void run() {
value_type cluster_forming_threshold = get_option_value ("threshold", NAN);
value_type tfce_dh = get_option_value ("tfce_dh", DEFAULT_TFCE_DH);
value_type tfce_H = get_option_value ("tfce_h", DEFAULT_TFCE_H);
value_type tfce_E = get_option_value ("tfce_e", DEFAULT_TFCE_E);
int num_perms = get_option_value ("nperms", DEFAULT_PERMUTATIONS);
int nperms_nonstationary = get_option_value ("nperms_nonstationary", DEFAULT_PERMUTATIONS_NONSTATIONARITY);
bool do_26_connectivity = get_options("connectivity").size();
bool do_nonstationary_adjustment = get_options ("nonstationary").size();
// Read filenames
std::vector<std::string> subjects;
{
std::string folder = Path::dirname (argument[0]);
std::ifstream ifs (argument[0].c_str());
std::string temp;
while (getline (ifs, temp))
subjects.push_back (Path::join (folder, temp));
}
// Load design matrix:
Eigen::Matrix<value_type, Eigen::Dynamic, Eigen::Dynamic> design = load_matrix<value_type> (argument[1]);
if (design.rows() != (ssize_t)subjects.size())
throw Exception ("number of input files does not match number of rows in design matrix");
// Load contrast matrix:
Eigen::Matrix<value_type, Eigen::Dynamic, Eigen::Dynamic> contrast = load_matrix<value_type> (argument[2]);
if (contrast.cols() != design.cols())
throw Exception ("the number of contrasts does not equal the number of columns in the design matrix");
// Load Mask
auto header = Header::open (argument[3]);
auto mask_image = header.get_image<value_type>();
Filter::Connector connector (do_26_connectivity);
std::vector<std::vector<int> > mask_indices = connector.precompute_adjacency (mask_image);
const size_t num_vox = mask_indices.size();
Eigen::Matrix<value_type, Eigen::Dynamic, Eigen::Dynamic> data (num_vox, subjects.size());
{
// Load images
ProgressBar progress("loading images", subjects.size());
for (size_t subject = 0; subject < subjects.size(); subject++) {
LogLevelLatch log_level (0);
auto input_image = Image<float>::open(subjects[subject]).with_direct_io (3);
check_dimensions (input_image, mask_image, 0, 3);
int index = 0;
std::vector<std::vector<int> >::iterator it;
for (it = mask_indices.begin(); it != mask_indices.end(); ++it) {
input_image.index(0) = (*it)[0];
input_image.index(1) = (*it)[1];
input_image.index(2) = (*it)[2];
data (index++, subject) = input_image.value();
}
progress++;
}
}
Header output_header (header);
output_header.datatype() = DataType::Float32;
output_header.keyval()["num permutations"] = str(num_perms);
output_header.keyval()["26 connectivity"] = str(do_26_connectivity);
output_header.keyval()["nonstationary adjustment"] = str(do_nonstationary_adjustment);
if (std::isfinite (cluster_forming_threshold)) {
output_header.keyval()["threshold"] = str(cluster_forming_threshold);
} else {
output_header.keyval()["tfce_dh"] = str(tfce_dh);
output_header.keyval()["tfce_e"] = str(tfce_E);
output_header.keyval()["tfce_h"] = str(tfce_H);
}
std::string prefix (argument[4]);
std::string cluster_name (prefix);
if (std::isfinite (cluster_forming_threshold))
cluster_name.append ("cluster_sizes.mif");
else
cluster_name.append ("tfce.mif");
auto cluster_image = Image<value_type>::create (cluster_name, output_header);
auto tvalue_image = Image<value_type>::create (prefix + "tvalue.mif", output_header);
auto fwe_pvalue_image = Image<value_type>::create (prefix + "fwe_pvalue.mif", output_header);
auto uncorrected_pvalue_image = Image<value_type>::create (prefix + "uncorrected_pvalue.mif", output_header);
Image<value_type> cluster_image_neg;
Image<value_type> fwe_pvalue_image_neg;
Image<value_type> uncorrected_pvalue_image_neg;
Eigen::Matrix<value_type, Eigen::Dynamic, 1> perm_distribution (num_perms);
