/* MELODIC - Multivariate exploratory linear optimized decomposition into
independent components
meldata.cc - data handler / container class
Christian F. Beckmann, FMRIB Image Analysis Group
Copyright (C) 1999-2008 University of Oxford */
// {{{ includes/namespaces
/* Part of FSL - FMRIB's Software Library
http://www.fmrib.ox.ac.uk/fsl
fsl@fmrib.ox.ac.uk
Developed at FMRIB (Oxford Centre for Functional Magnetic Resonance
Imaging of the Brain), Department of Clinical Neurology, Oxford
University, Oxford, UK
LICENCE
FMRIB Software Library, Release 4.0 (c) 2007, The University of
Oxford (the "Software")
The Software remains the property of the University of Oxford ("the
University").
The Software is distributed "AS IS" under this Licence solely for
non-commercial use in the hope that it will be useful, but in order
that the University as a charitable foundation protects its assets for
the benefit of its educational and research purposes, the University
makes clear that no condition is made or to be implied, nor is any
warranty given or to be implied, as to the accuracy of the Software,
or that it will be suitable for any particular purpose or for use
under any specific conditions. Furthermore, the University disclaims
all responsibility for the use which is made of the Software. It
further disclaims any liability for the outcomes arising from using
the Software.
The Licensee agrees to indemnify the University and hold the
University harmless from and against any and all claims, damages and
liabilities asserted by third parties (including claims for
negligence) which arise directly or indirectly from the use of the
Software or the sale of any products based on the Software.
No part of the Software may be reproduced, modified, transmitted or
transferred in any form or by any means, electronic or mechanical,
without the express permission of the University. The permission of
the University is not required if the said reproduction, modification,
transmission or transference is done without financial return, the
conditions of this Licence are imposed upon the receiver of the
product, and all original and amended source code is included in any
transmitted product. You may be held legally responsible for any
copyright infringement that is caused or encouraged by your failure to
abide by these terms and conditions.
You are not permitted under this Licence to use this Software
commercially. Use for which any financial return is received shall be
defined as commercial use, and includes (1) integration of all or part
of the source code or the Software into a product for sale or license
by or on behalf of Licensee to third parties or (2) use of the
Software or any derivative of it for research with the final aim of
developing software products for sale or license to a third party or
(3) use of the Software or any derivative of it for research with the
final aim of developing non-software products for sale or license to a
third party, or (4) use of the Software to provide any service to an
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interested in using the Software commercially, please contact Isis
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University, to negotiate a licence. Contact details are:
innovation@isis.ox.ac.uk quoting reference DE/1112. */
#include "newimage/newimageall.h"
#include "meloptions.h"
#include "meldata.h"
#include "melodic.h"
#include "utils/log.h"
#include <time.h>
#include "miscmaths/miscprob.h"
#include "melhlprfns.h"
using namespace Utilities;
