NeuroImaging,Training,Program,UCLA,
July,13,,2016,
Agatha,Lenartowicz,,Ph.D.,
,
,
Functional,Connectivity,(PPI,&,PLS),
“Connectivity”,
• Being,joined,together,
• Ability,to,communicate,(transfer,of,information),
Member,Units,+,Paths,
Network,
“Connectivity”,
• Being,joined,together,
• Ability,to,communicate,(transfer,of,information),
Connectivity,in,Neuroscience,
1881,International,Medical,Congress:,Segregation,(Ferrier),vs,
Integration,(Goltz),
W1000,
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1000,
2000,
3000,
4000,
5000,
6000,
7000,
1855, 1875, 1895, 1915, 1935, 1955, 1975, 1995, 2015,YEAR%
Cumulative%Manuscripts%Published%(brain%OR%neural%OR%cortical)%AND%(connectivity)%
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1000,
2000,
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7000,
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Cumulative%Manuscripts%Published%(brain%OR%neural%OR%cortical)%AND%(connectivity)%
0,1000,2000,3000,4000,5000,6000,7000,8000,
YEAR%
Connectivity,in,Neuroscience,
1881,International,Medical,Congress:,Segregation,(Ferrier),vs,
Integration,(Goltz),
Ogawa,1990,BOLD,Filler,,LeBihan,1991/2,DTI,
McIntosh,,Horwitz,,Friston,1990,Biswal,1997,RSN,
W1000,
0,
1000,
2000,
3000,
4000,
5000,
6000,
7000,
1855, 1875, 1895, 1915, 1935, 1955, 1975, 1995, 2015,YEAR%
Cumulative%Manuscripts%Published%(brain%OR%neural%OR%cortical)%AND%(connectivity)%
Connectivity,in,Neuroscience,
Petrides M., (2005) Phil. Trans. R. Soc. B;360:781-795
Felleman & Van Essen, (1991), Cereb Cortex;1(1);1-47
Transition,from,architectonic,analysis,and,neurophysiological,recordings,in,the,animal,model,to,inWvivo,human,(and,nonWhuman),experiments.,
Fox et al., (2005) Proc. Natl. Acad. Sci. USA. 102; 967-9678
Image Courtesy of Jesse Brown, Ph.D.
Categories,of,Connectivity,
Thanks to SPM group for slide images.
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Physical,Connections,(tracing,*DTI/DWI,*dissections)*
Statistical,Connections,(correlation,*coherence,*mutual*information)*Sporns*2007*(Scholaropedia,*2*(10):4695)*
Categories,of,Connectivity,
Thanks to SPM group for slide images.
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Physical,Connections,(tracing,*DTI/DWI,*dissections)*
Statistical,Connections,(correlation,*coherence,*mutual*information)*Sporns*2007*(Scholaropedia,*2*(10):4695)*
No,information,about,direction,of,information,flow,(causal,relationships).,
Bird’s,Eye,View,of,Methods,Sp
atial,S
cale,of,N
etwork,
Degree,of,Causal,Inference,
ICA PCA
Graph Analysis
Bivariate Correlation
Connectome Virtual Brain
Granger Causality, Bayes Nets, SEM, DCM
Lesions, TMS
PPI
Seed-Analysis
Partial Correlation
Partial Least Squares MVPA
Model,Free,
Model,Based,
DTI
Bird’s,Eye,View,of,Methods,Sp
atial,S
cale,of,N
etwork,
Degree,of,Causal,Inference,
ICA PCA
Graph Analysis
Bivariate Correlation
Connectome Virtual Brain
Granger Causality, Bayes Nets, SEM, DCM
Lesions, TMS
PPI
Seed-Analysis
Partial Correlation
Partial Least Squares MVPA
Model,Free,
Model,Based,
DTI
“effective,connectivity”,
Bird’s,Eye,View,of,Methods,Sp
atial,S
cale,of,N
etwork,
Degree,of,Causal,Inference,
ICA PCA
Graph Analysis
Bivariate Correlation
Connectome Virtual Brain
Granger Causality, Bayes Nets, SEM, DCM
Lesions, TMS
PPI
Seed-Analysis
Partial Correlation
Partial Least Squares MVPA
Model,Free,
Model,Based,
DTI
