CCforms{iGMsca})); g = g.*gSF; case 9 %-No grand mean scaling gSF = ones(nScan,1); otherwise error('illegal iGMsca') end %-Apply gSF to memory-mapped scalefactors to implement scaling %----------------------------------------------------------------------- for i=1:nScan, VY(i).pinfo(1:2,:)=VY(i).pinfo(1:2,:)*gSF(i); end %-AnCova: Construct global nuisance covariates partition (if AnCova) %----------------------------------------------------------------------- if any(iGloNorm==[1:7]) %-Centre global covariate as requested %--------------------------------------------------------------- switch iGC, case {1,2,3,4,5,6,7} %-Standard sCC options gc = spm_meanby(g,eval(CCforms{iGC})); case 8 %-No centering gc = 0; case 9 %-User specified centre %-gc set above case 10 %-As implied by AnCova option gc = spm_meanby(g,eval(CCforms{iGloNorm})); case 11 %-Around GM gc = GM; otherwise %-unknown iGC error('unexpected iGC value') end %-AnCova - add scaled centred global to DesMtx `G' partition %--------------------------------------------------------------- tI = [eval(CFIforms{iGloNorm,1}),g-gc]; tConst = CFIforms{iGloNorm,2}; tFnames = [eval(CFIforms{iGloNorm,3}),{'global'}]; [g,gname] = spm_DesMtx(tI,tConst,tFnames); clear tI tConst tFnames %-Save GX info in xC struct for reference str = {sprintf('%s: global',dstr{2})}; if any(iGMsca==[1:7]), str=[str;{['(after ',sGMsca,')']}]; end if iGC~=8, str=[str;{['used centered ',sCC{iGC}]}]; end if iGloNorm>1, str=[str;{['fitted as interaction ',sCFI{iGloNorm}]}]; end tmp = struct( 'rc',rg.*gSF, 'rcname','global',... 'c',g, 'cname',{gname},... 'iCC',iGC, 'iCFI',iGloNorm,... 'type',3,... 'cols',[1:size(g,2)]+size([H C B G],2),... 'descrip',{str} ); G = [G,g]; Gnames = [Gnames; gname]; if isempty(xC), xC=tmp; else, xC=[xC,tmp]; end elseif iGloNorm==8 | iGXcalc>1 if iGloNorm==8 str = { 'global values: (used for proportional scaling)';... '("raw" unscaled globals shown)'}; elseif isfinite(M_T) & ~isreal(M_T) str = { 'global values: (used to compute analysis threshold)'}; else str = { 'global values: (computed but not used)'}; end tmp = struct( 'rc',rg, 'rcname','global',... 'c',{[]}, 'cname',{{}},... 'iCC',0, 'iCFI',0,... 'type',3,... 'cols',{[]},... 'descrip',{str} ); if isempty(xC), xC=tmp; else, xC=[xC,tmp]; end end %-Save info on global calculation in xGX structure %----------------------------------------------------------------------- xGX = struct(... 'iGXcalc',iGXcalc, 'sGXcalc',sGXcalc, 'rg',rg,... 'iGMsca',iGMsca, 'sGMsca',sGMsca, 'GM',GM,'gSF',gSF,... 'iGC', iGC, 'sGC', sCC{iGC}, 'gc', gc,... 'iGloNorm',iGloNorm, 'sGloNorm',sGloNorm); %-Construct masking information structure and compute actual analysis % threshold using scaled globals (rg.*gSF) %----------------------------------------------------------------------- if isreal(M_T), M_TH = M_T * ones(nScan,1); %-NB: -Inf is real else, M_TH = imag(M_T) * (rg.*gSF); end if ~isempty(M_P) VM = spm_vol(char(M_P)); xsM.Explicit_masking = [{'Yes: mask images :'};{VM.fname}']; else VM=[]; xsM.Explicit_masking = 'No'; end xM = struct('T',M_T, 'TH',M_TH, 'I',M_I, 'VM',{VM}, 'xs',xsM); %-Construct full design matrix (X), parameter names (Xnames), % and design information structure (xX) %======================================================================= X = [H C B G]; Xnames = [Hnames; Cnames; Bnames; Gnames]; tmp = cumsum([size(H,2), size(C,2), size(B,2), size(G,2)]); xX = struct( 'I', I,... 'sF', {D.sF},... 'X', X,... 'sigma', 0,... 'iH', [1:size(H,2)],... 'iC', [1:size(C,2)] + tmp(1),... 