using namespace NEWIMAGE;
// }}}
namespace Melodic{
// {{{ Setup
Matrix MelodicData::process_file(string fname, int numfiles)
{
volume4D<float> RawData;
//read data
message("Reading data file " << fname << " ... ");
read_volume4D(RawData,fname);
message(" done" << endl);
del_vols(RawData,opts.dummy.value());
Mean += meanvol(RawData)/numfiles;
//reshape
Matrix tmpData;
tmpData = RawData.matrix(Mask);
//estimate smoothness
if((Resels == 0)&&(!opts.filtermode))
Resels = est_resels(RawData,Mask);
//convert to percent BOLD signal change
if(opts.pbsc.value()){
message(" Converting data to percent BOLD signal change ...");
Matrix meanimg = convert_to_pbsc(tmpData);
meanR = meanimg.Row(1);
message(" done" << endl);
}
else{
if(opts.remove_meanvol.value())
{
message(string(" Removing mean image ..."));
meanR = mean(tmpData);
tmpData = remmean(tmpData);
message(" done" << endl);
}
else meanR=ones(1,tmpData.Ncols());
}
if(opts.remove_meantc.value()){
meanC = mean(tmpData,2);
tmpData = remmean(tmpData,2);
}
//convert to power spectra
if(opts.pspec.value()){
message(" Converting data to powerspectra ...");
tmpData = calc_FFT(tmpData);
message(" done" << endl);
}
//switch dimension in case temporal ICA is required
if(opts.temporal.value()){
message(string(" Switching dimensions for temporal ICA") << endl);
tmpData = tmpData.t();
Matrix tmp;
tmp = meanC;
meanC = meanR.t();
meanR = tmp.t();
message(" Data size : " << Data.Nrows() << " x " << Data.Ncols() <<endl);
}
//variance - normalisation
if(opts.varnorm.value()){
message(" Normalising by voxel-wise variance ...");
if(stdDev.Storage()==0)
stdDev = varnorm(tmpData,std::min(30,tmpData.Nrows()-1),
opts.vn_level.value())/numfiles;
else
stdDev += varnorm(tmpData,std::min(30,tmpData.Nrows()-1),
opts.vn_level.value())/numfiles;
stdDevi = pow(stdDev,-1);
message(" done" << endl);
}
return tmpData;
}
Matrix MelodicData::expand_mix()
{
Matrix out;
out = expand_dimred(mixMatrix);
return out;
}
Matrix MelodicData::expand_dimred(const Matrix& Mat)
{
int first, last;
first = 1;
last = DWM.at(0).Ncols();
Matrix tmp = DWM.at(0) * Mat.Rows(first,last);
for(unsigned int ctr = 1; ctr < DWM.size(); ctr++){
first = last + 1;
last += DWM.at(ctr).Ncols();
tmp &= DWM.at(ctr) * Mat.Rows(first, last);
}
return tmp;
}
Matrix MelodicData::reduce_dimred(const Matrix& Mat)
{
int first, last;
first = 1;
last = WM.at(0).Ncols();
Matrix tmp = WM.at(0) * Mat.Rows(first,last);
for(unsigned int ctr = 1; ctr < WM.size(); ctr++){
first = last + 1;
last += WM.at(ctr).Ncols();
tmp &= WM.at(ctr) * Mat.Rows(first, last);
}
return tmp;
}
void MelodicData::set_TSmode()
{
Matrix tmp, tmpT, tmpS, tmpT2, tmpS2, tmpT3;
tmp = expand_dimred(mixMatrix);
tmpT = zeros(tmp.Nrows()/numfiles, tmp.Ncols());
tmpS = zeros(numfiles, tmp.Ncols());
explained_var = krfact(tmp,tmpT,tmpS);
outMsize("tmp",tmp);
outMsize("tmpT",tmpT);
outMsize("tmpS",tmpS);
Tmodes.clear(); Smodes.clear();
for(int ctr = 1; ctr <= tmp.Ncols(); ctr++){
tmpT3 << reshape(tmp.Column(ctr),tmpT.Nrows(),numfiles);
outMsize("tmpT3", tmpT3);
tmpT2 << tmpT.Column(ctr);
tmpS2 << tmpS.Column(ctr);
tmpT3 << SP(tmpT3,pow(ones(tmpT3.Nrows(),1)*tmpS2.t(),-1));
if(numfiles>1)
tmpT2 |= tmpT3;
if(mean(tmpS2,1).AsScalar()<0){
tmpT2*=-1.0;
tmpS2*=-1.0;
}
add_Tmodes(tmpT2);
add_Smodes(tmpS2);
}
//add GLM OLS fit
if(Tdes.Storage()){
Matrix alltcs = Tmodes.at(0).Column(1);
for(int ctr=1; ctr < (int)Tmodes.size();ctr++)
alltcs|=Tmodes.at(ctr).Column(1);
if((alltcs.Nrows()==Tdes.Nrows())&&(Tdes.Nrows()>Tdes.Ncols()))
glmT.olsfit(alltcs,Tdes,Tcon);
}
