Multivariate analyses in clinical populations: General factors & neuroimaging Joseph Callicott, MD fMRI/MRI Summer Course 6/20/14

Multivariate analyses in clinical populations: General

factors & neuroimaging

Joseph Callicott, MD

fMRI/MRI Summer Course 6/20/14

Introduction

The ‘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 analyses GWAS model = strict correction for

multiple comparisons Current model = parallel

correlation/association per dataset Proposed model = multivariate approach

w/data reduction Simplified analyses Smaller statistical ‘cost’ Some current theoretical approaches become

testable’ (RDoC)

Outline

A Tale of Two Lectures:

I. Imaging genetics & schizophrenia

I. Relevant issues for clinical populations

II. Why imaging genetics?III. Imaging genetics 101IV. Multivariate analyses of fMRI:

within experimental dataset

II. General vs specific factors in dataI. gII. “i” : factor analytic solution of

general factors in fMRI task dataIII. Multivariate analyses of fMRI

redux: across experimental dataset

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 ceiling I.e., ~100% accuracy and faster RT than patients

BOLD fMRI in healthy subjects ‘predictive relationships’ between

behavior and BOLD implicit in design, perhaps not strongly correlated with group map activation

Genetic associations to BOLD (the bulk of the imaging genetics literature) do not necessarily connote a ‘real’ effect of a given polymorphism

Is the phenotype heritable? In past, twin or sibling studies Currently, within ‘only’ HV = GCTA (Visscher)

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

(Van Snellenberg et al. 2006)

So, then, why imaging genetics? Crass commercial message: o Simple plan, high impact

o Have an fMRI task in a relatively large sample?

o Healthy controls preferableo N > 40o Draw blood or swab cheek

o Genotyping at most resolutions fast & cheap*

o In SPM: ANCOVA or regression suffice and seem reasonably powered

COMT led fro

m primate to

human imaging, a

nd then to

drugs targetin

g cognitive

impairm

ents

Seriously, though, why imaging genetics?

o Few routes to neural mechanism using in vivo human datao Animal model o Drug studyo BOLD fMRI (MRSI, MEG, EEG)

o Genes do not code for mental illness, per seo Genes code for heritable aspects of brain function,

intermediate- or endo-phenotypeso Genetic risk for illnesses like schizophrenia is polygenic,

heterogeneouso Gene interact with each other and the environment

o BOLD fMRI, as an alternate metric of specific or general cognitive systems, offers ‘real world validation’ o Putative genetic mutations (including private mutations

(CNVs)o In spite of growing sample sizes, association studies risk

false positiveso RDoC domains and constructs

o If these do not correspond to brain systems we can map, then may be as doomed as DSM

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 & cognition Very good software often

freely available: PLINK AFNI GingerALE-SLEUTH-MANGO R (many)

(Stefansson et al., Nature, 2014)

(*Visscher et al., 2010; Nan et al. 2012**; Postuma et al. 2002***; McGue & Bouchard, 1998#;^Burmeister et al., 2008)

Interest in imaging genetics predicated on heritability of phenotypes…

Callicott et al. Cereb Cortex 2000

Patients > Controls (N=13) (N=18)

Callicott et al. Am J Psychiatry 2003

Healthy Siblings > Controls (N=48) (N=33)

PFC BOLD during our Nback h2 = 0.4-0.5

Blokland et al. Biol Psych 2009, J Neurosci 2011; Koten et al. Science

2009

Finding genes for highly heritable, but complex diseases

affected person

unaffected

“nonpenetrant”

(Goldman et al. Nat Rev Genet 2006)

Remains difficult, even when n=100K1. Caused by many (100-1000s) of genes2. The effects of a mutation vary between people

Has 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

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

(Apud et al. 2006)

(Egan et al. 2001) (Meyer-Lindenberg et al. 2006)

Functional impact COMT Val105/158Met

val/metrs4680

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: • Sex effects mostly in males

((Papaleo et al., 2014)

striatum

mammalianPFC

sibs

patients

controls

COMT Genotype

WC

ST

Pe

rse

ve

rati

ve

Err

ors

(t-

sc

ore

s)

30

35

40

45

50

55

60

v v v m m m

genotype effectF=5.41, df= 2, 449;

p<.005.

Executive cognition

Effect of rs4680 on frontal lobe function

(Egan et al PNAS 2001)

n = 218n = 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

14

BOLD phenotypes in simple association: COMT and PFC

(Mier, Kirsch, Meyer-Lindenberg; 2009)

15

BOLD phenotypes in simple association: 5-HTTP and Amygdala

(Munafo, Brown, and Hariri; 2007))

16

BOLD phenotypes in simple association: Power?