'iB', [1:size(B,2)] + tmp(2),... 'iG', [1:size(G,2)] + tmp(3),... 'Xnames', {Xnames}); %-Pre-specified contrast for F-test on effects of interest %======================================================================= if ~isempty([H,C]) %-Some effects designated "of interest" F_iX0 = (size(H,2)+size(C,2)) + [1:size(B,2)+size(G,2)]; else %-No effects designated "of interest" - F-test for B=0 F_iX0 = []; end %-Design description (an nx2 cellstr) - for saving and display %======================================================================= tmp = { sprintf('%d condition, +%d covariate, +%d block, +%d nuisance',... size(H,2),size(C,2),size(B,2),size(G,2));... sprintf('%d total, having %d degrees of freedom',... size(X,2),rank(X));... sprintf('leaving %d degrees of freedom from %d images',... size(X,1)-rank(X),size(X,1)) }; xsDes = struct( 'Design', {D.DesName},... 'Global_calculation', {sGXcalc},... 'Grand_mean_scaling', {sGMsca},... 'Global_normalisation', {sGloNorm},... 'Parameters', {tmp} ); fprintf('%30s\n','...done') %-# %-Save SPMcfg.mat file fprintf('%-40s: ','Saving SPMstats configuration') %-# save SPMcfg SPMid D xsDes VY xX xC xGX xM F_iX0 fprintf('%30s\n','...SPMcfg.mat saved') %-# %-Display Design reports %======================================================================= fprintf('%-40s: ','Design reporting') %-# spm_DesRep('DesMtx',xX,{VY.fname}',xsDes) fprintf('%30s\n','...done') %-# %-Analysis Proper? %======================================================================= spm('Pointer','Arrow') fprintf('%-40s: %30s\n','Completed',spm('time')) %-# if spm_input('estimate?','_+0','b','now|later',[1,0],1) spm('Pointer','Watch') spm('FigName','Stats: estimating...',Finter,CmdLine); spm_spm(VY,xX,xM,F_iX0,xC,xsDes) spm('Pointer','Arrow') else spm('FigName','Stats: configured',Finter,CmdLine); spm('Pointer','Arrow') spm_DesRep('DesRepUI',struct( 'xX', xX,... 'VY', VY,... 'xM', xM,... 'F_iX0', F_iX0,... 'xC', xC,... 'xsDes', xsDes,... 'swd', pwd,... 'SPMid', SPMid,... 'cfg', 'SPMcfg')); end fprintf('\n\n') case 'files&indices' %======================================================================= % - Get files and factor indices %======================================================================= % [P,I] = spm_spm_ui('Files&Indices',DsF,Dn,DbaTime) % DbaTime=D.b.aTime; Dn=D.n; DsF=D.sF; if nargin<4, DbaTime = 1; else, DbaTime = varargin{4}; end if nargin<3, Dn = [Inf,Inf,Inf,Inf]; else, Dn=varargin{3}; end if nargin<2, DsF = {'Fac1','Fac2','Fac3','Fac4'}; else, DsF=varargin{2}; end %-Initialise variables %----------------------------------------------------------------------- i4 = []; % factor 4 index (usually group) i3 = []; % factor 3 index (usually subject), per f4 i2 = []; % factor 2 index (usually condition), per f3/f4 i1 = []; % factor 1 index (usually replication), per f2/f3/f4 P = {}; % cell array of string filenames %-Accrue filenames and factor level indicator vectors %----------------------------------------------------------------------- if isinf(Dn(4)), n4 = spm_input(['#',DsF{4},'''s'],'+1','n1'); else, n4 = Dn(4); end bL4 = n4>1; ti2 = ''; GUIpos = spm_input('!NextPos'); for j4 = 1:n4 spm_input('!SetNextPos',GUIpos); sF4P=''; if bL4, sF4P=[DsF{4},' ',int2str(j4),': ']; end if isinf(Dn(3)), n3=spm_input([sF4P,'#',DsF{3},'''s'],'+1','n1'); else, n3 = Dn(3); end bL3 = n3>1; if DbaTime & Dn(2)>1 %disp('NB:selecting in time order - manually specify conditions') %-NB: This means f2 levels might not be 1:n2 GUIpos2 = spm_input('!NextPos'); for j3 = 1:n3 sF3P=''; if bL3, sF3P=[DsF{3},' ',int2str(j3),': ']; end str = [sF4P,sF3P]; tP = spm_get(Dn(2)*Dn(1),'.img',{[str,'select images...']