Multivariate analyses in clinical populations: General factors & neuroimaging - PowerPoint Presentation

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Multivariate analyses in clinical populations: General factors & neuroimaging

Joseph Callicott, MD

fMRI/MRI Summer Course 6/20/14

Slide2

IntroductionThe ‘Age of Big Data’

Lohr

, “GOOD with numbers? Fascinated by data? The sound you hear is opportunity

knocking…” (NY Times, 2/22/2012)We routinely collect ‘multimodal data’ E.g., mood rating scale and structural MRI Compile or compare, but typically without multimodal analysisProjects classified as ‘geno-,’ ‘proteo-,’ or ‘pheno-’ already connotes ‘big data:’Each fMRI image presents ~20K analysesGWAS model = strict correction for multiple comparisonsCurrent model = parallel correlation/association per datasetProposed model = multivariate approach w/data reductionSimplified analysesSmaller statistical ‘cost’Some current theoretical approaches become testable’ (RDoC)

Slide3

OutlineA Tale of Two Lectures:

Imaging genetics & schizophrenia

Relevant issues for

clinical populationsWhy imaging genetics?Imaging genetics 101Multivariate analyses of fMRI: within experimental datasetGeneral vs specific factors in datag“i” : factor analytic solution of general factors in fMRI task data Multivariate analyses of fMRI redux: across experimental dataset

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Issues of special interest to clinical studies…BOLD fMRI in clinical populations:

BOLD fMRI is not, strictly speaking, a clinically informative measure

No pathognomic findings, to date

Performance likely to differ fundamentally in tasks that HVs will perform near ceilingI.e., ~100% accuracy and faster RT than patientsBOLD fMRI in healthy subjects‘predictive relationships’ between behavior and BOLD implicit in design, perhaps not strongly correlated with group map activationGenetic associations to BOLD (the bulk of the imaging genetics literature) do not necessarily connote a ‘real’ effect of a given polymorphismIs the phenotype heritable? In past, twin or sibling studiesCurrently, within ‘only’ HV = GCTA (Visscher)

(Callicott et. al. 2000;

Manoach

et al., 2000; Callicott et al. 2003; others)

(Van Snellenberg et al. 2006)

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So, then, why

imaging genetics?

Crass commercial message:

Simple plan, high impactHave an fMRI task in a relatively large sample?Healthy controls preferableN > 40Draw blood or swab cheek Genotyping at most resolutions fast & cheap*In SPM: ANCOVA or regression suffice and seem reasonably poweredCOMT led from primate to human imaging, and then to drugs targeting cognitive impairments

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Seriously, though, why imaging genetics

?

Few routes to neural mechanism using

in vivo human dataAnimal model Drug studyBOLD fMRI (MRSI, MEG, EEG)Genes do not code for mental illness, per seGenes code for heritable aspects of brain function, intermediate- or endo-phenotypesGenetic risk for illnesses like schizophrenia is polygenic, heterogeneousGene interact with each other and the environment BOLD fMRI, as an alternate metric of specific or general cognitive systems, offers ‘real world validation’ Putative genetic mutations (including private mutations (CNVs)In spite of growing sample sizes, association studies risk false positivesRDoC domains and constructsIf these do not correspond to brain systems we can map, then may be as doomed as DSM

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Take home message…Larger samples needed (GWAS), typically via collaboration across centers (ENIGMA)

BIG DATA is here

Multivariate or non-hypothesis-driven analyses offer the potential for novel, highly informative findings

CNV & cognitionVery good software often freely available:PLINK AFNI GingerALE-SLEUTH-MANGO R (many)

(Stefansson et al., Nature, 2014)

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(*

Visscher

et al., 2010; Nan et al. 2012**;

Postuma et al. 2002***; McGue & Bouchard, 1998#;^Burmeister et al., 2008)

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Interest in imaging genetics predicated on heritability of phenotypes…

Callicott

et al

. Cereb Cortex 2000Patients > Controls (N=13) (N=18)Callicott et al. Am J Psychiatry 2003Healthy Siblings > Controls (N=48) (N=33)

PFC BOLD during our Nback h2 = 0.4-0.5

Blokland

e

t

al.

Biol

Psych

2009,

J

Neurosci

2011;

Koten

et al.