std::shared_ptr<Eigen::Matrix<value_type, Eigen::Dynamic, 1> > perm_distribution_neg;
std::vector<value_type> default_cluster_output (num_vox, 0.0);
std::shared_ptr<std::vector<value_type> > default_cluster_output_neg;
std::vector<value_type> tvalue_output (num_vox, 0.0);
std::shared_ptr<std::vector<double> > empirical_tfce_statistic;
std::vector<value_type> uncorrected_pvalue (num_vox, 0.0);
std::shared_ptr<std::vector<value_type> > uncorrected_pvalue_neg;
bool compute_negative_contrast = get_options("negative").size() ? true : false;
if (compute_negative_contrast) {
std::string cluster_neg_name (prefix);
if (std::isfinite (cluster_forming_threshold))
cluster_neg_name.append ("cluster_sizes_neg.mif");
else
cluster_neg_name.append ("tfce_neg.mif");
cluster_image_neg = Image<value_type>::create (cluster_neg_name, output_header);
perm_distribution_neg.reset (new Eigen::Matrix<value_type, Eigen::Dynamic, 1> (num_perms));
default_cluster_output_neg.reset (new std::vector<value_type> (num_vox, 0.0));
fwe_pvalue_image_neg = Image<value_type>::create (prefix + "fwe_pvalue_neg.mif", output_header);
uncorrected_pvalue_neg.reset (new std::vector<value_type> (num_vox, 0.0));
uncorrected_pvalue_image_neg = Image<value_type>::create (prefix + "uncorrected_pvalue_neg.mif", output_header);
}
{ // Do permutation testing:
Math::Stats::GLMTTest glm (data, design, contrast);
// Suprathreshold clustering
if (std::isfinite (cluster_forming_threshold)) {
if (do_nonstationary_adjustment)
throw Exception ("nonstationary adjustment is not currently implemented for threshold-based cluster analysis");
Stats::Cluster::ClusterSize cluster_size_test (connector, cluster_forming_threshold);
Stats::PermTest::precompute_default_permutation (glm, cluster_size_test, empirical_tfce_statistic,
default_cluster_output, default_cluster_output_neg, tvalue_output);
Stats::PermTest::run_permutations (glm, cluster_size_test, num_perms, empirical_tfce_statistic,
default_cluster_output, default_cluster_output_neg,
perm_distribution, perm_distribution_neg,
uncorrected_pvalue, uncorrected_pvalue_neg);
// TFCE
} else {
Stats::TFCE::Enhancer tfce_integrator (connector, tfce_dh, tfce_E, tfce_H);
if (do_nonstationary_adjustment) {
empirical_tfce_statistic.reset (new std::vector<double> (num_vox, 0.0));
Stats::PermTest::precompute_empirical_stat (glm, tfce_integrator, nperms_nonstationary, *empirical_tfce_statistic);
}
Stats::PermTest::precompute_default_permutation (glm, tfce_integrator, empirical_tfce_statistic,
default_cluster_output, default_cluster_output_neg, tvalue_output);
Stats::PermTest::run_permutations (glm, tfce_integrator, num_perms, empirical_tfce_statistic,
default_cluster_output, default_cluster_output_neg,
perm_distribution, perm_distribution_neg,
uncorrected_pvalue, uncorrected_pvalue_neg);
}
}
save_matrix (perm_distribution, prefix + "perm_dist.txt");
std::vector<value_type> pvalue_output (num_vox, 0.0);
Math::Stats::statistic2pvalue (perm_distribution, default_cluster_output, pvalue_output);
{
ProgressBar progress ("generating output");
for (size_t i = 0; i < num_vox; i++) {
for (size_t dim = 0; dim < cluster_image.ndim(); dim++)
tvalue_image.index(dim) = cluster_image.index(dim) = fwe_pvalue_image.index(dim) = uncorrected_pvalue_image.index(dim) = mask_indices[i][dim];
tvalue_image.value() = tvalue_output[i];
cluster_image.value() = default_cluster_output[i];
fwe_pvalue_image.value() = pvalue_output[i];
uncorrected_pvalue_image.value() = uncorrected_pvalue[i];
}
}
{
if (compute_negative_contrast) {
save_matrix (*perm_distribution_neg, prefix + "perm_dist_neg.txt");