}); n21 = length(tP); ti2 = spm_input([str,' ',DsF{2},'?'],GUIpos2,'c',ti2',n21,Dn(2)); %-Work out i1 & check [tl2,null,j] = unique(ti2); tn1 = zeros(size(tl2)); ti1 = zeros(size(ti2)); for i=1:length(tl2) tn1(i)=sum(j==i); ti1(ti2==tl2(i))=1:tn1(i); end if isfinite(Dn(1)) & any(tn1~=Dn(1)) %-#i1 levels mismatches specification in Dn(1) error(sprintf('#%s not %d as pre-specified',DsF{1},Dn(1))) end P = [P;tP]; i4 = [i4; j4*ones(n21,1)]; i3 = [i3; j3*ones(n21,1)]; i2 = [i2; ti2]; i1 = [i1; ti1]; end else if isinf(Dn(2)) n2 = spm_input([sF4P,'#',DsF{2},'''s'],'+1','n1'); else n2 = Dn(2); end bL2 = n2>1; if n2==1 & Dn(1)==1 %-single scan per f3 (subj) %disp('NB:single scan per f3') str = [sF4P,'select images, ',DsF{3},' 1-',int2str(n3)]; P = [P;spm_get(n3,'.img',{str})]; i4 = [i4; j4*ones(n3,1)]; i3 = [i3; [1:n3]']; i2 = [i2; ones(n3,1)]; i1 = [i1; ones(n3,1)]; else %-multi scan per f3 (subj) case %disp('NB:multi scan per f3') for j3 = 1:n3 sF3P=''; if bL3, sF3P=[DsF{3},' ',int2str(j3),': ']; end if Dn(1)==1 %-No f1 (repl) within f2 (cond) %disp('NB:no f1 within f2') str = [sF4P,sF3P,'select images: ',DsF{2},... ' 1-',int2str(n2)]; P = [P;spm_get(n2,'.img',{str})]; i4 = [i4; j4*ones(n2,1)]; i3 = [i3; j3*ones(n2,1)]; i2 = [i2; [1:n2]']; i1 = [i1; ones(n2,1)]; else %-multi f1 (repl) within f2 (cond) %disp('NB:f1 within f2') for j2 = 1:n2 sF2P=''; if bL2, sF2P=[DsF{2},' ',int2str(j2),': ']; end str = [sF4P,sF3P,sF2P,' select images...']; tP = spm_get(Dn(1),'.img',{str}); n1 = size(tP,1); P = [P;tP]; i4 = [i4; j4*ones(n1,1)]; i3 = [i3; j3*ones(n1,1)]; i2 = [i2; j2*ones(n1,1)]; i1 = [i1; [1:n1]']; end % (for j2) end % (if Dn(1)==1) end % (for j3) end % (if n2==1 &...) end % (if DbaTime & Dn(2)>1) end % (for j4) varargout = {P,[i1,i2,i3,i4]}; %%%%%%%%%%%%%% %start cbuhack % case 'cbuhack' %======================================================================= % - Get files and factor indices from a file %======================================================================= % [P,I] = spm_spm_ui('cbuhack',DsF,Dn,DbaTime) % DbaTime=D.b.aTime; Dn=D.n; DsF=D.sF; if nargin<4, DbaTime = 1; else, DbaTime = varargin{4}; end if nargin<3, Dn = [Inf,Inf,Inf,Inf]; else, Dn=varargin{3}; end if nargin<2, DsF = {'Fac1','Fac2','Fac3','Fac4'}; else, DsF=varargin{2}; end %-Initialise variables %----------------------------------------------------------------------- i4 = []; % factor 4 index (usually group) i3 = []; % factor 3 index (usually subject), per f4 i2 = []; % factor 2 index (usually condition), per f3/f4 i1 = []; % factor 1 index (usually replication), per f2/f3/f4 P = {}; % cell array of string filenames cbu_covariates = []; cbufilestuff=spm_get(1,'.csv',{}); hash=''; at=''; start=0; cbu_factors=''; cbu_factor_format=''; cbu_indices=''; cbu_cov_columns=0; cbu_cov_partition={}; cbu_cov_block_labels={}; cbu_cov_format=''; cbu_count=[]; cbuf=fopen(cbufilestuff{1},'r'); cellstring='tmpstring=[''a'',int2str(t4),''b'',int2str(t3),''c'',int2str(t2)];'; fullstring='GSCR'; while (feof(cbuf) == 0) line = fgetl(cbuf); if size(line, 2) == 0 line='%'; % ignore blank lines end if strcmp(line(1),'=') % '=' initialises stuff eval(line(2:size(line,2))); elseif strcmp(line(1),'+') % '+' marks end of preamble start=1; if isempty(cbu_factors) cbu_factor_format='%d,%d,%d,%d,'; cbu_factor_columns=4; else cbu_factor_columns=size(cbu_factors,2); end %cbu_factors for j = 1:3 i=findstr(fullstring(j),cbu_factors); if i cbu_factor_format = [cbu_factor_format,'%d,']; cbu_indices=[cbu_indices,'t',int2str(5-j),'=cbu_line(',int2str(i),');']; else cbu_indices=[cbu_indices,'t',int2str(5-j),'=1;']; end %if end %j i=findstr('R',cbu_factors); if i cbu_factor_format = [cbu_factor_format,'%d,']; cbu_indices=[cbu_indices,'t1=cbu_line(',int2str(i),');']; else cbu_indices=[cbu_indices,cellstring,'if isfield(cbu_count,tmpstring),',... 