if(Sdes.Storage()){
Matrix alltcs = Smodes.at(0);
for(int ctr=1; ctr < (int)Smodes.size();ctr++)
alltcs|=Smodes.at(ctr);
if((alltcs.Nrows()==Sdes.Nrows())&&(Sdes.Nrows()>Sdes.Ncols()&&alltcs.Nrows()>2))
glmS.olsfit(alltcs,Sdes,Scon);
}
}
void MelodicData::setup()
{
numfiles = (int)opts.inputfname.value().size();
setup_misc();
if(opts.filtermode){ // basic setup for filtering only
Data = process_file(opts.inputfname.value().at(0));
}
else{
if((numfiles > 1) && (opts.approach.value()==string("defl") || opts.approach.value()==string("symm")))
opts.approach.set_T("tica");
Matrix alldat, tmpData;
bool tmpvarnorm = opts.varnorm.value();
if(numfiles > 1 && opts.joined_vn.value()){
opts.varnorm.set_T(false);
}
alldat = process_file(opts.inputfname.value().at(0), numfiles) / numfiles;
if(opts.pca_dim.value() > alldat.Nrows()-2){
cerr << "ERROR:: too many components selected \n\n";
exit(2);
}
if(opts.debug.value())
save4D(alldat,string("preproc_dat") + num2str(1));
for(int ctr = 1; ctr < numfiles; ctr++){
tmpData = process_file(opts.inputfname.value().at(ctr), numfiles);
if(opts.debug.value())
save4D(tmpData /numfiles,string("preproc_dat") + num2str(ctr+1));
if(tmpData.Ncols() == alldat.Ncols() && tmpData.Nrows() == alldat.Nrows())
alldat += tmpData / numfiles;
else
message("Data dimensions do not match - ignoring "+opts.inputfname.value().at(ctr) << endl);
}
//update mask
if(opts.update_mask.value()){
message("Excluding voxels with constant value ...");
update_mask(Mask, alldat);
message(" done" << endl);
}
if((numfiles > 1 ) && opts.joined_vn.value() && tmpvarnorm){
//variance - normalisation
message(endl<<"Normalising jointly by voxel-wise variance ...");
stdDev = varnorm(alldat,alldat.Nrows(),3.1);
stdDevi = pow(stdDev,-1);
message(" done" << endl);
}
if(numfiles>1)
message(endl << "Initial data size : "<<alldat.Nrows()<<" x "<<alldat.Ncols()<<endl<<endl);
if(opts.debug.value())
save4D(alldat,"alldat");
//estimate model order
Matrix tmpPPCA;
RowVector AdjEV, PercEV;
Matrix Corr, tmpE;
int order;
order = ppca_dim(remmean(alldat,2), RXweight, tmpPPCA, AdjEV, PercEV, Corr, pcaE, pcaD, Resels, opts.pca_est.value());
if (opts.paradigmfname.value().length()>0)
order += param.Ncols();
if(opts.pca_dim.value() == 0){
opts.pca_dim.set_T(order);
PPCA=tmpPPCA;
}
order = opts.pca_dim.value();
if (opts.paradigmfname.value().length()>0){
Matrix tmpPscales;
tmpPscales = param.t() * alldat;
paramS = stdev(tmpPscales.t());
calc_white(pcaE, pcaD, order, param, paramS, whiteMatrix, dewhiteMatrix);
}else
calc_white(pcaE, pcaD, order, whiteMatrix, dewhiteMatrix);
if(opts.debug.value()){
outMsize("pcaE",pcaE); saveascii(pcaE,"pcaE");
outMsize("pcaD",pcaD); saveascii(pcaD,"pcaD");
outMsize("AdjEV",AdjEV); saveascii(AdjEV,"AdjEV");
outMsize("PercEV",PercEV); saveascii(PercEV,"PercEV");
outMsize("tmpPPCA",tmpPPCA); saveascii(tmpPPCA,"tmpPPCA");
outMsize("whiteMatrix",whiteMatrix); saveascii(whiteMatrix,"whiteMatrix");
outMsize("dewhiteMatrix",dewhiteMatrix); saveascii(dewhiteMatrix,"dewhiteMatrix");
cerr << "Order: " << order << endl;
}
EV = AdjEV;
EVP = PercEV;
if(numfiles == 1){
Data = alldat;
Matrix tmp = IdentityMatrix(Data.Nrows());
DWM.push_back(tmp);
WM.push_back(tmp);
}
else {
for(int ctr = 0; ctr < numfiles; ctr++){
tmpData = process_file(opts.inputfname.value().at(ctr), numfiles);
if(opts.joined_vn.value() && tmpvarnorm){
tmpData=SP(tmpData,pow(ones(tmpData.Nrows(),1)*stdDev,-1));
}
// whiten (separate / joint)
Matrix newWM,newDWM;
if(!opts.joined_whiten.value()){
message(" Individual whitening in a " << order << " dimensional subspace " << endl);
