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Copy pathperform_onesample_t_TopoCorr.m
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perform_onesample_t_TopoCorr.m
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function [T]=perform_onesample_t_TopoCorr(S, behavior, f_ranges, chan_hood, cond_cell, time_roi,max_dist, sigbool)
if ~isfield(S, 'weighted') || isempty(S.weighted)
weighted=1;
else
weighted=S.weighted;
end
if ~isfield(S,'timebin')
S.timebin=[];
end
if isempty(S.timebin)
timebin=0;
else
timebin=S.timebin;
end
if ~isfield(S,'chs')
S.chs=[];
chs=[];
else
chs=S.chs;
end
if ~isfield(S, 'alpha')
alpha=0.05;
else
alpha=S.alpha;
end
if ~isfield(S, 'n_perm')
n_perm=2000;
else
n_perm=S.n_perm;
end
D = spm_eeg_load(S.D);
if isempty(chs) % all chs are included
conn_idid=D.conn_idid;
loc2d=conn_idid(:,[2, 1]);
label=chanlabels(D,1:length(loc2d))';
else
A=ismember(D.conn_idid(:,1),chs);
B=ismember(D.conn_idid(:,2),chs);
subconn_chs=find(A & B);
subconn_idid=D.conn_idid(subconn_chs,:);
[~,locb]=ismember(subconn_idid,chs);
loc2d=locb(:,[2, 1]);
label=chanlabels(D,subconn_chs)';
S.subconn_chs=subconn_chs;
end
conn=1;
if isempty(chan_hood)
mychan_hood=0;
else
mychan_hood=1;
end
S.Din=D;
if size(cond_cell,1)>1
choice='concatenation';
end
ep_initime=D.timeonset*1000;
roi=time_roi;
abs_roi=[D.time(1) D.time(end)]*1000;
if roi(1)<abs_roi(1)
roi(1)=abs_roi(1) ;
end
if roi(2)>abs_roi(2)
roi(2)=abs_roi(2) ;
end
samp_interval=1000/D.fsample;
slice_roi=round((roi-ep_initime)/samp_interval)+1;
% slice_times=ep_initime+samp_interval*((slice_roi(1)-1):(slice_roi(2)-1));
if conn==1
if isempty(chs)
channels=meegchannels(D,{'EEG','MEG','LFP'});
else
channels=subconn_chs;
end
else
channels=meegchannels(D,{'EEG','MEG','LFP'});
end
structD=struct(D);
Freqs=frequencies(D);
%for h=1:length(bandbool)
h=1;
idF=find(Freqs>=f_ranges{h}(1) & Freqs<=f_ranges{h}(2));
Ddata=zeros(nchannels(D), 1, nsamples(D), ntrials(D));
for c=1:D.ntrials
x=spm_squeeze(D(:,idF,:,c), 4);
x=mean(x,2);
%D(:,1,:,c)= x;
Ddata(:,1,:,c)= x;
end
if length(cond_cell)>1
switch choice
case 'first'
idx1=indtrial(D,cond_cell{1});
tmpdata1=Ddata(channels,1,slice_roi(1):slice_roi(2),idx1);
tmpdata1=permute(tmpdata1,[2,1,3,4]);
tmpdata1=shiftdim(tmpdata1);
case 'concatenation'
idx1=indtrial(D,cond_cell{1});
%repl_1=getRepl(structD, idx1);
if weighted
repl_1=getRepl(structD, idx1);
else
repl_1=ones(1,length(idx1));
end
repl_1_4d(1,1,:)=repl_1;
tmp=Ddata(channels,1,slice_roi(1):slice_roi(2),idx1);
tmp=permute(tmp,[2,1,3,4]);
tmp=shiftdim(tmp);
tmpdata1=tmp.*repmat(repl_1_4d,[length(channels),length(slice_roi(1):slice_roi(2)),1]);
c1=repl_1;
for k=2:size(cond_cell,1)*size(cond_cell,2)
idxh1=indtrial(D,cond_cell{k});
%repl_1=getRepl(structD, idxh1);
if weighted
repl_1=getRepl(structD, idxh1);
else
repl_1=ones(1,length(idxh1));
end
repl_1_4d(1,1,:)=repl_1;
tmp=Ddata(channels,1,slice_roi(1):slice_roi(2),idxh1);
tmp=permute(tmp,[2,1,3,4]);
tmp=shiftdim(tmp);
tmpdata1=tmpdata1+tmp.*repmat(repl_1_4d,[length(channels),length(slice_roi(1):slice_roi(2)),1]);
c1=c1+repl_1;
end
c1_4d(1,1,:)=c1;
tmpdata1=tmpdata1./repmat(c1_4d,[length(channels),length(slice_roi(1):slice_roi(2)),1]);
case 'no test'
return
end
elseif length(cond_cell)==1
idx1=indtrial(D,cond_cell{1});
tmpdata1=Ddata(channels,1,slice_roi(1):slice_roi(2),idx1);
tmpdata1=permute(tmpdata1,[2,1,3,4]);
tmpdata1=shiftdim(tmpdata1);
else
return
end
if timebin==0
avgdata1=tmpdata1;
else
bno=abs(round(timebin));
avgdata1=zeros(size(tmpdata1,1),bno,size(tmpdata1,3));
%avgdata2=zeros(size(tmpdata2,1),bno,size(tmpdata2,3));
b_sep=linspace(1,size(tmpdata1,2),bno+1);
b_sep_ceil=ceil(b_sep);