'tmpcnt=getfield(cbu_count,tmpstring)+1;',... 't1=tmpcnt;cbu_count=setfield(cbu_count,tmpstring,tmpcnt);,',... 'else t1=1;cbu_count=setfield(cbu_count,tmpstring,1);,end']; end %if if cbu_cov_columns==0 cbu_cov_format=''; cbu_cov_blocknos=0; else for i = 1:cbu_cov_columns cbu_cov_format=[cbu_cov_format,'%g,']; end if isempty(cbu_cov_partition) cbu_cov_format=''; cbu_cov_blocknos=0; else cbu_cov_blocknos=size(cbu_cov_partition,2); end end %cbu_cov_columns cbu_columns=cbu_factor_columns+cbu_cov_columns; elseif strcmp(line(1),'#') % '#' indicates a change of super-directory hash=strrep(line(2:size(line,2)),' ',''); elseif strcmp(line(1),'@') % '@' indicates a change subdirectory at=strrep(line(2:size(line,2)),' ',''); elseif ~strcmp(line(1),'%') % '%' is an ignorable comment line cbu_line = sscanf(line,[cbu_factor_format,cbu_cov_format,'%s\n']); cbu_file=char(cbu_line(cbu_columns+1:size(cbu_line,1))'); cbu_covs = cbu_line(cbu_factor_columns+1:cbu_columns)'; eval(cbu_indices); if findstr('#',cbu_file) | findstr('@',cbu_file) cbu_file = strrep(strrep(cbu_file,'#',hash),'@',at); elseif strncmp(cbu_file,'/',1) cbu_file = cbu_file; else cbu_file = [hash,at,cbu_file]; end i4 = [i4; t4]; i3 = [i3; t3]; i2 = [i2; t2]; i1 = [i1; t1]; P = [ P; cellstr(strrep(cbu_file,' ',''))]; cbu_covariates=[cbu_covariates;cbu_covs]; end %if end %while fclose(cbuf); varargout = {P,[i1,i2,i3,i4], ... cbu_title, ... cbu_factors, ... cbu_factor_format, ... cbu_cov_columns, ... cbu_cov_partition, ... cbu_cov_block_labels, ... cbu_cov_format, ... cbu_covariates}; % %end cbuhack %%%%%%%%%%%% case 'desdefs_stats' %======================================================================= % - Basic Stats Design definitions... %======================================================================= % D = spm_spm_ui('DesDefs_Stats'); % These are the basic Stats design definitions... %-Note: struct expands cell array values to give multiple records: % => must embed cell arrays within another cell array! %-Negative indices indicate defaults (first used) D = struct(... 'DesName','One sample t-test',... 'n', [Inf 1 1 1], 'sF',{{'obs','','',''}},... 'Hform', 'I(:,2),''-'',''mean''',... 'Bform', '[]',... 'nC',[0,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',[-1,2,3],'iGMsca',[1,-9],'GM',[],... 'iGloNorm',9,'iGC',12,... 'M_',struct('T',-Inf,'I',Inf,'X',Inf),... 'b',struct('aTime',0)); D = [D, struct(... 'DesName','Two sample t-test',... 'n', [Inf 2 1 1], 'sF',{{'obs','group','',''}},... 'Hform', 'I(:,2),''-'',''group''',... 'Bform', 'I(:,3),''-'',''\mu''',... 'nC',[0,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',[-1,2,3],'iGMsca',[1,-9],'GM',[],... 'iGloNorm',9,'iGC',12,... 'M_',struct('T',[-Inf,0,0.8*sqrt(-1)],'I',Inf,'X',Inf),... 'b',struct('aTime',1))]; D = [D, struct(... 'DesName','Paired t-test',... 'n', [1 2 Inf 1], 'sF',{{'','cond','pair',''}},... 'Hform', 'I(:,2),''-'',''condition''',... 'Bform', 'I(:,3),''-'',''\gamma''',... 'nC',[0,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',[-1,2,3],'iGMsca',[1,-9],'GM',[],... 'iGloNorm',9,'iGC',12,... 