%% Modified by Leehyun Yoon 20161111 (Original ver. Bob Spunt, 20111107) %% ==== Let's make VOI (spehre image) clc; clear all; close all; subjectDir = {'Parti_1', 'Parti_2','Parti_3','Parti_4','Parti_5','Parti_6','Parti_7','Parti_8','Parti_9', 'Parti_10', 'Parti_11','Parti_12','Parti_13','Parti_14','Parti_15','Parti_16','Parti_17','Parti_18','Parti_19', 'Parti_21','Parti_22','Parti_23','Parti_24','Parti_25','Parti_26','Parti_27','Parti_28','Parti_29','Parti_30', 'Parti_31', 'Parti_32', 'Parti_33', 'Parti_34', 'Parti_35', 'Parti_36', 'Parti_37', 'Parti_38', 'Parti_40', 'Parti_41', 'Parti_42', 'Parti_43', 'Parti_44', 'Parti_45', 'Parti_46', 'Parti_47', 'Parti_48', 'Parti_49', 'Parti_50', 'Parti_51', 'Parti_52', 'Parti_53', 'Parti_54', 'Parti_55', 'Parti_56', 'Parti_57', 'Parti_58', 'Parti_59', 'Parti_60'}; % subject name % loop for multi subjects baseDir = '/Volumes/MGTEC/NFR_60Devel_Analysis'; cd (baseDir) addpath('/Volumes/MGTEC/NFR_60Devel_Analysis/Parti_1/Analysis_10th/cnpFB_parametric_onlythisparameter'); create_sphere_image('SPM.mat', [-24 60 0], 'IntermediateFPC_-24_50_0_r0', 0); clear all; % USER INPUT: Paths, file-finding patterns, and analysis parameters %---------------------------------------------------------------------% % -----------Paths----------- gPPIpath = '/Users/YoonLeehyun/Documents/PPPIv13.1/PPPIv13'; % path for gPPI toolbox baseDir = '/Volumes/MGTEC/NFR_60Devel_Analysis'; % path for study directory spmpath=fileparts(which('spm.m')); spm('defaults','fmri'); % maskpath = '/Applications/fmri/data/matchrest/_rois_'; % path for folder containing ROIs maskpath = '/Volumes/MGTEC/NFR_60Devel_Analysis'; % path for folder containing ROIs level1path = 'Analysis_10th/cnpFB_parametric_onlythisparameter'; % path of level analysis (relative to subject folder) % -----------Patterns for finding files and folders ----------- % subjectpattern = 'Parti_*'; % pattern for finding subject folders (use wildcards) subjectDir = {'Parti_1', 'Parti_2','Parti_3','Parti_4','Parti_5','Parti_6','Parti_7','Parti_8','Parti_9', 'Parti_10', 'Parti_11','Parti_12','Parti_13','Parti_14','Parti_15','Parti_16','Parti_17','Parti_18','Parti_19', 'Parti_21','Parti_22','Parti_23','Parti_24','Parti_25','Parti_26','Parti_27','Parti_28','Parti_29','Parti_30', 'Parti_31', 'Parti_32', 'Parti_33', 'Parti_34', 'Parti_35', 'Parti_36', 'Parti_37', 'Parti_38', 'Parti_40', 'Parti_41', 'Parti_42', 'Parti_43', 'Parti_44', 'Parti_45', 'Parti_46', 'Parti_47', 'Parti_48', 'Parti_49', 'Parti_50', 'Parti_51', 'Parti_52', 'Parti_53', 'Parti_54', 'Parti_55', 'Parti_56', 'Parti_57', 'Parti_58', 'Parti_59', 'Parti_60'}; % subject name %subTAG = [1:19, 21:38, 40:60]; %% maskpattern = '*nii'; % pattern for finding image files in ROI directory % -----------General PPI analysis parameters----------- spmversion = 8; % version of spm (5 or 8) estimateTAG = 1; % estimate the level 1 models? (1 = yes, 0 = no) ppi_folder_affix = 'cond'; % string to affix to name of your PPI analysis folder % -----------Defining the conditions (psychological effects)----------- method = 'cond'; % 'trad' for traditional method, 'cond' for generalized (condition-specific) method conditions = {'FB.cnpFB'}; % conditions to compute PPIs for (must match names used in level 1 analysis) % weights = [1]; % weights to apply to conditions in traditional PPI (ignore if doing condition-spec) % -----------Definining the seed timecourse (physiological effect)----------- % fcontrast = 0; % in my case, I will do default advised by https://www.nitrc.org/forum/message.php?msg_id=16257 . F contrast to adjust for (corresponds to number of the ess image in level 1 folder) extract='eig'; % extract timecourse as first eigenvariate ('eig') or mean eigenvariate ('mean') default is eig masks={'IntermediateFPC_-24_50_0_r0_mask.nii'}; % name of images to use to constrain definition of the seed region threshold = [0.99]; % values to threshold the images above at (number of values must match number of masks) VOImin = 0; % minimum number of voxels to accept a seed region as valid %---------------------------------------------------------------------% addpath(gPPIpath) % Find subjects %---------------------------------------------------------------------% cd(baseDir); fprintf('\nSUBJECT LIST:\n'); d=subjectDir; for i=1:length(d) subnam{i}=d(i); subdir{i} = [baseDir '/' subnam{i}]; % fprintf('\tAdding %s to subject list\n',subnam{i}) end nsubs = length(subnam); dosubs= 1:nsubs; % Find mask files %---------------------------------------------------------------------% cd(maskpath) fprintf('\n\nMASK LIST:\n'); d=dir(maskpattern); for i=1:length(d) voiNAMES{i}=d(i).name; voi_anal_names{i} = voiNAMES{i}(1:end-4); % fprintf('\tAdding %s to mask list\n',voiNAMES{i}) end nmasks = length(voiNAMES); domasks = 1:nmasks; %---------------------------------------------------------------------% %---------------------------------------------------------------------% % Begin looping through regions and subjects %---------------------------------------------------------------------% for v=domasks for i=dosubs try cd(gPPIpath) % load('parameters.mat') P.subject=subnam{i}; P.VOI.VOI = [maskpath filesep voiNAMES{v}]; P.VOI.masks = masks; P.VOI.thresh = threshold; P.VOI.min = VOImin; P.VOImin = VOImin; P.Region = [voi_anal_names{v} '_' ppi_folder_affix]; P.SPMver = spmversion; P.directory=[subdir{i} filesep level1path]; P.method = method; P.Estimate = estimateTAG; % P.contrast = fcontrast; %% I would do this default P.extract = extract; if strcmp(method,'trad') P.Tasks = conditions; P.Weights = weights; P.Contrasts(1).name = 'PPI'; P.Contrasts(1).left = convec; P.Contrasts(1).right = []; else P.Tasks = ['0' conditions]; end if strcmp(method,'cond') % USER INPUT: Specify your contrasts of interest (Cond PPI only) %--------------------------------------------------------------% % P.Contrasts(1).name = 'FB.cnpFB'; P.Contrasts(1).left = {'FB.cnpFB'}; P.Contrasts(1).right = {'none'}; %--------------------------------------------------------------% end % now run! PPPI(P) end end end