std::vector<value_type> pvalue_output_neg (num_vox, 0.0);
Math::Stats::statistic2pvalue (*perm_distribution_neg, *default_cluster_output_neg, pvalue_output_neg);
ProgressBar progress ("generating negative contrast output");
for (size_t i = 0; i < num_vox; i++) {
for (size_t dim = 0; dim < cluster_image.ndim(); dim++)
cluster_image_neg.index(dim) = fwe_pvalue_image_neg.index(dim) = uncorrected_pvalue_image_neg.index(dim) = mask_indices[i][dim];
cluster_image_neg.value() = (*default_cluster_output_neg)[i];
fwe_pvalue_image_neg.value() = pvalue_output_neg[i];
uncorrected_pvalue_image_neg.value() = (*uncorrected_pvalue_neg)[i];
}
}
}
}
void run() {
const value_type cluster_forming_threshold = get_option_value ("threshold", NaN);
const value_type tfce_dh = get_option_value ("tfce_dh", DEFAULT_TFCE_DH);
const value_type tfce_H = get_option_value ("tfce_h", DEFAULT_TFCE_H);
const value_type tfce_E = get_option_value ("tfce_e", DEFAULT_TFCE_E);
const bool use_tfce = !std::isfinite (cluster_forming_threshold);
int num_perms = get_option_value ("nperms", DEFAULT_NUMBER_PERMUTATIONS);
int nperms_nonstationary = get_option_value ("nperms_nonstationary", DEFAULT_NUMBER_PERMUTATIONS_NONSTATIONARITY);
const bool do_26_connectivity = get_options("connectivity").size();
const bool do_nonstationary_adjustment = get_options ("nonstationary").size();
// Read filenames
vector<std::string> subjects;
{
std::string folder = Path::dirname (argument[0]);
std::ifstream ifs (argument[0].c_str());
std::string temp;
while (getline (ifs, temp))
subjects.push_back (Path::join (folder, temp));
}
// Load design matrix
const matrix_type design = load_matrix<value_type> (argument[1]);
if (design.rows() != (ssize_t)subjects.size())
throw Exception ("number of input files does not match number of rows in design matrix");
// Load permutations file if supplied
auto opt = get_options("permutations");
vector<vector<size_t> > permutations;
if (opt.size()) {
permutations = Math::Stats::Permutation::load_permutations_file (opt[0][0]);
num_perms = permutations.size();
if (permutations[0].size() != (size_t)design.rows())
throw Exception ("number of rows in the permutations file (" + str(opt[0][0]) + ") does not match number of rows in design matrix");
}
// Load non-stationary correction permutations file if supplied
opt = get_options("permutations_nonstationary");
vector<vector<size_t> > permutations_nonstationary;
if (opt.size()) {
permutations_nonstationary = Math::Stats::Permutation::load_permutations_file (opt[0][0]);
nperms_nonstationary = permutations.size();
if (permutations_nonstationary[0].size() != (size_t)design.rows())
throw Exception ("number of rows in the nonstationary permutations file (" + str(opt[0][0]) + ") does not match number of rows in design matrix");
}
// Load contrast matrix
const matrix_type contrast = load_matrix<value_type> (argument[2]);
if (contrast.cols() != design.cols())
throw Exception ("the number of contrasts does not equal the number of columns in the design matrix");
auto mask_header = Header::open (argument[3]);
// Load Mask and compute adjacency
auto mask_image = mask_header.get_image<value_type>();
Filter::Connector connector (do_26_connectivity);
vector<vector<int> > mask_indices = connector.precompute_adjacency (mask_image);
const size_t num_vox = mask_indices.size();
matrix_type data (num_vox, subjects.size());
{
// Load images
ProgressBar progress("loading images", subjects.size());
for (size_t subject = 0; subject < subjects.size(); subject++) {
LogLevelLatch log_level (0);
auto input_image = Image<float>::open (subjects[subject]); //.with_direct_io (3); <- Should be inputting 3D images?