'M_',struct('T',[-Inf,0,0.8*sqrt(-1)],'I',Inf,'X',Inf),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName','One way Anova',... 'n', [Inf Inf 1 1], 'sF',{{'repl','group','',''}},... 'Hform', 'I(:,2),''-'',''group''',... 'Bform', 'I(:,3),''-'',''\mu''',... 'nC',[0,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',[-1,2,3],'iGMsca',[1,-9],'GM',[],... 'iGloNorm',9,'iGC',12,... 'M_',struct('T',[-Inf,0,0.8*sqrt(-1)],'I',Inf,'X',Inf),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName','Simple regression (correlation)',... 'n', [Inf 1 1 1], 'sF',{{'repl','','',''}},... 'Hform', '[]',... 'Bform', 'I(:,2),''-'',''\mu''',... 'nC',[1,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',[-1,2,3],'iGMsca',[1,-9],'GM',[],... 'iGloNorm',9,'iGC',12,... 'M_',struct('T',[-Inf,0,0.8*sqrt(-1)],'I',Inf,'X',Inf),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName','Multiple regression',... 'n', [Inf 1 1 1], 'sF',{{'repl','','',''}},... 'Hform', '[]',... 'Bform', 'I(:,2),''-'',''\mu''',... 'nC',[Inf,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',[-1,2,3],'iGMsca',[1,-9],'GM',[],... 'iGloNorm',9,'iGC',12,... 'M_',struct('T',[-Inf,0,0.8*sqrt(-1)],'I',Inf,'X',Inf),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName','AnCova',... 'n', [Inf Inf 1 1], 'sF',{{'repl','group','',''}},... 'Hform', 'I(:,2),''-'',''group''',... 'Bform', 'I(:,3),''-'',''\mu''',... 'nC',[0,1],'iCC',{{8,1}},'iCFI',{{1,1}},... 'iGXcalc',[-1,2,3],'iGMsca',[1,-9],'GM',[],... 'iGloNorm',9,'iGC',12,... 'M_',struct('T',[-Inf,0,0.8*sqrt(-1)],'I',Inf,'X',Inf),... 'b',struct('aTime',0))]; varargout = {D}; case 'desdefs_pet' %======================================================================= % - Standard (SPM99) PET/SPECT Design definitions... %======================================================================= % D = spm_spm_ui('DesDefs_PET'); % These are the standard PET design definitions... %-Single subject %----------------------------------------------------------------------- D = struct(... 'DesName','Single-subject: conditions & covariates',... 'n', [Inf Inf 1 1], 'sF',{{'repl','condition','',''}},... 'Hform', 'I(:,2),''-'',''cond''',... 'Bform', 'I(:,3),''-'',''\mu''',... 'nC',[Inf,Inf],'iCC',{{[-1,3,8],[-1,8]}},'iCFI',{{[1,3],1}},... 'iGXcalc',[1,2,-3],'iGMsca',[-1,9],'GM',50,... 'iGloNorm',[1,8,9],'iGC',10,... 'M_',struct('T',[0,0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',1)); D = [D, struct(... 'DesName','Single-subject: covariates only',... 'n', [Inf 1 1 1], 'sF',{{'repl','','',''}},... 'Hform', '[]',... 'Bform', 'I(:,3),''-'',''\mu''',... 'nC',[Inf,Inf],'iCC',{{[-1,8],[-1,8]}},'iCFI',{{1,1}},... 'iGXcalc',[1,2,-3],'iGMsca',[-1,9],'GM',50,... 'iGloNorm',[1,8,9],'iGC',10,... 'M_',struct('T',[0,0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',1))]; %-Multi-subject %----------------------------------------------------------------------- D = [D, struct(... 'DesName','Multi-subj: conditions & covariates',... 'n',[Inf Inf Inf 1], 'sF',{{'repl','condition','subject',''}},... 'Hform', 'I(:,2),''-'',''cond''',... 'Bform', 'I(:,3),''-'',''subj''',... 'nC',[Inf,Inf],'iCC',{{[1,3,4,8],[1,4,8]}},'iCFI',{{[1,3,4],[1,4]}},... 'iGXcalc',[1,2,-3],'iGMsca',[-4,9],'GM',50,... 'iGloNorm',[4,8,9],'iGC',10,... 'M_',struct('T',[0,0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',1))]; D = [D, struct(... 'DesName','Multi-subj: cond x subj interaction & covariates',... 'n',[Inf Inf Inf 1], 'sF',{{'repl','condition','subject',''}},... 'Hform', 'I(:,[3,2]),''-'',{''subj'',''cond''}',... 