Science

2009

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Finding genes for highly heritable, but complex diseases

affected person

unaffected

“nonpenetrant”(Goldman et al. Nat Rev Genet 2006)Remains difficult, even when n=100KCaused by many (100-1000s) of genesThe effects of a mutation vary between peopleHas all the genes (note this doesn’t mean the exact same set)

May still carry some genes (like a parent of a sick person)

Has all of the genes but is NOT sick for reasons we can’t explain

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Catechol O-methyltransferase (COMT): NIMH Intramural Success Story

(Apud et al. 2006)

(Egan et al. 2001)

(Meyer-Lindenberg et al. 2006)

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Functional impact COMT Val105/158Met

val

/met

rs4680

5’

Now validated at multiple levels:

Animal models:

Reduced enzymatic activity

Altered synaptic dopamine levels

Human data:

Reduced enzymatic activity in vitro

lymphoblastoid

cell lines

Altered transcription/reduced activity post mortem

Altered D1 but not D2 receptor density in PFC

PFC efficiency in BOLD fMRI

Combined:

S

ex effects mostly in males ((

Papaleo

et al., 2014)

striatum

mammalian

PFC

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genotype effect

F=5.41, df= 2, 449;

p<.005.

Executive cognitionEffect of rs4680 on frontal lobe function (Egan et al

PNAS

2001)

n = 218

n = 181

n = 58

vv>vm>mm

, SPM 99, p<.005

Physiological efficiency

Circa 2014: How have these findings held up?

Replicated but n’s ~20

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BOLD phenotypes in simple association: COMT and PFC14

(

Mier

, Kirsch, Meyer-Lindenberg; 2009)

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BOLD phenotypes in simple association: 5-HTTP and Amygdala15

(

Munafo

, Brown, and Hariri; 2007))

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BOLD phenotypes in simple association: Power?16

(Barnett et al, 2008)

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1st generation imaging genetics: simple association

Candidate genes

KIBRA impaired memory & expressed in hippocampus (

Papassotiropoulos et al., Science 2006)Replication in 3 independent populations in behavioral memory measuresIn 30 healthy subjects, KIBRA associated with reduced hippo activation

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Genetic mutations modeled in cell culture or animalsAssociation based on disease GWAS (ZNF804A)Esslinger et al. 2009 (Science)

Rasetti et al. (Arch Gen Psychiatry)

2

nd generation imaging genetics: GWAS era

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PFC neuronal function: ‘optimized’ by dopamine & GABA interactions

(Goldman-Rakic & Selemon 1997)

(Seamans et al., 2001)

2nd generation imaging genetics: Epistasis and pathways

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(Straub et al., 2007)

2

nd

generation imaging genetics: Epistasis

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COMT

:

V/V

V/MM/MBray Hap:-/-

-/-

-/-

+/-

+/-

+/-

V/V

V/M

M/M

V/V

V/M

M/M

+/+

+/+

+/+

BOLD

fMRI

Left DLPFC (

a.u

.)

COMT x Dysbindin interaction

Epistasis (gene-gene interaction)

Initially based on candidate-by-candidate

Buckholtz et al.,

Mol

Psychiatry 2007

Data-driven (machine learning)

Nicodemus et al., Hum Genet 2010

Now predicated on detailed cellular or animal modeling

COMT x DTNBP1 (

Papaleo

et al., 2013)

DISC1 x NKCC1 (Kim et al., Cell 2012 & Callicott et al. J

Clin

Invest 2013)

2

nd

generation

imaging genetics: Translational neuroscience

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22

More of the same (‘sophisticated

univariate

’)? Network/connectivity? Hypothesis-free?Hypothesis-free pattern detection (random forest)ICA/PCA/CPCA networksNext generation imaging genetics?Novel phenotypes (processing speed)

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Sophisticated univariate: Imaging GWAS

BOLD fMRI GWAS

Nback (n = 364)

Illumina 650K chip genotypingAutomated extraction of AAL ROIsFirst GWAS + using BOLD fMRI (Callicott, Spencer, et al., in prep)

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As a heritable trait, BOLD fMRI phenotypes show other sensitivities…..

Long history within animal literature showing significant effects of environment on brain structure &

function

Beneficial effects of ‘enrichment’ (toys, limit isolation) (Hebb, Am J Psychiatry, 1955) fMRI during social stress task influenced by environmentUrban upbringing or urbanicity linked to increased risk for mental illness (Van Os et al., Nature, 2010)(Lederbogen et al., Nature, 2012)Sophisticated univariate

:

N

ovel questions

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As a heritable trait, BOLD fMRI phenotypes show other sensitivities…..fMRI during WM ( 3 cohorts (USA1 = 124; USA2 =92; Italy1=226 )