std_pca(tmpData, RXweight, Corr, pcaE, pcaD);
calc_white(pcaE, pcaD, order, newWM, newDWM);
}else{
if(!opts.dr_pca.value()){
std_pca(whiteMatrix*tmpData, RXweight, Corr, pcaE, pcaD);
calc_white(pcaE, pcaD, order, newWM, newDWM);
newDWM=(dewhiteMatrix*newDWM);
newWM=(newWM*whiteMatrix);
}
else{
if(opts.debug.value())
message(" --mod_pca ");
Matrix tmp1, tmp2;
tmp1 = whiteMatrix * alldat;
tmp1 = remmean(tmp1,2) * tmpData.t();
tmp2 = pinv(tmp1.t()).t();
std_pca(tmp1 * tmpData, RXweight, Corr, pcaE, pcaD);
calc_white(pcaE, pcaD, order, newWM, newDWM);
newDWM=(tmp2*newDWM);
newWM=(newWM * tmp1);
}
}
DWM.push_back(newDWM);
WM.push_back(newWM);
tmpData = newWM * tmpData;
//concatenate Data
if(Data.Storage() == 0)
Data = tmpData;
else
Data &= tmpData;
}
}
opts.varnorm.set_T(tmpvarnorm);
message(endl << " Data size : "<<Data.Nrows()<<" x "<<Data.Ncols()<<endl<<endl);
outMsize("stdDev",stdDev);
//meanC=mean(Data,2);
if(opts.debug.value())
save4D(Data,"concat_data");
//save the mean & mask
save_volume(Mask,logger.appendDir("mask"));
save_volume(Mean,logger.appendDir("mean"));
}
} // void setup()
void MelodicData::setup_misc()
{
//initialize Mean
read_volume(Mean,opts.inputfname.value().at(0));
//create mask
create_mask(Mask);
//setup background image
if(opts.bgimage.value()>""){
read_volume(background,opts.bgimage.value());
if(!samesize(Mean,background)){
cerr << "ERROR:: background image and data have different dimensions \n\n";
exit(2);
}
}else{
background = Mean;
}
if(!samesize(Mean,Mask)){
cerr << "ERROR:: mask and data have different dimensions \n\n";
exit(2);
}
//reset mean
Mean *= 0;
//set up weighting
if(opts.segment.value().length()>0){
create_RXweight();
}
//seed the random number generator
double tmptime = time(NULL);
if ( opts.seed.value() != -1 ) {
tmptime = opts.seed.value();
}
srand((unsigned int) tmptime);
if(opts.paradigmfname.value().length()>0){
message(" Use columns in " << opts.paradigmfname.value()
<< " for PCA initialisation" <<endl);
param = read_ascii_matrix(opts.paradigmfname.value());
Matrix tmpPU, tmpPV;
DiagonalMatrix tmpPD;
SVD(param, tmpPD, tmpPU, tmpPV);
param = tmpPU;
opts.pca_dim.set_T(std::max(opts.pca_dim.value(), param.Ncols()+3));
if(opts.debug.value()){
outMsize("Paradigm",param); saveascii(param,"param");
}
//opts.guessfname.set_T(opts.paradigmfname.value());
}
//read in post-proc design matrices etc
if(opts.fn_Tdesign.value().length()>0)
Tdes = read_ascii_matrix(opts.fn_Tdesign.value());
if(opts.fn_Sdesign.value().length()>0)
Sdes = read_ascii_matrix(opts.fn_Sdesign.value());
if(opts.fn_Tcon.value().length()>0)
Tcon = read_ascii_matrix(opts.fn_Tcon.value());
if(opts.fn_Scon.value().length()>0)
Scon = read_ascii_matrix(opts.fn_Scon.value());
if(opts.fn_TconF.value().length()>0)
TconF = read_ascii_matrix(opts.fn_TconF.value());
if(opts.fn_SconF.value().length()>0)
SconF = read_ascii_matrix(opts.fn_SconF.value());
if(numfiles>1 && Sdes.Storage() == 0){
Sdes = ones(numfiles,1);
if(Scon.Storage() == 0){
Scon = ones(1,1);
Scon &= -1*Scon;
}
}
Tdes = remmean(Tdes,1);
}
void MelodicData::save()
{
//check for temporal ICA
if(opts.temporal.value()){
message(string("temporal ICA: transform back the data ... "));
Matrix tmpIC = mixMatrix.t();
mixMatrix=IC.t();
IC=tmpIC;
unmixMatrix=pinv(mixMatrix);
Data = Data.t();
tmpIC = meanC;
meanC = meanR.t();
meanR = tmpIC.t();
// whiteMatrix = whiteMatrix.t;
// dewhiteMatrix = dewhiteMatrix.t();
message(string("done") << endl);
opts.temporal.set_T(false); // Do not switch again!