b_sep_floor=floor(b_sep);
b_sep_ids=[b_sep_ceil(1:bno);b_sep_floor(2:(bno+1))]';
for ib=1:bno
avgdata1(:,ib,:)=mean(tmpdata1(:,b_sep_ids(ib,1):b_sep_ids(ib,2),:),2);
%avgdata2(:,ib,:)=mean(tmpdata2(:,b_sep_ids(ib,1):b_sep_ids(ib,2),:),2);
end
end
if sum(sigbool(1,1:5))>0
corr_spec=zeros(size(avgdata1,1),size(avgdata1,2));
p_spec=ones(size(avgdata1,1),size(avgdata1,2));
for j=1:size(avgdata1,1)
tmpdata=squeeze(avgdata1(j,:,:));
tmpdata=permute(tmpdata,[2,1]);
[rho,p] = corr(behavior,tmpdata);
corr_spec(j,:)=rho;
p_spec(j,:)=p;
end
test1=isnan(corr_spec);
i_nan = find(test1);
corr_spec(i_nan)=0;
test2=isnan(p_spec);
i_nan = find(test2);
p_spec(i_nan)=1;
T(1,h).t=corr_spec;
if sigbool(1)==1
s=p_spec<0.05;
end
if sigbool(2)==1
s=p_spec<0.01;
end
if sigbool(3)==1
s=p_spec<0.001;
end
if sigbool(4)==1
[p_fdr,P_masked]=fdr(p_spec,0.05);
s=P_masked;
end
if sigbool(5)==1
[p_fdr,P_masked]=fdr(p_spec,0.01);
s=P_masked;
end
T(1,h).s=s;
end
if sigbool(6)==1
% chan_hood=spatial_neighbors_spm(chanlocs,max_dist);
% [pval, t_orig, clust_info, seed_state, est_alpha]=clust_perm1(avgdata1,chan_hood,2000,0.05,0);
rshp_data1=permute(avgdata1,[3,1,2]);
rshp_data1=reshape(rshp_data1, size(rshp_data1,1),[]);
behaviorEx=repmat(behavior,[1,size(rshp_data1,2)]);
[pval, corr_obs, crit_corr, est_alpha, seed_state]=mult_comp_perm_corr(rshp_data1,behaviorEx,5000,0);
corr_obs=reshape(corr_obs,size(avgdata1,1),[]);
pval=reshape(pval,size(avgdata1,1),[]);
T(1,h).t=corr_obs;
T(1,h).pval=pval;
end
if sigbool(7)==1
% chan_hood=spatial_neighbors_spm(chanlocs,max_dist);
% [pval, t_orig, clust_info, seed_state, est_alpha]=clust_perm1(avgdata1,chan_hood,2000,0.05,1);
rshp_data1=permute(avgdata1,[3,1,2]);
rshp_data1=reshape(rshp_data1, size(rshp_data1,1),[]);
behaviorEx=repmat(behavior,[1,size(rshp_data1,2)]);
[pval, corr_obs, crit_corr, est_alpha, seed_state]=mult_comp_perm_corr(rshp_data1,behaviorEx,2000,1);
corr_obs=reshape(corr_obs,size(avgdata1,1),[]);
pval=reshape(pval,size(avgdata1,1),[]);
T(1,h).t=corr_obs;
T(1,h).pval=pval;
end
if sum(sigbool(1,8:10))>0
if ~mychan_hood
chan_hood=spatial_neighbors_spm(chanlocs,max_dist);
if conn==2 && ~isempty(chs)
chan_hood=chan_hood(chs,chs);
end
else
if conn==1 && ~isempty(chs)
chan_hood=chan_hood(subconn_chs,subconn_chs);
end
end
end
if sigbool(8)==1
%chan_hood=spatial_neighbors_spm(chanlocs,max_dist);
behaviorEx=repmat(shiftdim(behavior,-2),[size(avgdata1,1), size(avgdata1,2)]);
[pval,t_orig, corr_obs, est_alpha, seed_state]=clust_perm_corr(avgdata1,behaviorEx,chan_hood,n_perm,0,alpha);
%[pval,t_orig, corr_obs, est_alpha, seed_state, clust_info]=clust_perm_corr(dataX,dataY,chan_hood,n_perm,tail,alpha_level,stat,reports,seed_state)
test1=isnan(corr_obs);
i_nan = find(test1);
corr_obs(i_nan)=0;
T(1,h).t=corr_obs;
T(1,h).pval=pval;
end
if sigbool(9)==1
%chan_hood=spatial_neighbors_spm(chanlocs,max_dist);
behaviorEx=repmat(shiftdim(behavior,-2),[size(avgdata1,1), size(avgdata1,2)]);
[pval,t_orig, corr_obs, est_alpha, seed_state]=clust_perm_corr(avgdata1,behaviorEx,chan_hood,n_perm,1,alpha);
test1=isnan(corr_obs);
i_nan = find(test1);
corr_obs(i_nan)=0;
T(1,h).t=corr_obs;
T(1,h).pval=pval;
end
if sigbool(10)==1
%chan_hood=spatial_neighbors_spm(chanlocs,max_dist);
behaviorEx=repmat(shiftdim(behavior,-2),[size(avgdata1,1), size(avgdata1,2)]);
[pval,t_orig, corr_obs, est_alpha, seed_state]=clust_perm_corr(avgdata1,behaviorEx,chan_hood,n_perm,-1,alpha);
test1=isnan(corr_obs);
i_nan = find(test1);
corr_obs(i_nan)=0;
T(1,h).t=corr_obs;
T(1,h).pval=pval;
end
function repl=getRepl(structD, idx)
repl=zeros(1,length(idx));
for k=1:length(repl)
repl(k)=structD.trials(1,idx(k)).repl;
end