%% % N=28 Parkinson's patients and N=28 matched controls % PD patients came in for 2 sessions 1 week apart: ON or OFF meds (counterbalanced). % EEG files are labeled with session #, see ONOFF.mat for which session was ON or OFF. % Note that controls DID NOT have 2 sessions. clear all; clc homedir='Y:\EEG_Data\PDDys\PD 4 PREDICT\'; datalocation=[homedir,'\PROCESSED EEG DATA\']; % Data are here savepath = [datalocation,'CLEAN\']; cd(datalocation); load([homedir,'VV_Behavior.mat']); % Aggregate behavior files output from Matlab Psychtoolbox load([homedir,'ONOFF.mat']); % 3 columns: subject, session, [ON=1 OFF=0] load([homedir,'BV_Chanlocs_60.mat']); %************************************* MEASURES = xlsread([homedir,'MEASURES']); % Subj symptom measures taken in ON session %************************ % COLUMN LABELS % MEASURES(:,1) = PD IDx % MEASURES(:,2) = NAART Scores % MEASURES(:,3) = BDI Ratings % MEASURES(:,4) = MMSE Scores % MEASURES(:,5) = UPDRS Ratings % MEASURES(:,6) = Years Since Diagnosis (Rank Ordered) % MEASURES(:,7) = Levadopa Equivalent Dose (LED) % MEASURES(:,8) = Accelerometer hand placement: 1 = Left Hand // 2 = Right hand %************************ % Subject Numbers PDsx=[801:811,813:829]; CTLsx=[8010,8070,8060,890:914]; %% MAKE ERPs for subj=[PDsx,CTLsx] for session=1:2; if (subj>850 && session==1) || subj<850 % If not CTL, do session 2 (CTL did not have a session 2) load([num2str(subj),'_Session_',num2str(session),'_PDDys_VV_withcueinfo.mat'],'EEG','bad_chans','bad_epochs','bad_ICAs'); for ai=1:size(EEG.epoch,2) VECTOR(ai,1)=EEG.epoch(ai).FB; VECTOR(ai,2)=EEG.epoch(ai).Resp; VECTOR(ai,3)=EEG.epoch(ai).Resptime; VECTOR(ai,4)=EEG.epoch(ai).Stim; VECTOR(ai,5)=EEG.epoch(ai).Stimtime; VECTOR(ai,6)=EEG.epoch(ai).Cie; VECTOR(ai,7)=EEG.epoch(ai).Cuetime; VECTOR(ai,8)=EEG.epoch(ai).RT; VECTOR(ai,9)=EEG.epoch(ai).BEHCondi; VECTOR(ai,10)=EEG.epoch(ai).BEHOptimal; VECTOR(ai,11)=EEG.epoch(ai).BEHRT; VECTOR(ai,12)=EEG.epoch(ai).BEHFB; end % Remove practice trials VECTOR(isnan(VECTOR(:,9)),:)=NaN; % Add this for later: FB-parsed by condi for vvi=1:length(VECTOR), if VECTOR(vvi,1)==0, VECTOR(vvi,13)=VECTOR(vvi,9); elseif VECTOR(vvi,1)==1, VECTOR(vvi,13)=4+VECTOR(vvi,9); end end % Remove the bad ICAs identified by APPLE: bad_ICAs_To_Remove=bad_ICAs{2}; EEG = pop_subcomp( EEG, bad_ICAs_To_Remove, 0); % low-pass filter for display