}
message(endl << "Writing results to : " << endl);
//Output IC
if((IC.Storage()>0)&&(opts.output_origIC.value())&&(after_mm==false))
save4D(IC,opts.outputfname.value() + "_oIC");
//Output IC -- adjusted for noise
if(IC.Storage()>0){
volume4D<float> tempVol;
//Matrix ICadjust;
if(after_mm){
save4D(IC,opts.outputfname.value() + "_IC");
// ICadjust = IC;
}
else{
Matrix resids = stdev(Data - mixMatrix * IC);
for(int ctr=1;ctr<=resids.Ncols();ctr++)
if(resids(1,ctr) < 0.05)
resids(1,ctr)=1;
// stdNoisei = pow(stdev(Data - mixMatrix * IC)*
// std::sqrt((float)(Data.Nrows()-1))/
// std::sqrt((float)(Data.Nrows()-IC.Nrows())),-1);
stdNoisei = pow(resids*
std::sqrt((float)(Data.Nrows()-1))/
std::sqrt((float)(Data.Nrows()-IC.Nrows())),-1);
ColumnVector diagvals;
diagvals=pow(diag(unmixMatrix*unmixMatrix.t()),-0.5);
save4D(SP(IC,diagvals*stdNoisei),opts.outputfname.value() + "_IC");
}
if(opts.output_origIC.value())
save4D(stdNoisei,string("Noise_stddev_inv"));
}
//Output T- & S-modes
save_Tmodes();
save_Smodes();
//Output mixMatrix
if(mixMatrix.Storage()>0){
saveascii(expand_mix(), opts.outputfname.value() + "_mix");
mixFFT=calc_FFT(expand_mix(), opts.logPower.value());
saveascii(mixFFT,opts.outputfname.value() + "_FTmix");
}
//Output PPCA
if(PPCA.Storage()>0)
saveascii(PPCA, opts.outputfname.value() + "_PPCA");
//Output ICstats
if(ICstats.Storage()>0)
saveascii(ICstats,opts.outputfname.value() + "_ICstats");
//Output unmixMatrix
if(opts.output_unmix.value() && unmixMatrix.Storage()>0)
saveascii(unmixMatrix,opts.outputfname.value() + "_unmix");
//Output Mask
message(" "<< logger.appendDir("mask") <<endl);
//Output mean
if(opts.output_mean.value() && meanC.Storage()>0 && meanR.Storage()>0){
saveascii(meanR,opts.outputfname.value() + "_meanR");
saveascii(meanC,opts.outputfname.value() + "_meanC");
}
//Output white
if(opts.output_white.value() && whiteMatrix.Storage()>0&&
dewhiteMatrix.Storage()>0){
saveascii(whiteMatrix,opts.outputfname.value() + "_white");
saveascii(dewhiteMatrix,opts.outputfname.value() + "_dewhite");
Matrix tmp;
tmp=calc_FFT(dewhiteMatrix, opts.logPower.value());
saveascii(tmp,opts.outputfname.value() + "_FTdewhite");
}
//Output PCA
if(opts.output_pca.value() && pcaD.Storage()>0&&pcaE.Storage()>0){
saveascii(pcaE,opts.outputfname.value() + "_pcaE");
saveascii((Matrix) diag(pcaD),opts.outputfname.value() + "_pcaD");
Matrix PCAmaps;
if(whiteMatrix.Ncols()==Data.Ncols())
PCAmaps = dewhiteMatrix.t();
else
PCAmaps = whiteMatrix * Data;
save4D(PCAmaps,opts.outputfname.value() + "_pca");
}
message("...done" << endl);
} //void save()
int MelodicData::remove_components()
{
message("Reading " << opts.filtermix.value() << endl)
mixMatrix = read_ascii_matrix(opts.filtermix.value());
if (mixMatrix.Storage()<=0) {
cerr <<" Please specify the mixing matrix correctly" << endl;
exit(2);
}
unmixMatrix = pinv(mixMatrix);
IC = unmixMatrix * Data;
string tmpstr;
tmpstr = opts.filter.value();
Matrix noiseMix;
Matrix noiseIC;
int ctr=0;
char *p;
char t[1024];
const char *discard = ", [];{(})abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ~!@#$%^&*_-=+|\':><./?";
message("Filtering the data...");
strcpy(t, tmpstr.c_str());
p=strtok(t,discard);
ctr = atoi(p);
if(ctr>0 && ctr<=mixMatrix.Ncols()){
message(" "<< ctr );
noiseMix = mixMatrix.Column(ctr);
noiseIC = IC.Row(ctr).t();
}
else{
cerr << endl<< "component number "<<ctr<<" does not exist" << endl;
}
do{