Sensitivity to childhood environment (Urbanicity) (

Ihne

et al., in submission)Sophisticated univariate: Imaging G x E

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fMRI during WM ( 3 cohorts (USA1 = 124; USA2 =112; Italy1=226 )Gene-by-environment interaction (COMT x Urbanicity) (Ihne et al., in submission)

Ihne et al., in preparation

Sophisticated

univariate: Imaging G x E

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Constrained principle component analysis (CPCA) (David AA Baranger

– Wash U)

27http://www.nitrc.org/projects/fmricpcaTodd Woodward and colleagues, University of British Columbia:CPCA provides a “unified framework [for]… regression analysis and principal component analysis .” To identify functional systems using from singular-value decomposition of BOLD time series, These systems are imaged by constraining analyzed BOLD signal from a particular interval of time against all other scans (i.e., all others are baseline) Multivariate network analysis: ICA/PCA/CPCA banish ‘blob-ology’

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CPCA28

Z or ‘activation’ matrix = individual time series for all subjects (rows) for all voxels in the brain (columns)

Our standard SPM5 via XNAT first level processing of 0B alternating with 2B

G or ‘design’ matrix = a model to predict BOLD signal changes (columns) over all fMRI scans (rows)SPM5 often uses a canonical hemodynamic response function (HRF) to deconvolve signal, fMRI-CPCA uses finite impulse response function (FIR)http://www.nitrc.org/projects/fmricpca

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CPCA29

N-back model not complicated:

Simply provide onset and offset of 0B and 2B task epochs

Components = extracted components represent networks Component loadings= loosely, correlation coefficients between component scores and BOLD signal that was predicted from imposed constraints (design)http://www.nitrc.org/projects/fmricpca

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30

Identify and then display components

using MRICon for anatomical localization

(http://www.nitrc.org/projects/mricron)In this case, not really using estimated hemodynamicsRather, we wish to compare effect of diagnosis or genotype using component scores and predictor weightsPredictor weight = contribution of G matrix to changes in components over the fMRI time series (~ correlation of component score and g) CPCA

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CPCA: Confusing Problematic Conflicting Agonizing

Unspecified error required recalculation of component weights

Same networks found with addition of a fourth DMN

Differentiates NC and SIBs from SCZNo longer appears to be identifying intermediate phenotype

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CPCA: Nback systems

Anti-task Network

resembles

cingulate from DMN+ hippocampus

WM Network

DLPFC

+parietal

Motor system

Anti-task #2

resembles parietal

From DMN

+ cerebellum

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CPCA: Factors sensitive to disease, not genetics

p

<0.05

p<0.05Unspecified error required recalculation of component weightsSame networks found with addition of a fourth DMNDifferentiates NC and SIBs from SCZ

No longer appeared to identify intermediate phenotype

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CPCA: Not particularly sensitive in general420 HV CPCA (2back) = 4 factors

Neuro-

= 6 cognitive factors

2B as measured in labg estimates

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Big data benefits reproducibility…

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ENIGMA: first

GWAS+

sMRI

(Stein et al. 2009, 2010; Thompson et al., 2014)Big data benefits reproducibility…Heritability for novel phenesReplication on large scale

Slide37

OutlineA Tale of Two Lectures:

Imaging genetics & schizophrenia

Why imaging genetics?

Imaging genetics 101Multivariate analyses of fMRI: within experimental datasetGeneral vs specific factors in datag“i” : factor analytic solution of general factors in fMRI task data Multivariate analyses of fMRI redux: across experimental dataset

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Are phenotypes independent?

Slide39

Are phenotypes independent?

Slide40

Are phenotypes independent?

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The general cognitive factor (Spearman’s g)

(Dickinson et al., 2008)

(Jensen, 1998)

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Where is g?

‘Lesion maps’ from 241 patients w/ focal brain damage and

g

(Gläscher et al. PNAS 2010)

.

Barbey et al. (Brain 2012)

found similar results in 182 focal brain lesion patients

Various conceptual, functional and structural support for PFC and PAR (at minimum)

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Is

g

associated with fMRI activation?