dims=size(EEG.data); EEG.data=eegfilt(EEG.data,500,[],20); EEG.data=reshape(EEG.data,dims(1),dims(2),dims(3)); % Set times tx=-6000:2:1998; b1=find(tx==-200); b2=find(tx==0); t1=find(tx==-500); t2=find(tx==1000); % For ERPs r1=find(tx==250); r2=find(tx==450); % For Topos tx2disp=-500:2:1000; % Accelerometer was worn on the non-dominant hand % Aggregate accelerometer data EEG.X=EEG.X-repmat(mean(EEG.X),4000,1); EEG.Y=EEG.Y-repmat(mean(EEG.Y),4000,1); EEG.Z=EEG.Z-repmat(mean(EEG.Z),4000,1); % Add to EEG.data as 61st channel - but not the rejected trials EEG.data(61,:,:)=(EEG.X(:,bad_epochs{1}~=1).^2)+(EEG.Y(:,bad_epochs{1}~=1).^2)+(EEG.Z(:,bad_epochs{1}~=1).^2); dims=size(EEG.data); % Basecor your ERPs here so they are pretty. BASE1=squeeze( mean(EEG.data(:,b1:b2,:),2) ); for chani=1:dims(1)-1 % don't basecor the tremor data DATA(chani,:,:)=squeeze(EEG.data(chani,:,:))-repmat( BASE1(chani,:),dims(2),1 ); end % Parse by condition %************************ % CONDITIONS % 1 = CHOOSE EASY LOSE % 2 = CHOOSE HARD LOSE % 3 = MATCH EASY LOSE % 4 = MATCH HARD LOSE % 5 = CHOOSE EASY WIN % 6 = CHOOSE HARD WIN % 7 = MATCH EASY WIN % 8 = MATCH HARD WIN %************************ for ai=1:8 % all FB ERP(:,ai,:)=mean(DATA(:,t1:t2,VECTOR(:,13)==ai),3); % DATA(ELECTRODE, TIME , CONDITION) TOPO(:,ai) = squeeze(mean(mean(DATA(:,r1:r2,VECTOR(:,13)==ai),2),3)); end % Save and move on to next save([savepath,num2str(subj),'_Session_',num2str(session),'_PDDys_VV_withcueinfo_ALL_THE_GOODS.mat'],'ERP','TOPO','VECTOR'); clc; disp(['AND PARTICPANT ',num2str(subj),' HAS BEEN SAVED']); clearvars -except datalocation ONOFF VV_Behavior BV_Chanlocs_60 PDsx CTLsx session subj savepath; close all; end end end %% COMBINE ERPS site = [21,36];% Cz FCz tx=-500:2:1000; time1 = 250; time2 = 450; t1=find(tx==time1); t2=find(tx==time2); % FOR REW-P TIME = time2-time1; tx2disp=-500:2:1000; COLS={'r','b','g','k'}; BigN=size(ONOFF,1)./2; row=1; for mi=1:size(ONOFF,1) disp([num2str(ONOFF(mi,1)),'_Session_',num2str(ONOFF(mi,2)),'_PDDys_VV_withcueinfo_ALL_THE_GOODS.mat']); load([savepath,num2str(ONOFF(mi,1)),'_Session_',num2str(ONOFF(mi,2)),'_PDDys_VV_withcueinfo_ALL_THE_GOODS.mat']); if ONOFF(mi,3)==1 % ON ON.ID(floor(row))=ONOFF(mi,1); ON.Session(floor(row))=ONOFF(mi,2); ON.VECTOR=VECTOR; ON.ERPs(floor(row),:,:)=squeeze(mean(ERP(site,:,:),1)); ON.Topos(floor(row),:,:)=TOPO(:,:); elseif ONOFF(mi,3)==0 % OFF