Bird’s,Eye,View,of,Methods,Sp
atial,S
cale,of,N
etwork,
Degree,of,Causal,Inference,
ICA PCA
Graph Analysis
Bivariate Correlation
Connectome Virtual Brain
Granger Causality, Bayes Nets, SEM, DCM
Lesions, TMS
PPI
Seed-Analysis
Partial Correlation
Partial Least Squares MVPA
Model,Free,
Model,Based,
DTI
Psychophysiological,Interaction,
Slide*images*courtesy*of*UCL*group,Friston, K.J., et al., (1997), NeuroImage, 6: 218-229
attention%
• PPI,is,an,interaction,term,between,a,
task,regressor,and,a,time,series,from,
an,ROI,(β3,=,β1,*,β2),
• test,of,slopes,
Y%=%β1%%%%%+%%β2%%%%%%%%+%β3%%%%%%+%error%%%%
Attention,No,Attention,
β3,=,β1,*,β3,,
Attention,
No,Attention,
β1,
β2,V1,
Corr(V1,PPI),=,.05,
Corr(PSY,PPI),=,.97,
Corr(V1,PSY),=,.03,
,
,,,,,NonWFactorial,
,
V1,
PSY:,Att,vs,NoAtt,
PPI:,Interaction,
Corr(V1,PPI),=,.02,
Corr(PSY,PPI),=,.01,
Corr(V1,PSY),=,.02,
,
,,,,,,,,,,,,Factorial,
,
Speed,1, Speed,2,
A, A,NA, NA,
A, A,NA, NA,
• Collinearity,in,this,model,will,decrease,power,of,detecting,β3,
• Factorial,designs,are,preferable,because,they,ensure,variance,
in,PPI,independent,of,main,effects,– 2,crossed,independent,variables,in,design,(e.g.,,attention,,speed),
– identify,seed,using,one,of,the,factors,(e.g.,,speed***),
– replace,factor,(speed),with,activity,of,seed,
,
No,Attention,
β3,=,β1,*,β3,,
Attention,
β1,
β2,V1,
• Must,include,main,effect,regressors,(β1,and,β2),to,test,unique,
variance,due,to,interaction,term,
– If,don’t,include,main,effects,=>,spuriously,significant,interaction,term,
– If,do,include,main,effects,=>,may,have,poor,power,(collinearity),to,detect,interaction,
,
No,Attention,
β3,=,β1,*,β3,,
Attention,
β1,
β2,V1,
• PPI,“concept”,can,be,used,to,test,for,differences,in,connectivity,
comparing,preFpost%intervention,and,subject%groups,
• ‘psychological’,variable,is,now,a,between,subject,variable;,compare,ROI,connectivity,between,sessions,or,groups,
No,Attention,
β3,=,β1,*,β3,,
Attention,
β1,
β2,V2,
Heinz, A., et al., (2005), Nat Neuro, 8(1): 20—21
EXAMPLE: AMY-PFC interactions vary as a function of depression
correlated genotype
• PSY,=,genotype,(group),,ROI,=,AMY,,no,PPI,
• Response,of,AMY,to,task,(aversive/pleasant),also,greater,in,ls/ss,genotypes,• If,compare,y=AMY+e,across,groups,,will,observe,differences,primarily,due,to,
task,response,of,AMY,to,aversive,stimuli,• If,compare,y=AMY+task+e,across,groups,,will,obtain,estimate,of,taskW
independent,difference,in,AMY,connectivity,between,groups,
PSY, l/l,s/s,
PPI in Event-Related Designs • Low,power,
– Smaller,signal,and,poorer,model,fit,than,in,block,designs,– Layering,on,top,of,this,variability,is,the,structured,variability,that,we,test,
with,PPI,– Convolution,with,HRF,impacts,interaction,term,(hrf(A)*hrf(B)),vs,hrf(A*B),– Recommended,with,caution,in,fast,designs;,alternate,approach,beta,
series,correlation,(Rissman),
PPI in Event-Related Designs • Low,power,
– Smaller,signal,and,poorer,model,fit,than,in,block,designs,– Layering,on,top,of,this,variability,is,the,structured,variability,that,we,test,
with,PPI,– Convolution,with,HRF,impacts,interaction,term,(hrf(A)*hrf(B)),vs,hrf(A*B),– Recommended,with,caution,in,fast,designs;,alternate,approach,beta,
series,correlation,(Rissman),
Gitelman et al., (2003), NeuroImage, 19: 200-207 hrf(A)*hrf(B) ≠ hrf(A*B)
ROIA, ROIB,
Generalized PPI Model • 1997,subtraction,model,is,limited,