p=strtok(NULL,discard);
if(p){
ctr = atoi(p);
if(ctr>0 && ctr<=mixMatrix.Ncols()){
message(" "<<ctr);
noiseMix |= mixMatrix.Column(ctr);
noiseIC |= IC.Row(ctr).t();
}
else{
cerr << endl<< "component number "<<ctr<<" does not exist" << endl;
}
}
}while(p);
message(endl);
Matrix newData;
outMsize("DATA",Data);
outMsize("IC",IC);
outMsize("noiseIC",noiseIC);
outMsize("noiseMix",noiseMix);
outMsize("meanR",meanR);
outMsize("meanC",meanC);
newData = Data - noiseMix * noiseIC.t();
if(meanR.Storage()>0)
newData = newData + ones(newData.Nrows(),1)*meanR;
volume4D<float> tmp;
read_volume4D(tmp,opts.inputfname.value().at(0));
tmp.setmatrix(newData,Mask);
save_volume4D(tmp,logger.appendDir(opts.outputfname.value() + "_ICAfiltered"));
return 0;
} // int remove_components()
void MelodicData::create_RXweight()
{
message("Reading the weights for the covariance R_X from file "<< opts.segment.value() << endl);
volume4D<float> tmpRX;
read_volume4D(tmpRX,opts.segment.value());
RXweight = tmpRX.matrix(Mask);
}
void MelodicData::est_smoothness()
{
if(Resels == 0){
string SM_path = opts.binpath + "smoothest";
string Mask_fname = logger.appendDir("mask");
if(opts.segment.value().length()>0){
Mask_fname = opts.segment.value();
}
// Setup external call to smoothest:
char callSMOOTHESTstr[1000];
ostrstream osc(callSMOOTHESTstr,1000);
osc << SM_path << " -d " << data_dim()
<< " -r " << opts.inputfname.value().at(0) << " -m "
<< Mask_fname << " > " << logger.appendDir("smoothest") << '\0';
message(" Calling Smoothest: " << callSMOOTHESTstr << endl);
system(callSMOOTHESTstr);
//read back the results
ifstream in;
string str;
Resels = 1.0;
in.open(logger.appendDir("smoothest").c_str(), ios::in);
if(in>0){
for(int ctr=1; ctr<7; ctr++)
in >> str;
in.close();
if(str!="nan")
Resels = atof(str.c_str());
}
}
}
unsigned long MelodicData::standardise(volume<float>& mask, volume4D<float>& R)
{
unsigned long count = 0;
int M=R.tsize();
for (int z=mask.minz(); z<=mask.maxz(); z++) {
for (int y=mask.miny(); y<=mask.maxy(); y++) {
for (int x=mask.minx(); x<=mask.maxx(); x++) {
if( mask(x,y,z) > 0.5) {
count ++;
if( M > 2 ) {
// For each voxel
// calculate mean and standard deviation...
double Sx = 0.0, SSx = 0.0;
for ( int t = 0; t < M; t++ ) {
float R_it = R(x,y,z,t);
Sx += R_it;
SSx += (R_it)*(R_it);
}
float mean = Sx / M;
float sdsq = (SSx - ((Sx)*(Sx) / M)) / (M - 1) ;
if (sdsq<=0) {
// trap for differences between mask and invalid data
mask(x,y,z)=0;
count--;
} else {
// ... and use them to standardise to N(0, 1).
for ( unsigned short t = 0; t < M; t++ ) {
R(x,y,z,t) = (R(x,y,z,t) - mean) / sqrt(sdsq);
}
}
}
}
}
}
}
return count;
}
float MelodicData::est_resels(volume4D<float> R, volume<float> mask)
{
message(" Estimating data smoothness ... ");
unsigned long mask_volume = standardise(mask, R);
int dof = R.tsize();
unsigned long N = mask_volume;
// MJ additions to make it cope with 2D images
bool usez = true;
if (R.zsize() <= 1) { usez = false; }
enum {X = 0, Y, Z};
float SSminus[3] = {0, 0, 0}, S2[3] = {0, 0, 0};
int zstart=1;
if (!usez) zstart=0;
for ( unsigned short z = zstart; z < R.zsize() ; z++ )
for ( unsigned short y = 1; y < R.ysize() ; y++ )
for ( unsigned short x = 1; x < R.xsize() ; x++ )
// Sum over N
if( (mask(x, y, z)>0.5) &&
(mask(x-1, y, z)>0.5) &&
(mask(x, y-1, z)>0.5) &&
( (!usez) || (mask(x, y, z-1)>0.5) ) ) {
N++;
for ( unsigned short t = 0; t < R.tsize(); t++ ) {