OFF.ID(floor(row))=ONOFF(mi,1); OFF.Session(floor(row))=ONOFF(mi,2); OFF.VECTOR=VECTOR; OFF.ERPs(floor(row),:,:)=squeeze(mean(ERP(site,:,:),1)); OFF.Topos(floor(row),:,:)=TOPO(:,:); end row=row+.5; clear ERPs VECTOR; end row=1; for CTLi=[8010,8060,8070,890:914]; disp([num2str(CTLi),'_Session_1_PDDys_VV_withcueinfo_ALL_THE_GOODS.mat']); load([savepath,num2str(CTLi),'_Session_1_PDDys_VV_withcueinfo_ALL_THE_GOODS.mat']); CTL.ID(floor(row))=CTLi; CTL.Session(floor(row))=1; CTL.VECTOR=VECTOR; CTL.ERPs(floor(row),:,:)=squeeze(mean(ERP(site,:,:),1)); CTL.Topos(floor(row),:,:)=TOPO(:,:); row=row+1; clear ERPs VECTOR; end CtlN=row-1; %************************ % TOPOS %************************ TOPO_CON = (CTL.Topos(:,:,5)+CTL.Topos(:,:,6)+CTL.Topos(:,:,7)+CTL.Topos(:,:,8) )/4; TOPO_ON = (ON.Topos(:,:,5)+ON.Topos(:,:,6)+ON.Topos(:,:,7)+ON.Topos(:,:,8) )/4; TOPO_OFF = (OFF.Topos(:,:,5)+OFF.Topos(:,:,6)+OFF.Topos(:,:,7)+OFF.Topos(:,:,8) )/4; figure; hold on % CONTROL TOPO subplot(1,3,1) topoplot(mean(TOPO_CON,1),BV_Chanlocs_60); title('CONTROL'); set(gca,'clim',[-3 3]); % ON TOPO subplot(1,3,2) topoplot(mean(TOPO_ON,1),BV_Chanlocs_60); title('ON'); set(gca,'clim',[-3 3]); % OFF TOPO subplot(1,3,3) topoplot(mean(TOPO_OFF,1),BV_Chanlocs_60); title('OFF'); set(gca,'clim',[-3 3]); cbar %************************ % ERP %************************ win_CON = (CTL.ERPs(:,5,:)+CTL.ERPs(:,6,:)+CTL.ERPs(:,7,:)+CTL.ERPs(:,8,:))/4; win_ON = (ON.ERPs(:,5,:)+ON.ERPs(:,6,:)+ON.ERPs(:,7,:)+ON.ERPs(:,8,:))/4; win_OFF = (OFF.ERPs(:,5,:)+OFF.ERPs(:,6,:)+OFF.ERPs(:,7,:)+OFF.ERPs(:,8,:))/4; lose_CON = (CTL.ERPs(:,:,1)+CTL.ERPs(:,:,2)+CTL.ERPs(:,:,3)+CTL.ERPs(:,:,4))/4; lose_ON = (ON.ERPs(:,:,1)+ON.ERPs(:,:,2)+ON.ERPs(:,:,3)+ON.ERPs(:,:,4))/4; lose_OFF = (OFF.ERPs(:,:,1)+OFF.ERPs(:,:,2)+OFF.ERPs(:,:,3)+OFF.ERPs(:,:,4))/4; figure;hold on; rectangle('Position',[time1,0,TIME,3],'Curvature',0.1,'FaceColor',[.9 .9 .9])% ON WIN plot(tx2disp,squeeze(nanmean(win_CON,1)),COLS{1}); plot(tx2disp,squeeze(nanmean(win_ON,1)),COLS{2}); plot(tx2disp,squeeze(nanmean(win_OFF,1)),COLS{3}); shadedErrorBar(tx2disp, squeeze(nanmean(win_CON,1)), nanstd(squeeze(win_CON)) ./sqrt(28),COLS{1}) shadedErrorBar(tx2disp, squeeze(nanmean(win_ON,1)), nanstd(squeeze(win_ON)) ./sqrt(28),COLS{2}) shadedErrorBar(tx2disp, squeeze(nanmean(win_OFF,1)), nanstd(squeeze(win_OFF)) ./sqrt(28),COLS{3}) plot(tx2disp,squeeze(nanmean(win_CON,1)),COLS{1},'LineWidth',4); plot(tx2disp,squeeze(nanmean(win_ON,1)),COLS{2},'LineWidth',4); plot(tx2disp,squeeze(nanmean(win_OFF,1)),COLS{3},'LineWidth',4); title('ERPs