Concatenate multiple sessions in ttest2

Hello! I am a über-beginner, so please excuse my lack of proper MATLAB nomenclature!

I am trying to create a matrix using ttest2 — a 20x1 to be exact — to assess the significance between number of trials and neuronal firing rate differences before and after stimulus onset. I am having difficulty extracting the data out as such.

The script is written in a series of for loops, but for some reason, I can type 'E' (first variable of interest) in the command window (without running the whole script) and it returns all values (a 20x1 matrix), but when I type 'difftot,' it gives me the last value after it cycled through the for loop. I would like the 20 incidences of this second variable of interest for 'E' stimulus events, again a 20x1 matrix.

Why are these outputs so different? As is, the ttest2 is, of course, only giving me one value.

I ran the script to see if that would remedy the situation, and it returned a lot of variables in the workspace to be '[]' instead of their (row x column) dimensions. And I received the error message that concatenation cannot happen because, of course, the matrices are not the same size.

My script is below; thank you in advance for any help!!!

clear all; close all tic file='/Users/ellenm.walker/Desktop/Lab matters/Maier Lab/MULTISENSORY/'; fileID=fopen('/Users/ellenm.walker/Desktop/Lab matters/Maier Lab/MULTISENSORY/PlasticityInfo.txt'); info=textscan(fileID,'%s%s%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f%f'); fclose(fileID); count=0; all_firing=[]; for ff=1:length(info{1}) animal=info{1}{ff}; this_path=strcat('/Users/ellenm.walker/Desktop/Lab matters/Maier Lab/MULTISENSORY/',animal,'/'); session=strcat(info{2}{ff},'/'); rcds=info{5}(ff); probetype=info{8}(ff); nchan=info{9}(ff); fs=info{10}(ff); sodr=info{16}(ff); wodr=info{17}(ff); isi_threshold=2; plt=0; tst=[0 11 13 6 2]; % tst=pump fluids % 0=water; 11=MV; 13=2H; 6=saccharin; 2=sodium chloride [spike_ts_merge,waves_merge,mua_ts,mua_waves]=Merge(this_path,session,probetype,nchan,fs,isi_threshold,plt); [evt_ts,evt_id]=GetEventsComplete(this_path,session,tst,[],1,0);

    pre=1:1000;
    post=1001:3000;
    total=1:3000;
    pre_scalar=1000;
    post_scalar=3000;
    nunit=0;
    format shortG;
    for ii=1:length(spike_ts_merge)
        if  ~isempty(spike_ts_merge{ii})
            units=unique(spike_ts_merge{ii}(:,2));
            nunit=nunit+1;
            for jj=1:length(units)
                idx=find(spike_ts_merge{ii}(:,2)==units(jj));
                spkbin=GetSpkbin(spike_ts_merge{ii}(idx,1),evt_ts,pre_scalar,post_scalar);
                %get odor trial info
                A=find((evt_id(:,1)==0) & ((evt_id(:,2)==wodr)));
                B=find((evt_id(:,1)==0) & ((evt_id(:,2)==sodr)));
                C=find((evt_id(A:2)==sodr));
                D=find((evt_id(:,2)==wodr));
                E=find((evt_id(:,1)==0) & ((evt_id(:,2)>0)));
                odras=evt_id(E,2);
                E(find(odras==sodr),2)=1;
                E(find(odras==wodr),2)=0;
                E(find(odras==sodr),3)=13;
                E(find(odras==wodr),3)=11;
                %plot responses
%                 figure;
%                 %plot neutral odor trials
                M=mean(spkbin(A,:));
                N=mean(spkbin(B,:));
                sm=smooth(M,500,'lowess');
                sm2=smooth(N,500,'lowess');
%                 plot(sm,'g');
%                 hold on
%                 plot(sm2,'r');
%                 legend('MV','2H');
%                 title({animal;session});

                count=count+1;
                prewtr=mean(M(pre));
                postwtr=mean(M(post));
                presac=mean(N(pre));
                postsac=mean(N(post));
                diffwtr=postwtr-prewtr;
                diffsac=postsac-presac;
                difftot=(postwtr+postsac)-(prewtr+presac);

                %alldata(count,1)=animal;
                alldata(count,2)=mean(rcds);
                alldata(count,3)=mean(prewtr);
                alldata(count,4)=mean(postwtr);
                alldata(count,5)=mean(diffwtr);
                alldata(count,6)=mean(presac);
                alldata(count,7)=mean(postsac);
                alldata(count,8)=mean(diffsac);

                all_firing(count,:)=[mean([M;N])];

                %h=ttest2=(for all 20 trials, df in firing rate (post-pre)) -- look if df firing rates are different from 0
%                 h=ttest2(E,diffwtr);
%                 h=ttest2(E,diffsac);
                h=ttest2(E,difftot)      
            end
        end
    end
end
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