文件准备
mask准备
协变量

clc; clear;
spm('Defaults', 'fMRI');
spm_jobman('initcfg');

% 定义基础路径和协变量路径
base_dir = 'E:\ICA2025\ica2025-1\recovery_vs_acute';
cov_dir = fullfile(base_dir, 'covariates');

% 加载协变量数据（假设已调整为每个扫描的协变量，共126个）
age = load(fullfile(cov_dir, 'Age.txt'));    % 需确保是126x1向量，每个被试重复两次
sex = load(fullfile(cov_dir, 'Sex.txt'));    % 同上
edu = load(fullfile(cov_dir, 'Edu.txt'));    % 同上

% 定义要分析的IC列表
ic_numbers = [2,3,4,5,6,10,11,12,16,17,18,21,22,24,27,30,31]; 

for ic_num = ic_numbers
    % ========== 动态生成路径 ==========
    result_dir = fullfile(base_dir, sprintf('result3_ica_ic%d_paired', ic_num));
    if ~exist(result_dir, 'dir')
        mkdir(result_dir);
    end
    
    mask_file = fullfile(base_dir, 'compute_mask\result3_ica_ICs_mask',...
        sprintf('IC%d_mask.nii,1', ic_num));
    
    % ========== 生成文件列表（配対结构）==========
    data_dir = fullfile(base_dir, 'result3_189');
    
    % 生成Group1和Group2文件列表（假设sub001-063为条件1，sub127-189为对应的条件2）
    scans_condition1 = cell(63,1);
    scans_condition2 = cell(63,1);
    for i = 1:63
        sub_cond1 = sprintf('sub%03d', i);
        sub_cond2 = sprintf('sub%03d', i+126); % 假设sub127对应sub001的配对
        scans_condition1{i} = fullfile(data_dir,...
            sprintf('ica3_%s_component_ica_s1_.nii,%d', sub_cond1, ic_num));
        scans_condition2{i} = fullfile(data_dir,...
            sprintf('ica3_%s_component_ica_s1_.nii,%d', sub_cond2, ic_num));
    end
    
    % ========== 配置分析流程 ==========
    matlabbatch = {};
    
    % 步骤1: 指定配对设计模型
    matlabbatch{1}.spm.stats.factorial_design.dir = {result_dir};
    matlabbatch{1}.spm.stats.factorial_design.des.pt.pair = struct('scans', {}, 'levels', {});
    
    % 为每个被试添加扫描对
    for subj = 1:63
        matlabbatch{1}.spm.stats.factorial_design.des.pt.pair(subj).scans = {
            scans_condition1{subj}
            scans_condition2{subj}
        };
    end
    
    % 协变量设置（需确保长度与总扫描数一致，此处为126）
    matlabbatch{1}.spm.stats.factorial_design.cov(1).c = age;
    matlabbatch{1}.spm.stats.factorial_design.cov(1).cname = 'age';
    matlabbatch{1}.spm.stats.factorial_design.cov(1).iCFI = 1;
    matlabbatch{1}.spm.stats.factorial_design.cov(1).iCC = 1;
    
    matlabbatch{1}.spm.stats.factorial_design.cov(2).c = sex;
    matlabbatch{1}.spm.stats.factorial_design.cov(2).cname = 'sex';
    matlabbatch{1}.spm.stats.factorial_design.cov(2).iCFI = 1;
    matlabbatch{1}.spm.stats.factorial_design.cov(2).iCC = 1;
    
    matlabbatch{1}.spm.stats.factorial_design.cov(3).c = edu;
    matlabbatch{1}.spm.stats.factorial_design.cov(3).cname = 'edu';
    matlabbatch{1}.spm.stats.factorial_design.cov(3).iCFI = 1;
    matlabbatch{1}.spm.stats.factorial_design.cov(3).iCC = 1;
    
    % 掩膜及其他设置
    matlabbatch{1}.spm.stats.factorial_design.masking.tm.tm_none = 1;
    matlabbatch{1}.spm.stats.factorial_design.masking.im = 1;
    matlabbatch{1}.spm.stats.factorial_design.masking.em = {mask_file};
    matlabbatch{1}.spm.stats.factorial_design.globalc.g_omit = 1;
    matlabbatch{1}.spm.stats.factorial_design.globalm.gmsca.gmsca_no = 1;
    matlabbatch{1}.spm.stats.factorial_design.globalm.glonorm = 1;
    