– Limited,to,2,conditions,– Dependent,on,accurate,centering,of,PSY,and,PPI,regressors,– Doesn’t,make,interpretation,easy,
Y,=,c,+,β1,(X1WX2),+,β2,,ROI,+,β3,(X1WX2)*ROI,
No,Attention,
Attention,
If,you,were,to,mean,center,and,there,were,more,time,points,in,Attention,condition,than,in,No,Attention,condition,,the,zero,point,would,be,biased,upwards.,ZeroWcenter,(minWmax),more,appropriate.,
No,Attention,
Attention,
+X1,0,X1,
+X1,
0,X2,WX2,
WX2,
Generalized PPI Model • Generalized,form,of,the,model,
– Initially,developed,by,Jeanette,Mumford,~2010,(not,published),– Independently,formalized,by,McLaren,et,al.,(2012),NeuroImage,61:,1277W1286,
Y,=,c,+,β1a,X1,+,β1bX2,+,β2,,ROI,+,β3a,(X1)*ROI,+,β3b,(X1)*ROI,,To,test,for,slope,difference:,β3a,W,β3b,To,test,for,within,condition,ROI,correlations:,β3a,,β3b,
+X1,0,X1,
+X1,
0,X2,WX2,
WX2,
PhysWPhys,Interactions,
• Can,use,PPI,to,evaluate,physioWphysiological,interactions,(Friston,
et,al.,,1997)%
Friston, K.J., et al., (1997), NeuroImage, 6: 218-229
PhysWPhys,Interactions,
• β1,=,L,Motor,,β2,=,R,Motor,,β3,,=,L,Motor,*,R,Motor,
• Does,connectivity,within,one,region,vary,with,activation,level,of,
another,region,in,resting,state,data,
Di & Biswal, (2013), PLOS ONE, 8(8): e71163
β1% β2%
β1%
β2%
PPI,Summary,• PPI,is,accessible,and,powerful,as,a,tool,to,study,connectivity,across,context,
(condition,,intervention,,group,,activity,of,another,region),
– Requires,attention,to,model,,inclusion,of,‘task’,regressors,,regressor,collinearity,and,
centering,(for,differenceWbased,regressors),,,
– Typically,limited,to,single,region,(except,for,physioWphysio,interactions),and,so,,not,as,
powerful,as,other,multivariate,techniques,such,as,MVPA,or,PLS,
• Direction,of,modulation,is,ambiguous,
attention%
attention%
Images*courtesy*of*UCL*group,
More on PPI?
Partial,Least,Squares,
• PLS,seeks,to,identify,a,profile,of,voxels,(and,timepoints),that,
covary,as,a,function,of,task,,group,etc.,
• ModelWfree,(unlike,PPI)*,
• Multivariate,(unlike,PPI),
• Singular,Value,Decomposition,of,CrossWBlock,Variance,Matrix,
,
McIntosh,et.,al,,1996,,2004ab,(adapted,to,PET/fMRI/EEG),
SVD,on,CrossWBlock,Variance,voxels, task,conditions,
voxe
ls,
task,con
ditio
ns,
Cross,Block,Variance,PLS,
Within,Block,Variance,Voxels,PCA,
Within,Block,Variance,Task,PCA,(Factor,Analysis),
voxels, task,conditions,e.g.,,attend,,nonWattend,
SVD,on,CrossWBlock,Variance,X=USVT,
=,
voxels,(v),
cond
ition
,(c), k,
c,Data(X), U,
k,
k, k,
v,
V,
• Least*squares*decomposition*of*data*matrix*into*orthogonal*basis*
function*k,latent,variables,produced,(LVs),• LV,=,saliences,+,singular*value*• Conceptually,similar,to,PCA,,ICA,etc.,maybe,even,FFT,because,we,
reNexpress*matrix*as*a*series*of*orthogonal*vectors*that*can*be*
recombined*to*recreate*the*original*matrix,
S,
%,cov,
SVD,on,CrossWBlock,Variance,X=USVT,
=,
voxels,(v),
cond
ition
,(c), k,
c, U,
k,
k, k,
v,
V,S,
%,cov,
Data(X),
Examples,
Spreng*et*al.,*2011,*Neuroimage*53(1):*303N317,
Examples,
Spreng*et*al.,*2011,*Neuroimage*53(1):*303N317,
Examples,
Lenartowicz*et*al.,*2011,*CABN,*10(2):*298N315*,
Note:*256*electrodes***500*time*points*=*128k*variables,
Flexibility,of,Technique,voxels,(v),
cond
ition
,(c),
Data(X),
Flexibility,of,Technique,voxels,(v),
cond
ition
,(c),
Data(X),
Data(Y),
cond
ition
,(c),
GRO
UP1
,GRO
UP2
,
Flexibility,of,Technique,voxels,(v),
cond
ition
,(c),
Data(X),
Data(Y),
cond
ition
,(c),