FOR WINS'); h_legend=legend({'HC','ON','OFF'}); set(h_legend,'FontSize',12); plot([0 0],[-6 6],'k:'); set(gca,'ylim',[-1 4],'xlim',[-100 1000]) pcrit=.05; [H,P,CI,STATS]=ttest(win_CON,win_ON); P(P>pcrit)=NaN; P(P<=pcrit)=1; plot(tx2disp,-.5*squeeze(P),'k','linewidth',3); clear H P CI STATS; [H,P,CI,STATS]=ttest(win_CON,win_OFF); P(P>pcrit)=NaN; P(P<=pcrit)=1; plot(tx2disp,-.7*squeeze(P),'r','linewidth',3); clear H P CI STATS; CONTROL_ERP = squeeze(mean(win_CON(:,:,t1:t2),3)); ON_ERP = squeeze(mean(win_ON(:,:,t1:t2),3)); OFF_ERP = squeeze(mean(win_OFF(:,:,t1:t2),3)); [H,P,CI,STATS]=ttest(CONTROL_ERP,ON_ERP) text(.7,3.5,['CONTROL v. ON t= ',num2str(STATS.tstat),' p= ',num2str(P)]) [H,P,CI,STATS]=ttest(CONTROL_ERP,OFF_ERP) text(.7,3.3,['CONTROL v. OFF t= ',num2str(STATS.tstat),' p= ',num2str(P)]) [H,P,CI,STATS]=ttest(ON_ERP,OFF_ERP) text(.7,3.1,['ON v. OFF t= ',num2str(STATS.tstat),' p= ',num2str(P)]) %************************ % FOR ANALYSIS %************************ % Run these in SPSS to determine that there's no interaction between group % and volition / difficulty for condi=1:8 SPSS_CONT(:,condi)= squeeze(nanmean(CTL.ERPs(:,condi,t1:t2),3)); SPSS_ON(:,condi)= squeeze(nanmean(ON.ERPs(:,condi,t1:t2),3)); SPSS_OFF(:,condi)= squeeze(nanmean(OFF.ERPs(:,condi,t1:t2),3)); end % So then combine all rewards across volition and difficulty conditions REWP_ON = [SPSS_ON(:,5),SPSS_ON(:,6),SPSS_ON(:,7),SPSS_ON(:,8)]; REWP_OFF = [SPSS_OFF(:,5),SPSS_OFF(:,6),SPSS_OFF(:,7),SPSS_OFF(:,8)]; ALL_REWP_ON = mean(REWP_ON,2); ALL_REWP_OFF = mean(REWP_OFF,2); %************************ % CORRELATIONS %************************ YrsDx=tiedrank(MEASURES(:,6),1); figure; hold on; subplot(2,1,1) scatter(YrsDx,ALL_REWP_ON,'MarkerEdgeColor',[0 .5 .5],... 'MarkerFaceColor',[0 .7 .7],... 'LineWidth',1.5) lsline title('ON: REW-P v. YRS DIAGNOSED ') set(gca,'ylim',[-2 5]) [RHO,PVAL] = corr(ALL_REWP_ON,MEASURES(:,6),'TYPE','Spearman'); text(.7,4,['r= ',num2str(RHO),' p= ',num2str(PVAL)]) subplot(2,1,2) hold on; scatter(YrsDx,ALL_REWP_OFF,'MarkerEdgeColor',[0 .5 .5],... 'MarkerFaceColor',[0 .7 .7],... 'LineWidth',1.5) lsline title('OFF: REW-P v. YRS DIAGNOSED ') set(gca,'ylim',[-2 5]) [RHO,PVAL] = corr(ALL_REWP_OFF,MEASURES(:,6),'TYPE','Spearman'); text(.7,4,['r= ',num2str(RHO),' p= ',num2str(PVAL)]) %%