    % 步骤2: 参数估计
    matlabbatch{2}.spm.stats.fmri_est.spmmat = {fullfile(result_dir, 'SPM.mat')};
    matlabbatch{2}.spm.stats.fmri_est.write_residuals = 0;
    matlabbatch{2}.spm.stats.fmri_est.method.Classical = 1;
    
    % 步骤3: 对比定义（配对T检验对比）
    matlabbatch{3}.spm.stats.con.spmmat = {fullfile(result_dir, 'SPM.mat')};
    matlabbatch{3}.spm.stats.con.consess{1}.tcon.name = 'Condition1 > Condition2';
    matlabbatch{3}.spm.stats.con.consess{1}.tcon.weights = [1 -1];
    matlabbatch{3}.spm.stats.con.consess{1}.tcon.sessrep = 'none';
    matlabbatch{3}.spm.stats.con.delete = 0;
    
    % 步骤4: 结果报告
    matlabbatch{4}.spm.stats.results.spmmat = {fullfile(result_dir, 'SPM.mat')};
    matlabbatch{4}.spm.stats.results.conspec.contrasts = 1;
    matlabbatch{4}.spm.stats.results.conspec.threshdesc = 'none';
    matlabbatch{4}.spm.stats.results.conspec.thresh = 0.001;
    matlabbatch{4}.spm.stats.results.conspec.extent = 0;
    matlabbatch{4}.spm.stats.results.conspec.mask = {mask_file};
    matlabbatch{4}.spm.stats.results.units = 1;
    matlabbatch{4}.spm.stats.results.print = 'ps';
    
    % ========== 运行分析 ==========
    try
        spm_jobman('run', matlabbatch);
        fprintf('IC%d 分析成功完成\n', ic_num);
    catch ME
        fprintf('IC%d 分析失败: %s\n', ic_num, ME.message);
    end
end
disp('所有IC分析完成！');

#接下来可以用xjview卡阈值voxel20/FDR0.05

双样本T检验

clc; clear;
spm('Defaults', 'fMRI');
spm_jobman('initcfg');

% 定义基础路径和协变量路径
base_dir = 'E:\ICA2025\ica2025-1\recovery_vs_acute';
cov_dir = fullfile(base_dir, 'covariates');

% 加载协变量数据
age = load(fullfile(cov_dir, 'Age.txt'));
sex = load(fullfile(cov_dir, 'Sex.txt'));
edu = load(fullfile(cov_dir, 'Edu.txt'));

% 定义要分析的IC列表
%ic_numbers = 2;  % 根据需要修改IC编号范围
ic_numbers = [2,3,4,5,6,10,11,12,16,17,18,21,22,24,27,30,31]; 
for ic_num = ic_numbers
    % ========== 动态生成路径 ==========
    % 结果目录
    result_dir = fullfile(base_dir, sprintf('result3_ica_ic%d_t2', ic_num));
    if ~exist(result_dir, 'dir')
        mkdir(result_dir);
    end
    
    % 掩膜文件路径
    mask_file = fullfile(base_dir, 'compute_mask\result3_ica_ICs_mask',...
        sprintf('IC%d_mask.nii,1', ic_num));
    
    % ========== 生成文件列表 ==========
    data_dir = fullfile(base_dir, 'result3_189');
    
    % 生成Group1文件列表（001-063）
    scans1 = cell(63,1);
    for i = 1:63
        sub_str = sprintf('sub%03d', i);
        scans1{i} = fullfile(data_dir,...
            sprintf('ica3_%s_component_ica_s1_.nii,%d', sub_str, ic_num)); % 根据实际文件名调整
    end
    
    % 生成Group2文件列表（064-126）
    scans2 = cell(63,1);
    for i = 127:189
        sub_str = sprintf('sub%03d', i);
        scans2{i-126} = fullfile(data_dir,...
            sprintf('ica3_%s_component_ica_s1_.nii,%d', sub_str, ic_num)); % 根据实际文件名调整
    end
    
    % ========== 配置分析流程 ==========
    matlabbatch = {};
    