'Bform', 'I(:,3),''-'',''subj''',... 'nC',[Inf,Inf],'iCC',{{[1,3,4,8],[1,4,8]}},'iCFI',{{[1,3,4],[1,4]}},... 'iGXcalc',[1,2,-3],'iGMsca',[-4,9],'GM',50,... 'iGloNorm',[4,8,9],'iGC',10,... 'M_',struct('T',[0,0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',1))]; D = [D, struct(... 'DesName','Multi-subj: covariates only',... 'n',[Inf 1 Inf 1], 'sF',{{'repl','','subject',''}},... 'Hform', '[]',... 'Bform', 'I(:,3),''-'',''subj''',... 'nC',[Inf,Inf],'iCC',{{[1,4,8],[1,4,8]}},'iCFI',{{[1,4],[1,4]}},... 'iGXcalc',[1,2,-3],'iGMsca',[-4,9],'GM',50,... 'iGloNorm',[4,8:9],'iGC',10,... 'M_',struct('T',[0,0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; %-Multi-group %----------------------------------------------------------------------- D = [D, struct(... 'DesName','Multi-group: conditions & covariates',... 'n',[Inf Inf Inf Inf], 'sF',{{'repl','condition','subject','group'}},... 'Hform', 'I(:,[4,2]),''-'',{''stud'',''cond''}',... 'Bform', 'I(:,[4,3]),''-'',{''stud'',''subj''}',... 'nC',[Inf,Inf],'iCC',{{[5:8],[5,7,8]}},'iCFI',{{[1,5,6,7],[1,5,7]}},... 'iGXcalc',[1,2,-3],'iGMsca',[-7,9],'GM',50,... 'iGloNorm',[7,8,9],'iGC',10,... 'M_',struct('T',[0,0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',1))]; D = [D, struct(... 'DesName','Multi-group: covariates only',... 'n',[Inf 1 Inf Inf], 'sF',{{'repl','','subject','group'}},... 'Hform', '[]',... 'Bform', 'I(:,[4,3]),''-'',{''stud'',''subj''}',... 'nC',[Inf,Inf],'iCC',{{[5,7,8],[5,7,8]}},'iCFI',{{[1,5,7],[1,5,7]}},... 'iGXcalc',[1,2,-3],'iGMsca',[-7,9],'GM',50,... 'iGloNorm',[7,8,9],'iGC',10,... 'M_',struct('T',[0,0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; %-Population comparisons %----------------------------------------------------------------------- D = [D, struct(... 'DesName',... 'Population main effect: 2 cond''s, 1 scan/cond (paired t-test)',... 'n',[1 2 Inf 1], 'sF',{{'','condition','subject',''}},... 'Hform', 'I(:,2),''-'',''cond''',... 'Bform', 'I(:,3),''-'',''\mu''',... 'nC',[0,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',[1,2,-3],'iGMsca',[-1,9],'GM',50,... 'iGloNorm',[8,9],'iGC',10,... 'M_',struct('T',[0,0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName',... 'Dodgy population main effect: >2 cond''s, 1 scan/cond',... 'n',[1 Inf Inf 1], 'sF',{{'','condition','subject',''}},... 'Hform', 'I(:,2),''-'',''cond''',... 'Bform', 'I(:,3),''-'',''\mu''',... 'nC',[0,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',[1,2,-3],'iGMsca',[-1,9],'GM',50,... 'iGloNorm',[8,9],'iGC',10,... 'M_',struct('T',[0,0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName','Compare-populations: 1 scan/subject (two sample t-test)',... 'n',[Inf 2 1 1], 'sF',{{'subject','group','',''}},... 'Hform', 'I(:,2),''-'',''group''',... 'Bform', 'I(:,3),''-'',''\mu''',... 'nC',[0,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',[1,2,-3],'iGMsca',[-1,9],'GM',50,... 'iGloNorm',[8,9],'iGC',10,... 'M_',struct('T',[0,0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; %-The Full Monty! %----------------------------------------------------------------------- D = [D, struct(... 'DesName','The Full Monty...',... 'n',[Inf Inf Inf Inf], 'sF',{{'repl','cond','subj','group'}},... 'Hform', 'I(:,[4,2]),''-'',{''stud'',''cond''}',... 'Bform', 'I(:,[4,3]),''-'',{''stud'',''subj''}',... 'nC',[Inf,Inf],'iCC',{{[1:8],[1:8]}},'iCFI',{{[1:7],[1:7]}},... 'iGXcalc',[1,2,3],'iGMsca',[1:7],'GM',50,... 'iGloNorm',[1:9],'iGC',[1:11],... 'M_',struct('T',[-Inf,0,0.8*sqrt(-1)],'I',Inf,'X',Inf),... 