Nback (n= 161) higher g  greater efficiencyReplication (n= 582) higher g  greater efficiencyExact overlap

Notes:

Analysis: SPM5 multiple regression controlling for age, sex

2B accuracy



g

(r = 0.3, p < 0.001)

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Replication…cont’dReplication 3 (n= 211)

Areas within replication exactly overlapping discovery…

discovery

Replication 5 (n= 306)

Replication 4 (n= 393)

Replication 4

Replication 3

Replication 5

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DSVT

v g

But…Faces v gMTL v g

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g

correlates with similar areas

across 4 tasks in same 161 HVs

Nback (n= 161)Notes:Analysis: SPM5 multiple regression controlling for age, sex161 with QC+ NB, MTL, Faces, DSVTNB as discovery ROI, others queried at p < 0.05

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Is there a general solution for fMRI?161 HVs with QC+ Nback, MTL (incidental encoding), Faces (response to aversive faces), and DSVT (processing speed)Individual 1st level maps created for each task

Sue Tong: automated script to extract parameter estimates in Automated Anatomical Labeling (AAL) ROIs

Mean fMRI ‘signal” transformed to Z score

Factor analysis:Principle component extraction Orthogonal and oblique rotationsFactor scores estimated i (fMRI g) = sum of factor scores Comparison across task and against cognitive measures (big g)

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fMRI (Nback) i

(161 HVs, max likelihood extraction w/ varimax rotation, 60.1% total variance explained, goodness-of-fit p < 1e-5)

.50

.41.40.42.26.42.30.13.11

.13

.16

.10

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fMRI (Nback) i

(161 HVs, max likelihood extraction w/ varimax rotation, 60.1% total variance explained, goodness-of-fit p < 1e-5)

.79

.90.59.54.7

.36

.43

.56

.52

.27

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fMRI (Nback) i

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fMRI (Faces) i

(161 HVs, max likelihood extraction w/ varimax rotation, 58.1 % total variance explained, goodness-of-fit p < 1e-5)

.44

.40.45.40.39.41

.10

.10

.10

.10

.10

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fMRI (Faces) i

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fMRI (DSVT) i

(161 HVs, max likelihood extraction w/ varimax rotation, 51.6 % total variance explained, goodness-of-fit p < 1e-5)

.40

.41.42.41.40.41.10

.10

.10

.10

.10

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fMRI (DSVT) i

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fMRI (MTL) i

(161 HVs, max likelihood extraction w/ varimax rotation, 51.6 % total variance explained, goodness-of-fit p < 1e-5)

.40

.41.42.41.40.41.10

.10

.10

.10

.10

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fMRI (MTL) i

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An i by any other name…

 

 

gFaces i

Nback i

DSVT i

MTL i

g

Pearson Correlation

1

-.060

.042

-.025

-.210

**

Sig. (1-tailed)

 

.226

.300

.378

.004

Faces i

Pearson Correlation

-.060

1

-.182

*

-.027

.014

Sig. (1-tailed)

.226

 

.010

.365

.428

Nback i

Pearson Correlation

.042

-.182

*

1

.056

-.049

Sig. (1-tailed)

.300

.010

 

.241

.267

DSVT i

Pearson Correlation

-.025

-.027

.056

1

.124

Sig. (1-tailed)

.378

.365

.241

 

.059

MTL i

Pearson Correlation

-.210

**

.014

-.049

.124

1

Sig. (1-tailed)

.004

.428

.267

.059

 

**. Correlation is significant at the 0.01 level (1-tailed).

*. Correlation is significant at the 0.05 level (1-tailed).

 

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Structural MRI

(351 HVs, max likelihood extraction w/ varimax rotation, 48 % total variance explained, goodness-of-fit p < 1e-5)

Slide59

Summary:

A Tale of Two Lectures:

Imaging genetics & schizophrenia

Imaging genetics easier in an age of data sharing and public databases

BOLD fMRI (IMHO) has never been about diagnosis = hello

RDoC

!

Multivariate analyses of fMRI: novel findings, novel questions

General vs specific factors in data

g inspires a straight-forward, replicable multivariate analysis of fMRI (Factor analytic approach) (Dickinson et al.,

Biol

Psych 2008; JAMA Psych 2014, numerous)

“

i

” : factor analytic solution of general factors in fMRI task data

Multivariate

analyses of fMRI

redux

:

Data reduction writ large

Replication across tasks, labs, designs?

Slide60

Further musing…Multimodal data, multimodal analysis

fMRI phenotypes are not independent

Aspects within each task representing individual ‘positive manifold’

Is heritability about this general shared variance or specific task aspects?FMRI i as a data reduction methodNot gComplicated, but factor solution may be informed by other dataStructural MRI factor solution, no relationship to g or other cognitive factors g holds special relationship to fMRI dataTest whether reduced factor structure more related to genes, other MRI, clinical measures

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Thanks:

Dwight Dickinson

Sue Tong

Jessica IhneKaren BermanBarbara SpencerGraham BaumMorgan BartholomewAmanda ZheutlinCTNB clinical staff