// Sum over M
SSminus[X] += R(x, y, z, t) * R(x-1, y, z, t);
SSminus[Y] += R(x, y, z, t) * R(x, y-1, z, t);
if (usez) SSminus[Z] += R(x, y, z, t) * R(x, y, z-1, t);
S2[X] += 0.5 * (R(x, y, z, t)*R(x, y, z, t) +
R(x-1, y, z, t)*R(x-1, y, z, t));
S2[Y] += 0.5 * (R(x, y, z, t)*R(x, y, z, t) +
R(x, y-1, z, t)*R(x, y-1, z, t));
if (usez) S2[Z] += 0.5 * (R(x, y, z, t)*R(x, y, z, t) +
R(x, y, z-1, t)*R(x, y, z-1, t));
}
}
float norm = 1.0/(float) N;
float v = dof; // v - degrees of freedom (nu)
if(R.tsize() > 1) {
norm = (v - 2) / ((v - 1) * N * R.tsize());
}
// for extreme smoothness
if (SSminus[X]>=0.99999999*S2[X])
SSminus[X]=0.99999*S2[X];
if (SSminus[Y]>=0.99999999*S2[Y])
SSminus[Y]=0.99999*S2[Y];
if (usez)
if (SSminus[Z]>=0.99999999*S2[Z])
SSminus[Z]=0.99999*S2[Z];
// Convert to sigma squared
float sigmasq[3] = {0,0,0};
sigmasq[X] = -1.0 / (4 * log(fabs(SSminus[X]/S2[X])));
sigmasq[Y] = -1.0 / (4 * log(fabs(SSminus[Y]/S2[Y])));
if (usez) { sigmasq[Z] = -1.0 / (4 * log(fabs(SSminus[Z]/S2[Z]))); }
// Convert to full width half maximum
float FWHM[3] = {0,0,0};
FWHM[X] = sqrt(8 * log(2) * sigmasq[X]);
FWHM[Y] = sqrt(8 * log(2) * sigmasq[Y]);
if (usez) { FWHM[Z] = sqrt(8 * log(2) * sigmasq[Z]); }
float resels = FWHM[X] * FWHM[Y];
if (usez) resels *= FWHM[Z];
message(" done " <<endl);
return resels;
}
void MelodicData::create_mask(volume<float>& theMask)
{
if(opts.use_mask.value() && opts.maskfname.value().size()>0){ // mask provided
read_volume(theMask,opts.maskfname.value());
message("Mask provided : " << opts.maskfname.value()<<endl<<endl);
}
else{
if(opts.perf_bet.value() && opts.use_mask.value()){ //use BET
message("Create mask ... ");
//save first image
tmpnam(Mean_fname); // generate a tmp name
save_volume(Mean,Mean_fname);
// set up all strings
string BET_outputfname = string(Mean_fname)+"_brain";
string BET_path = opts.binpath + "bet";
string BET_optarg = "-m -f 0.4"; // see man bet
string Mask_fname = BET_outputfname+"_mask";
// Setup external call to BET:
// char callBETstr[1000];
// ostrstream betosc(callBETstr,1000);
// betosc << BET_path << " " << Mean_fname << " "
// << BET_outputfname << " " << BET_optarg << " > /dev/null " << '\0';
// message(" Calling BET: " << callBETstr << endl);
// system(callBETstr);
string tmpstr = BET_path + string(" ") +
Mean_fname + string(" ") + BET_outputfname + string(" ") +
BET_optarg + string(" > /dev/null ");
system(tmpstr.c_str());
// read back the Mask file
read_volume(theMask,Mask_fname);
// clean /tmp
char callRMstr[1000];
ostrstream osc(callRMstr,1000);
osc << "rm " << string(Mean_fname) <<"* " << '\0';
system(callRMstr);
message("done" << endl);
}
else{
if(opts.use_mask.value()){ //just threshold the Mean
message("Create mask ... ");
float Mmin, Mmax, Mtmp;
Mmin = Mean.min(); Mmax = Mean.max();
theMask = binarise(Mean,Mmin + opts.threshold.value()*
(Mmax-Mmin),Mmax);
Mtmp = Mmin + opts.threshold.value()* (Mmax-Mmin);
message("done" << endl);
}
else{ //well, don't threshold then
theMask = Mean;
theMask = 1.0;
}
}
}
if(opts.remove_endslices.value()){
// just in case mc introduced something nasty
message(" Deleting end slices" << endl);
for(int ctr1=theMask.miny(); ctr1<=theMask.maxy(); ctr1++){
for(int ctr2=theMask.minx(); ctr2<=theMask.maxx(); ctr2++){
theMask(ctr2,ctr1,Mask.minz()) = 0.0;
theMask(ctr2,ctr1,Mask.maxz()) = 0.0;
}
}
}
} //void create_mask()
void MelodicData::sort()
{