'b',struct('aTime',1))]; varargout = {D}; case 'desdefs_pet96' %======================================================================= % - SPM96 PET/SPECT Design definitions... %======================================================================= % D = spm_spm_ui('DesDefs_PET96'); %-Single subject %----------------------------------------------------------------------- D = struct(... 'DesName','SPM96:Single-subject: replicated conditions',... 'n', [Inf Inf 1 1], 'sF',{{'repl','condition','',''}},... 'Hform', 'I(:,2),''-'',''cond''',... 'Bform', 'I(:,3),''-'',''\mu''',... 'nC',[0,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',3,'iGMsca',[1,9],'GM',50,... 'iGloNorm',[1,8,9],'iGC',10,... 'M_',struct('T',[0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0)); D = [D, struct(... 'DesName','SPM96:Single-subject: replicated conditions & covariates',... 'n', [Inf Inf 1 1], 'sF',{{'repl','condition','',''}},... 'Hform', 'I(:,2),''-'',''cond''',... 'Bform', 'I(:,3),''-'',''\mu''',... 'nC',[Inf,Inf],'iCC',{{1,1}},'iCFI',{{1,1}},... 'iGXcalc',3,'iGMsca',[1,9],'GM',50,... 'iGloNorm',[1,8,9],'iGC',10,... 'M_',struct('T',[0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName','SPM96:Single-subject: covariates only',... 'n', [Inf 1 1 1], 'sF',{{'repl','','',''}},... 'Hform', '[]',... 'Bform', 'I(:,3),''-'',''\mu''',... 'nC',[Inf,Inf],'iCC',{{1,1}},'iCFI',{{1,1}},... 'iGXcalc',3,'iGMsca',[1,9],'GM',50,... 'iGloNorm',[1,8,9],'iGC',10,... 'M_',struct('T',[0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; %-Multi-subject %----------------------------------------------------------------------- D = [D, struct(... 'DesName','SPM96:Multi-subject: different conditions',... 'n', [1 Inf Inf 1], 'sF',{{'','condition','subject',''}},... 'Hform', 'I(:,2),''-'',''scancond''',... 'Bform', 'I(:,3),''-'',''subj''',... 'nC',[0,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',3,'iGMsca',[1,9],'GM',50,... 'iGloNorm',[1,4,8,9],'iGC',10,... 'M_',struct('T',[0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName','SPM96:Multi-subject: replicated conditions',... 'n',[Inf Inf Inf 1], 'sF',{{'repl','condition','subject',''}},... 'Hform', 'I(:,2),''-'',''cond''',... 'Bform', 'I(:,3),''-'',''subj''',... 'nC',[0,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',3,'iGMsca',[1,9],'GM',50,... 'iGloNorm',[1,4,8,9],'iGC',10,... 'M_',struct('T',[0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName','SPM96:Multi-subject: different conditions & covariates',... 'n', [1 Inf Inf 1], 'sF',{{'','condition','subject',''}},... 'Hform', 'I(:,2),''-'',''cond''',... 'Bform', 'I(:,3),''-'',''subj''',... 'nC',[Inf,Inf],'iCC',{{1,1}},'iCFI',{{[1,4],[1,4]}},... 'iGXcalc',3,'iGMsca',[1,9],'GM',50,... 'iGloNorm',[1,4,8,9],'iGC',10,... 'M_',struct('T',[0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName','SPM96:Multi-subject: replicated conditions & covariates',... 'n',[Inf Inf Inf 1], 'sF',{{'repl','condition','subject',''}},... 'Hform', 'I(:,2),''-'',''condition''',... 'Bform', 'I(:,3),''-'',''subj''',... 'nC',[Inf,Inf],'iCC',{{1,1}},'iCFI',{{[1,3,4],[1,4]}},... 'iGXcalc',3,'iGMsca',[1,9],'GM',50,... 'iGloNorm',[1,4,8,9],'iGC',10,... 'M_',struct('T',[0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName','SPM96:Multi-subject: covariates only',... 'n',[Inf 1 Inf 1], 'sF',{{'repl','','subject',''}},... 'Hform', '[]',... 'Bform', 'I(:,3),''-'',''subj''',... 'nC',[Inf,Inf],'iCC',{{[1,4,8],[1,4,8]}},'iCFI',{{[1,4],[1,4]}},... 'iGXcalc',3,'iGMsca',[1,9],'GM',50,... 'iGloNorm',[1,4,8,9],'iGC',10,... 