check_dimensions (input_image, mask_image, 0, 3);
int index = 0;
vector<vector<int> >::iterator it;
for (it = mask_indices.begin(); it != mask_indices.end(); ++it) {
input_image.index(0) = (*it)[0];
input_image.index(1) = (*it)[1];
input_image.index(2) = (*it)[2];
data (index++, subject) = input_image.value();
}
progress++;
}
}
if (!data.allFinite())
WARN ("input data contains non-finite value(s)");
Header output_header (mask_header);
output_header.datatype() = DataType::Float32;
output_header.keyval()["num permutations"] = str(num_perms);
output_header.keyval()["26 connectivity"] = str(do_26_connectivity);
output_header.keyval()["nonstationary adjustment"] = str(do_nonstationary_adjustment);
if (use_tfce) {
output_header.keyval()["tfce_dh"] = str(tfce_dh);
output_header.keyval()["tfce_e"] = str(tfce_E);
output_header.keyval()["tfce_h"] = str(tfce_H);
} else {
output_header.keyval()["threshold"] = str(cluster_forming_threshold);
}
const std::string prefix (argument[4]);
bool compute_negative_contrast = get_options("negative").size();
vector_type default_cluster_output (num_vox);
std::shared_ptr<vector_type> default_cluster_output_neg;
vector_type tvalue_output (num_vox);
vector_type empirical_enhanced_statistic;
if (compute_negative_contrast)
default_cluster_output_neg.reset (new vector_type (num_vox));
Math::Stats::GLMTTest glm (data, design, contrast);
std::shared_ptr<Stats::EnhancerBase> enhancer;
if (use_tfce) {
std::shared_ptr<Stats::TFCE::EnhancerBase> base (new Stats::Cluster::ClusterSize (connector, cluster_forming_threshold));
enhancer.reset (new Stats::TFCE::Wrapper (base, tfce_dh, tfce_E, tfce_H));
} else {
enhancer.reset (new Stats::Cluster::ClusterSize (connector, cluster_forming_threshold));
}
if (do_nonstationary_adjustment) {
if (!use_tfce)
throw Exception ("nonstationary adjustment is not currently implemented for threshold-based cluster analysis");
empirical_enhanced_statistic = vector_type::Zero (num_vox);
if (permutations_nonstationary.size()) {
Stats::PermTest::PermutationStack permutations (permutations_nonstationary, "precomputing empirical statistic for non-stationarity adjustment...");
Stats::PermTest::precompute_empirical_stat (glm, enhancer, permutations, empirical_enhanced_statistic);
} else {
Stats::PermTest::PermutationStack permutations (nperms_nonstationary, design.rows(), "precomputing empirical statistic for non-stationarity adjustment...", false);
Stats::PermTest::precompute_empirical_stat (glm, enhancer, permutations, empirical_enhanced_statistic);
}
save_matrix (empirical_enhanced_statistic, prefix + "empirical.txt");
}
Stats::PermTest::precompute_default_permutation (glm, enhancer, empirical_enhanced_statistic,
default_cluster_output, default_cluster_output_neg, tvalue_output);
{
ProgressBar progress ("generating pre-permutation output", (compute_negative_contrast ? 3 : 2) + contrast.cols() + 3);
{
auto tvalue_image = Image<float>::create (prefix + "tvalue.mif", output_header);
write_output (tvalue_output, mask_indices, tvalue_image);
}
++progress;
{
auto cluster_image = Image<float>::create (prefix + (use_tfce ? "tfce.mif" : "cluster_sizes.mif"), output_header);
write_output (default_cluster_output, mask_indices, cluster_image);
}
++progress;
if (compute_negative_contrast) {
assert (default_cluster_output_neg);
auto cluster_image_neg = Image<float>::create (prefix + (use_tfce ? "tfce_neg.mif" : "cluster_sizes_neg.mif"), output_header);