(Barnett et al, 2008)

1st generation imaging genetics: simple associationo Candidate genes

o KIBRA impaired memory & expressed in hippocampus (Papassotiropoulos et al., Science 2006)

o Replication in 3 independent populations in behavioral memory measures

o In 30 healthy subjects, KIBRA associated with reduced hippo activation

o Genetic mutations modeled in cell culture or animals

o Association based on disease GWAS (ZNF804A)o Esslinger et al. 2009 (Science)o Rasetti et al. (Arch Gen Psychiatry)

2nd generation imaging genetics: GWAS era

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

(Goldman-Rakic & Selemon 1997)

(Seamans et al., 2001)

2nd generation imaging genetics: Epistasis and pathways

(Straub et al., 2007)

2nd generation imaging genetics: Epistasis

COMT: V/V V/M

M/M

Bray Hap:

-/-

-/-

-/- +/- +/-

+/-

V/V V/M

M/M

V/V

V/M M/M

+/+

+/+

+/+

BO

LD

fM

RI

Lef

t D

LP

FC

(a

.u.)

COMT x Dysbindin interaction

o Epistasis (gene-gene interaction)o Initially based on candidate-by-candidate

o Buckholtz et al., Mol Psychiatry 2007o Data-driven (machine learning)

o Nicodemus et al., Hum Genet 2010o Now predicated on detailed cellular or animal modeling

o COMT x DTNBP1 (Papaleo et al., 2013)o DISC1 x NKCC1 (Kim et al., Cell 2012 & Callicott et al. J Clin

Invest 2013)

2nd generation imaging genetics: Translational neuroscience

22

More of the same (‘sophisticated univariate’)? Network/connectivity? Hypothesis-free?

Hypothesis-free pattern detection (random forest)

ICA/PCA/CPCA networks

Next generation imaging genetics?

Novel phenotypes (processing speed)

Sophisticated univariate: Imaging GWAS

o BOLD fMRI GWAS o Nback (n = 364)o Illumina 650K chip genotypingo Automated extraction of AAL ROIso First GWAS + using BOLD fMRI (Callicott, Spencer, et al., in prep)

As a heritable trait, BOLD fMRI phenotypes show other sensitivities…..o Long history within animal literature showing significant effects of environment

on brain structure & function o Beneficial effects of ‘enrichment’ (toys, limit isolation) (Hebb, Am J

Psychiatry, 1955) o fMRI during social stress task influenced by environment

o Urban upbringing or urbanicity linked to increased risk for mental illness (Van Os et al., Nature, 2010)

(Lederbogen et al., Nature, 2012)

Sophisticated univariate: Novel questions

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

o fMRI during WM ( 3 cohorts (USA1 = 124; USA2 =92; Italy1=226 )o Sensitivity to childhood environment (Urbanicity) (Ihne et al., in submission)

Sophisticated univariate: Imaging G x E

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

Ihne et al., in preparation

Sophisticated univariate: Imaging G x E

27Constrained principle component analysis (CPCA) (David AA Baranger – Wash U)

http://www.nitrc.org/projects/fmricpca

• Todd 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’

28

CPCA

• 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)


29

CPCA

• 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)


30

• Identify and then display components using MRICon for anatomical localization (http://www.nitrc.org/projects/mricron)

• In this case, not really using estimated hemodynamics

• Rather, we wish to compare effect of diagnosis or genotype using component scores and predictor weights

• Predictor weight = contribution of G matrix to changes in components over the fMRI time series (~ correlation of component score and g)

CPCA

http://www.nitrc.org/projects/mricron



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 SCZ• No longer appears to be identifying intermediate phenotype

CPCA: Nback systems

Anti-task Networkresembles

cingulate from DMN+ hippocampus

WM NetworkDLPFC

+parietal

Motor system

Anti-task #2 resembles parietal

From DMN+ cerebellum

CPCA: Factors sensitive to disease, not genetics

p<0.05 p<0.05

• Unspecified error required recalculation of component weights• Same networks found with addition of a fourth DMN

• Differentiates NC and SIBs from SCZ• No longer appeared to identify intermediate phenotype

CPCA: Not particularly sensitive in general

• 420 HV • CPCA (2back) = 4 factors• Neuro- = 6 cognitive factors

• 2B as measured in lab• g estimates

CPCA F1 CPCA F2 CPCA F3 CPCA F42B accuracy 0.16 -0.21

2B accuracy (y) -0.082B RT (y) -0.11

F1 VerbalMemory 0.08F2 Nback

F3 VisualMemory 0.10 -0.14F4 ProcessingSpeed

F5 CardSortingF6 Span 0.09Little gBig g 0.10 (0.07)

Big data benefits reproducibility…

ENIGMA: first GWAS+ sMRI (Stein et al. 2009, 2010; Thompson et al., 2014)

Big data benefits reproducibility…

Heritability for novel phenes Replication on large scale

Outline


I. Imaging genetics & schizophrenia

I. Why imaging genetics?II. Imaging genetics 101III. Multivariate analyses of

fMRI: within experimental dataset

II. General vs specific factors in dataI. gII. “i” : factor analytic solution of

general factors in fMRI task data

III. Multivariate analyses of fMRI redux: across experimental dataset

Are phenotypes independent?