int numComp = mixMatrix.Ncols(), numVox = IC.Ncols(),
numTime = mixMatrix.Nrows(), i,j;
//flip IC maps to be positive (on max)
//flip Subject/Session modes to be positive (on average)
//flip time courses accordingly
for(int ctr_i = 1; ctr_i <= numComp; ctr_i++)
if(IC.Row(ctr_i).MaximumAbsoluteValue()>IC.Row(ctr_i).Maximum()){
flipres(ctr_i);
}
message("Sorting IC maps" << endl);
Matrix tmpscales, tmpICrow, tmpMIXcol;
if(numfiles > 1){
set_TSmode();
Matrix allmodes = Smodes.at(0);
for(int ctr = 1; ctr < (int)Smodes.size();++ctr)
allmodes |= Smodes.at(ctr);
tmpscales = median(allmodes).t();
} else {
// re-order wrt standard deviation of IC maps
tmpscales = stdev(IC,2);
}
ICstats = tmpscales;
double max_val, min_val = tmpscales.Minimum()-1;
for(int ctr_i = 1; ctr_i <= numComp; ctr_i++){
max_val = tmpscales.Maximum2(i,j);
ICstats(ctr_i,1)=max_val;
tmpICrow = IC.Row(ctr_i);
tmpMIXcol = mixMatrix.Column(ctr_i);
IC.SubMatrix(ctr_i,ctr_i,1,numVox) = IC.SubMatrix(i,i,1,numVox);
mixMatrix.SubMatrix(1,numTime,ctr_i,ctr_i) =
mixMatrix.SubMatrix(1,numTime,i,i);
IC.SubMatrix(i,i,1,numVox) = tmpICrow.SubMatrix(1,1,1,numVox);
mixMatrix.SubMatrix(1,numTime,i,i) = tmpMIXcol.SubMatrix(1,numTime,1,1);
tmpscales(i,1)=tmpscales(ctr_i,1);
tmpscales(ctr_i,1)=min_val;
}
ICstats /= ICstats.Column(1).Sum();
ICstats *= 100;
if(EVP.Storage()>0){
tmpscales = ICstats.Column(1).AsMatrix(ICstats.Nrows(),1) * EVP(1,numComp);
ICstats |= tmpscales;
}
if(Data.Storage()>0&&stdDev.Storage()>0){
Matrix copeP(tmpscales), copeN(tmpscales);
Matrix max_ICs(tmpscales), min_ICs(tmpscales);
for(int ctr_i = 1; ctr_i <= numComp; ctr_i++){
int i,j;
max_ICs(ctr_i,1) = IC.Row(ctr_i).Maximum2(i,j);
copeP(ctr_i,1) = std::abs((pinv(mixMatrix)*
Data.Column(j)).Row(ctr_i).AsScalar()*stdDev(1,j)*100*
(mixMatrix.Column(ctr_i).Maximum()-
mixMatrix.Column(ctr_i).Minimum())/meanR(1,j));
min_ICs(ctr_i,1) = IC.Row(ctr_i).Minimum2(i,j);
copeN(ctr_i,1) = -1.0*std::abs((pinv(mixMatrix)*
Data.Column(j)).Row(ctr_i).AsScalar()*stdDev(1,j)*100*
(mixMatrix.Column(ctr_i).Maximum()-
mixMatrix.Column(ctr_i).Minimum())/meanR(1,j));
}
ICstats |= copeP;
ICstats |= copeN;
}
mixFFT=calc_FFT(expand_mix(), opts.logPower.value());
unmixMatrix = pinv(mixMatrix);
}
void MelodicData::reregress()
{
if((numfiles > 1)){
}
}
void MelodicData::status(const string &txt)
{
cout << "MelodicData Object " << txt << endl;
if(Data.Storage()>0){cout << "Data: " << Data.Nrows() <<"x" << Data.Ncols() << endl;}else{cout << "Data empty " <<endl;}
if(pcaE.Storage()>0){cout << "pcaE: " << pcaE.Nrows() <<"x" << pcaE.Ncols() << endl;}else{cout << "pcaE empty " <<endl;}
if(pcaD.Storage()>0){cout << "pcaD: " << pcaD.Nrows() <<"x" << pcaD.Ncols() << endl;}else{cout << "pcaD empty " <<endl;}
if(whiteMatrix.Storage()>0){cout << "white: " << whiteMatrix.Nrows() <<"x" << whiteMatrix.Ncols() << endl;}else{cout << "white empty " <<endl;}
if(dewhiteMatrix.Storage()>0){cout << "dewhite: " << dewhiteMatrix.Nrows() <<"x" << dewhiteMatrix.Ncols() << endl;}else{cout << "dewhite empty " <<endl;}
if(mixMatrix.Storage()>0){cout << "mix: " << mixMatrix.Nrows() <<"x" << mixMatrix.Ncols() << endl;}else{cout << "mix empty " <<endl;}
if(unmixMatrix.Storage()>0){cout << "unmix: " << unmixMatrix.Nrows() <<"x" << unmixMatrix.Ncols() << endl;}else{cout << "unmix empty " <<endl;}
if(IC.Storage()>0){cout << "IC: " << IC.Nrows() <<"x" << IC.Ncols() << endl;}else{cout << "IC empty " <<endl;}
} //void status()
}