write_output (*default_cluster_output_neg, mask_indices, cluster_image_neg);
++progress;
}
auto temp = Math::Stats::GLM::solve_betas (data, design);
for (ssize_t i = 0; i < contrast.cols(); ++i) {
auto beta_image = Image<float>::create (prefix + "beta" + str(i) + ".mif", output_header);
write_output (temp.row(i), mask_indices, beta_image);
++progress;
}
{
const auto temp = Math::Stats::GLM::abs_effect_size (data, design, contrast);
auto abs_effect_image = Image<float>::create (prefix + "abs_effect.mif", output_header);
write_output (temp.row(0), mask_indices, abs_effect_image);
}
++progress;
{
const auto temp = Math::Stats::GLM::std_effect_size (data, design, contrast);
auto std_effect_image = Image<float>::create (prefix + "std_effect.mif", output_header);
write_output (temp.row(0), mask_indices, std_effect_image);
}
++progress;
{
const auto temp = Math::Stats::GLM::stdev (data, design);
auto std_dev_image = Image<float>::create (prefix + "std_dev.mif", output_header);
write_output (temp.row(0), mask_indices, std_dev_image);
}
}
if (!get_options ("notest").size()) {
vector_type perm_distribution (num_perms);
std::shared_ptr<vector_type> perm_distribution_neg;
vector_type uncorrected_pvalue (num_vox);
std::shared_ptr<vector_type> uncorrected_pvalue_neg;
if (compute_negative_contrast) {
perm_distribution_neg.reset (new vector_type (num_perms));
uncorrected_pvalue_neg.reset (new vector_type (num_vox));
}
if (permutations.size()) {
Stats::PermTest::run_permutations (permutations, glm, enhancer, empirical_enhanced_statistic,
default_cluster_output, default_cluster_output_neg,
perm_distribution, perm_distribution_neg,
uncorrected_pvalue, uncorrected_pvalue_neg);
} else {
Stats::PermTest::run_permutations (num_perms, glm, enhancer, empirical_enhanced_statistic,
default_cluster_output, default_cluster_output_neg,
perm_distribution, perm_distribution_neg,
uncorrected_pvalue, uncorrected_pvalue_neg);
}
save_matrix (perm_distribution, prefix + "perm_dist.txt");
if (compute_negative_contrast) {
assert (perm_distribution_neg);
save_matrix (*perm_distribution_neg, prefix + "perm_dist_neg.txt");
}
ProgressBar progress ("generating output", compute_negative_contrast ? 4 : 2);
{
auto uncorrected_pvalue_image = Image<float>::create (prefix + "uncorrected_pvalue.mif", output_header);
write_output (uncorrected_pvalue, mask_indices, uncorrected_pvalue_image);
}
++progress;
{
vector_type fwe_pvalue_output (num_vox);
Math::Stats::Permutation::statistic2pvalue (perm_distribution, default_cluster_output, fwe_pvalue_output);
auto fwe_pvalue_image = Image<float>::create (prefix + "fwe_pvalue.mif", output_header);
write_output (fwe_pvalue_output, mask_indices, fwe_pvalue_image);
}
++progress;
if (compute_negative_contrast) {
assert (uncorrected_pvalue_neg);
assert (perm_distribution_neg);
auto uncorrected_pvalue_image_neg = Image<float>::create (prefix + "uncorrected_pvalue_neg.mif", output_header);
write_output (*uncorrected_pvalue_neg, mask_indices, uncorrected_pvalue_image_neg);
++progress;
vector_type fwe_pvalue_output_neg (num_vox);
Math::Stats::Permutation::statistic2pvalue (*perm_distribution_neg, *default_cluster_output_neg, fwe_pvalue_output_neg);
auto fwe_pvalue_image_neg = Image<float>::create (prefix + "fwe_pvalue_neg.mif", output_header);
write_output (fwe_pvalue_output_neg, mask_indices, fwe_pvalue_image_neg);
}
}
}