GRO
UP1
,GRO
UP2
,
Data(X),
cond
ition
,(c), voxels,(v),*,time,(event,related,design),
Examples,
Spreng*et*al.,*2011,*Neuroimage*53(1):*303N317,
Flexibility,of,Technique,voxels,(v),
cond
ition
,(c),
Data(X),
Data(Y),
cond
ition
,(c),
GRO
UP1
,GRO
UP2
,
Data(X),
cond
ition
,(c), voxels,(v),*,time,(event,related,design),
cond
ition
,(c),
r(perf,,seed,,EEG,,symptoms,…),
voxels,(v),
Flexibility,of,Technique,voxels,(v),
cond
ition
,(c),
Data(X),
Data(Y),
cond
ition
,(c),
GRO
UP1
,GRO
UP2
,
Data(X),
cond
ition
,(c), voxels,(v),*,time,(event,related,design),
cond
ition
,(c),
r(perf,,seed,,EEG,,symptoms,…),
voxels,(v),
Data(X),
cond
ition
,(c),
r(perf,,seed,,EEG,,symptoms,…),
voxels,(v),
cond
ition
,(c),
Model,Free*,
• We,can,steer,analysis,to,various,contrasts,by,demeaning,cross,block,matrix,selectively,
Group1, Group2,
10, 5,W5, W10,
7.5,
W7.5,
Model,Free*,
Group1, Group2,
10, 5,W5, W10,
2.5,
W2.5,
• We,can,steer,analysis,to,various,contrasts,by,demeaning,cross,block,matrix,selectively,
Statistics,
• Resampling,methods,are,used,in,Matlab,batch/GUI,PLS,implementation,(distribution,free),
,• Permutation,(resampling,without,replacement/shuffling,assignment,between,e.g.,,conditions,and,subjects),approach,for,testing,overall,significance,of,singular,value,(i.e.,,%,covariance,accounted,for),–,depends,on,exchangeability,
,• Bootstrap,(resampling,with,replacement/varying,which,subjects,are,in,sample),approach,to,calculate,confidence,intervals,,on,the,saliences,(weights),
PLS,Summary,• Similar,idea,to,PPI,in,seeking,task,related,patterns,of,functional,
connectivity,however:,
– Multivariate,
– Model,Free,
– Flexible,to,incorporate,time,,covariates,,seeds,,beta,maps,etc.,
– Nonparametric,statistical,assessment,
• Originally*designed*for*between*subject*analyses*whereas*PPI*is*designed*for*
within*subject*connectivity;*natural*extensions*for*withinNsubject*designs*do*
exist*
• May,not,produce,the,contrast,you,*want*,(modelWfree),,but,often,that,
should,be,informative,in,itself,
For,more,on,PLS,
https://www.rotmanWbaycrest.on.ca/index.php?section=84,
PLS,Summary,• Similar,idea,to,PPI,in,seeking,task,related,patterns,of,functional,
connectivity,however:,
– Multivariate,
– Model,Free,
– Flexible,to,incorporate,time,,covariates,,seeds,,beta,maps,etc.,
– Nonparametric,statistical,assessment,
• Originally*designed*for*between*subject*analyses*whereas*PPI*is*designed*for*
within*subject*connectivity;*natural*extensions*for*withinNsubject*designs*do*
exist*
• May,not,produce,the,contrast,you,*want*,(modelWfree),,but,often,that,
should,be,informative,in,itself,
Within,vs,Between,Ss,Variance,
• FC,has/can,assessed,using,withinW,&,betweenWsubject,variance,,
– Within:,across,time,(e.g.,,continuous,–,RSN,or,trials,–,PPI),
– Between:,across,individuals,on,mean,withinWsubject,values,(e.g.,,PLS),
– Not,unique,to,FC,analyses,,true,in,general,when,multiple,measures,
obtained,(e.g.,,EEGWfMRI,,BOLDWRT,etc.),
Within,vs,Between,Ss,Variance,
• Does,between,subject,variance,recapitulate,within,subject,
variance?,Not,always.,
– No,e.g.,,positive,correlation,between,DMN,and,visual,cortex,across,people,
greater,for,visual,flashes,than,for,,passive,,could,be,because,some,people,
have,overall,greater,activation,(in,all,regions);,may,in,fact,have,negative,
correlation,within,individuals,
– Yes,if,measuring,the,same,processes,and,the,effects,are,consistent,over,