'M_',struct('T',[0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; %-Multi-study %----------------------------------------------------------------------- D = [D, struct(... 'DesName','SPM96:Multi-study: different conditions',... 'n',[1 Inf Inf Inf], 'sF',{{'','cond','subj','study'}},... 'Hform', 'I(:,[4,2]),''-'',{''study'',''cond''}',... 'Bform', 'I(:,[4,3]),''-'',{''study'',''subj''}',... 'nC',[0,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',3,'iGMsca',[1,5,9],'GM',50,... 'iGloNorm',[1,5,7,8,9],'iGC',10,... 'M_',struct('T',[0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName','SPM96:Multi-study: replicated conditions',... 'n',[Inf Inf Inf Inf], 'sF',{{'repl','cond','subj','study'}},... 'Hform', 'I(:,[4,2]),''-'',{''study'',''condition''}',... 'Bform', 'I(:,[4,3]),''-'',{''study'',''subj''}',... 'nC',[0,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',3,'iGMsca',[1,5,9],'GM',50,... 'iGloNorm',[1,5,7,8,9],'iGC',10,... 'M_',struct('T',[0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName','SPM96:Multi-study: different conditions & covariates',... 'n',[1 Inf Inf Inf], 'sF',{{'','cond','subj','study'}},... 'Hform', 'I(:,[4,2]),''-'',{''study'',''cond''}',... 'Bform', 'I(:,[4,3]),''-'',{''study'',''subj''}',... 'nC',[Inf,Inf],'iCC',{{1,1}},'iCFI',{{[1,5,6,7],[1,5,7]}},... 'iGXcalc',3,'iGMsca',[1,5,9],'GM',50,... 'iGloNorm',[1,5,7,8,9],'iGC',10,... 'M_',struct('T',[0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName','SPM96:Multi-study: replicated conditions & covariates',... 'n',[Inf Inf Inf Inf], 'sF',{{'','cond','subj','study'}},... 'Hform', 'I(:,[4,2]),''-'',{''study'',''condition''}',... 'Bform', 'I(:,[4,3]),''-'',{''study'',''subj''}',... 'nC',[Inf,Inf],'iCC',{{1,1}},'iCFI',{{[1,5,6,7],[1,5,7]}},... 'iGXcalc',3,'iGMsca',[1,5,9],'GM',50,... 'iGloNorm',[1,5,7,8,9],'iGC',10,... 'M_',struct('T',[0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; D = [D, struct(... 'DesName','SPM96:Multi-study: covariates only',... 'n',[Inf 1 Inf Inf], 'sF',{{'repl','','subj','study'}},... 'Hform', '[]',... 'Bform', 'I(:,[4,3]),''-'',{''study'',''subj''}',... 'nC',[Inf,Inf],'iCC',{{1,1}},'iCFI',{{[1,5,7],[1,5,7]}},... 'iGXcalc',3,'iGMsca',[1,5,9],'GM',50,... 'iGloNorm',[1,5,7,8,9],'iGC',10,... 'M_',struct('T',[0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; %-Group comparisons %----------------------------------------------------------------------- D = [D, struct(... 'DesName','SPM96:Compare-groups: 1 scan per subject',... 'n',[Inf Inf 1 1], 'sF',{{'subject','group','',''}},... 'Hform', 'I(:,2),''-'',''group''',... 'Bform', '[]',... 'nC',[0,0],'iCC',{{8,8}},'iCFI',{{1,1}},... 'iGXcalc',3,'iGMsca',[1,9],'GM',50,... 'iGloNorm',[1,8,9],'iGC',10,... 'M_',struct('T',[0.8*sqrt(-1)],'I',0,'X',0),... 'b',struct('aTime',0))]; varargout = {D}; otherwise %======================================================================= % - U N K N O W N A C T I O N %======================================================================= warning(['Illegal Action string: ',Action]) %======================================================================= % - E N D %======================================================================= end %======================================================================= %- S U B - F U N C T I O N S %======================================================================= function str = sf_estrrep(str,srstr) %======================================================================= for i = 1:size(srstr,1) str = strrep(str,srstr{i,1},srstr{i,2}); end