    % 步骤1: 指定模型
    matlabbatch{1}.spm.stats.factorial_design.dir = {result_dir};
    matlabbatch{1}.spm.stats.factorial_design.des.t2.scans1 = scans1;
    matlabbatch{1}.spm.stats.factorial_design.des.t2.scans2 = scans2;
    matlabbatch{1}.spm.stats.factorial_design.des.t2.dept = 0;
    matlabbatch{1}.spm.stats.factorial_design.des.t2.variance = 1;
    matlabbatch{1}.spm.stats.factorial_design.des.t2.gmsca = 0;
    matlabbatch{1}.spm.stats.factorial_design.des.t2.ancova = 0;
    
    % 协变量设置
    matlabbatch{1}.spm.stats.factorial_design.cov(1).c = age;
    matlabbatch{1}.spm.stats.factorial_design.cov(1).cname = 'age';
    matlabbatch{1}.spm.stats.factorial_design.cov(1).iCFI = 1;
    matlabbatch{1}.spm.stats.factorial_design.cov(1).iCC = 1;
    
    matlabbatch{1}.spm.stats.factorial_design.cov(2).c = sex;
    matlabbatch{1}.spm.stats.factorial_design.cov(2).cname = 'sex';
    matlabbatch{1}.spm.stats.factorial_design.cov(2).iCFI = 1;
    matlabbatch{1}.spm.stats.factorial_design.cov(2).iCC = 1;
    
    matlabbatch{1}.spm.stats.factorial_design.cov(3).c = edu;
    matlabbatch{1}.spm.stats.factorial_design.cov(3).cname = 'edu';
    matlabbatch{1}.spm.stats.factorial_design.cov(3).iCFI = 1;
    matlabbatch{1}.spm.stats.factorial_design.cov(3).iCC = 1;
    
    % 掩膜设置
    matlabbatch{1}.spm.stats.factorial_design.masking.tm.tm_none = 1;
    matlabbatch{1}.spm.stats.factorial_design.masking.im = 1;
    matlabbatch{1}.spm.stats.factorial_design.masking.em = {mask_file};
    matlabbatch{1}.spm.stats.factorial_design.globalc.g_omit = 1;
    matlabbatch{1}.spm.stats.factorial_design.globalm.gmsca.gmsca_no = 1;
    matlabbatch{1}.spm.stats.factorial_design.globalm.glonorm = 1;
    
    % 步骤2: 参数估计
    matlabbatch{2}.spm.stats.fmri_est.spmmat = {fullfile(result_dir, 'SPM.mat')};
    matlabbatch{2}.spm.stats.fmri_est.write_residuals = 0;
    matlabbatch{2}.spm.stats.fmri_est.method.Classical = 1;
    
    % 步骤3: 对比定义
    matlabbatch{3}.spm.stats.con.spmmat = {fullfile(result_dir, 'SPM.mat')};
    matlabbatch{3}.spm.stats.con.consess{1}.tcon.name = 'Group1 > Group2';
    matlabbatch{3}.spm.stats.con.consess{1}.tcon.weights = [1 -1];
    matlabbatch{3}.spm.stats.con.consess{1}.tcon.sessrep = 'none';
    matlabbatch{3}.spm.stats.con.delete = 0;
    
    % 步骤4: 结果报告
    matlabbatch{4}.spm.stats.results.spmmat = {fullfile(result_dir, 'SPM.mat')};
    matlabbatch{4}.spm.stats.results.conspec.contrasts = 1;
    matlabbatch{4}.spm.stats.results.conspec.threshdesc = 'none';
    matlabbatch{4}.spm.stats.results.conspec.thresh = 0.001;
    matlabbatch{4}.spm.stats.results.conspec.extent = 0;
    matlabbatch{4}.spm.stats.results.conspec.mask = {mask_file};
    matlabbatch{4}.spm.stats.results.units = 1;
    matlabbatch{4}.spm.stats.results.print = 'ps';
    
    % ========== 运行分析 ==========
    try
        for step = 1:4
            spm_jobman('run', matlabbatch(step));
        end
        fprintf('IC%d 分析成功完成\n', ic_num);
    catch ME
        fprintf('IC%d 分析失败: %s\n', ic_num, ME.message);
    end
end
disp('所有IC分析完成！');