time,and,across,individuals,(ergodicity),e.g.,*Molenaar*&*Campbell*2009*
Curr*Directions*in*Psych*Science.18(2),*112N117*
Within,vs,Between,Ss,Variance,
• Example,in,FC,–,Roberts,et,al.,(2016),Neuroimage,135,,1W15,
• Moderate,prediction,of,(“ws”),withinWsubject,FC,from,betweenWsubject,(“as”),FC,(DMN),
Within,vs,Between,Ss,Variance,• Better,prediction,for,regions,within,network,than,between,network,(DAN,,DMN),
• BetweenWsubject,FC,has,more,negative,relationships,between,networks,
• BetweenWsubject,FC,is,more,variable,
Within,vs,Between,Ss,Variance,• Simpson’s,Paradox,
Within,vs,Between,Ss,Variance,
• FC,analysis,requires,analysis,of,withinWsubject,variance,
• This,is,not,unlike,hierarchical,GLM,approach,(study,effect,within,individual),
• Holds,true,for,correlations,with,external,variables,like,RT,,or,EEG,
Which,method?,MVPA,,PPI,,PLS,grouped,as,taskWrelated,connectivity,methods,
ML,
PPI,
PLS,
Y(label),=,b1X1,+,b2X2,+,b3X3,….,*
Multivariate*(however*voxels*independent*variables)*and*model*based.*Objective*is*to*predict*class*label,*not*
to*understand*the*correlations*(connectivity*patterns)*and*weights*may*or*may*not*be*related*to*interpretable*
brain*phenomena.*General*class*prediction*tool*(decoder),*not*a*functional*connectivity*method.*User*defined*
interpretation*based*on*choice*of*features*(EEG,*fMRI,*PET,*connectivity,*activity,*etc.)*
Y=,b1X1,+,b2X2,+,b3ROI,+,b4X1*ROI,…,,Mass*univariate*(GLM),*but*bound*by*ROI*correlation*across*conditions.*We*are*testing*FC*hypotheses.*Model*
based.*Conceptually*a*precursor*to*causality.*Subject*to*standard*regression*assumptions.*Excellent*for*
factorial*designs*where*precise,*largeNpower,*wellNdefined*network*hypotheses*exist.*
a1Y1,+,a2Y2,+,a3Y3,…,=,b1X1,,+b2x2,+,b3x3,,Multivariate*(voxels*dependent*variables),*aiming*to*discover*combination*of*activity*patterns*that*covary*
across*context*(e.g.,*condition*X1*levels).*Not*model*based.*Precursor*to*causality*analyses.*Relatively*
assumption*free.**Excellent*for*high*dimensional*data,*with*or*without*priors*on*network*architecture,*and*
flexible*across*multiple*modalities.*
Questions?,
Lab,• DOWNLOAD,TO,YOUR,OWN,DESKTOP,(as,well,as,to,the,virtual,box),
in,order,to,open,the,instruction,guides,(docx,files),,as,the,VB,does,not,have,a,Word,reader,
• Lab,will,be,demo,format,and,I,will,move,quickly,in,order,to,focus,on,higher,level,concepts,
,• If,you,get,stuck,,stop,,follow,along,with,a,colleague,or,presenter,,• If,you,are,unfamiliar,with,shell,,Matlab,,and,possibly,FSL,,follow,
along,with,a,colleague,or,presenter,i.e.,*don’t*waste*time*trouble*
shooting*trivial*problems*during*the*lab*to*get*conceptual*
understanding*of*lab*objective*
*
• Each*lab*has*a*readme*with*complete*instructions*for*performing*all*
steps*demonstrated*
,
Lab,,• OBJECTIVE1:,Using,1,subject’s,data,from,Friston,97,paper,(attend/
nonattend,,motion/nomotion),we,will,replicate,the,steps,needed,to,set,up,a,PPI,analysis,of,the,V1,connectivity,as,it,varies,with,attention,condition,–,using,FSL,(ppi_lab/ppi_fsl_lab_2012.docx),
,• OBJECTIVE2:,Appreciate,how,to,set,up,this,analysis,using,subtraction,
versus,generalized,PPI,model.,(ppi_lab/pls_fsl_lab_2014ADDENDUM.docx),
,• OBJECTIVE3:,Appreciate,how,to,set,up,this,analysis,using,PLS,(not,to,
validate,analysis,but,to,experience,the,PLS,code/GUI),(pls_lab/pls_lab_2015.docx).,
,