Pearson’s Correlations

DSST

HV>SIB>SCZ [Tal:--47 7 39]

LDLFPC

DSST HV>SIB>SCZ [Tal: 20 -30 1]

Rhippo

DSST SCZ>SIB>HV

[Tal: -47 32 26] LBA9

DSST SCZ>SIB>HV [Tal: 36 6 53]

RBA6

DSST HV>SIB>SCZ [Tal:--47 7 39]

LDLFPC

Pearson Correlation 1 .052 .173** .174**

Sig. .412 .006 .006

N 249 249 249 249

DSST HV>SIB>SCZ [Tal: 20 -30 1]

Rhippo

Pearson Correlation .052 1 -.121 .046

Sig. .412 .056 .468

N 249 249 249 249

DSST SCZ>SIB>HV [Tal: -47 32

26] LBA9

Pearson Correlation .173** -.121 1 .194**

Sig. .006 .056 .002

N 249 249 249 249

DSST

SCZ>SIB>HV [Tal: 36 6 53]

RBA6

Pearson Correlation .174** .046 .194** 1

Sig. .006 .468 .002

N 249 249 249 249

Nback SCZ>SIB>HV [Tal: 21 16 60]

LBA6

Pearson Correlation .029 -.128* .152* .235**

Sig. .647 .044 .017 .000

N 249 249 249 249

Nback HV>SIB>SCZ [Tal: 32 -42 7]

Rhippo

Pearson Correlation .152* .034 -.042 .106

Sig. .016 .594 .511 .095

N 249 249 249 249


ICC DSVT hypo Nback hyper NC (n=194)

Intraclass

Correlationa

95% Confidence Interval F Test with True Value 0

Lower Bound Upper Bound Value df1 df2 Sig

Single Measures .104b -.038 .243 1.233 189 189 .076

Average Measures .189c -.080 .391 1.233 189 189 .076


The general cognitive factor (Spearman’s g)

(Dickinson et al., 2008)(Jensen, 1998)

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)

Is g associated with fMRI activation?

Nback (n= 161) higher g greater

efficiency

Replication (n= 582) higher g greater

efficiency

Exact overlap

Notes:1. Analysis: SPM5 multiple regression controlling

for age, sex2. 2B accuracy g (r = 0.3, p < 0.001)

Replication…cont’d

Replication 3 (n= 211)

Areas within replication exactly overlapping discovery…

discovery



Replication 4

Replication 3

Replication 5

DSVT v g

But…

Faces v g

MTL v g

g correlates with similar areas across 4 tasks in same 161 HVs

Nback (n= 161)

Notes:1. Analysis: SPM5 multiple regression controlling for

age, sex2. 161 with QC+ NB, MTL, Faces, DSVT3. NB as discovery ROI, others queried at p < 0.05

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 rotations Factor scores estimated i (fMRI g) = sum of factor scores Comparison across task and against cognitive measures (big g)

iF1

Motor (R)Operculum (13)Cingulate (32)

SMA (R)Postcentral

Superior temporal gyrus (41,42)

Middle temporal gyrus (motion)

F2Insula

CaudatePutamen Pallidum

F3Cuneus (18)

Sup Occipital (19/7)Mid Occipital (39 &

19/37)Inf Occipital (V5/MT)

F4Cerebellum (8-

9)Cerebellar

vermis

F7SFG (8/9/6) (R)

MFG (R) IFG (44/45) (R)

Angular (R) (39)

F6SFG (8/9/6) (L)Medial SFG (8)Ant Cingulate

(24,32)Mid Cingulate

(24,31)F5

MFG (L)Sup Parietal (7)Inf Parietal (40)Supramarginal

(40)

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

fMRI (Nback) i

F7SFG (8/9/6) (R)

MFG (R) IFG (44/45) (R) Angular (R) (39)

SFG (R)

MFG (R) IFG (R)

Angular (R)

(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

iF1

Motor (R)Operculum (13)Cingulate (32)

SMA (R)FPostcentral

Superior temporal gyrus (41,42)

Middle temporal gyrus (motion)

F2Insula

CaudatePutamen Pallidum

F3Cuneus (18)

Sup Occipital (19/7)Mid Occipital (39 &

19/37)Inf Occipital (V5/MT)

F4Cerebellum (8-

9)Cerebellar

vermis

F7SFG (8/9/6) (R)

MFG (R) IFG (44/45) (R)

Angular (R) (39)

F6SFG (8/9/6) (L)Medial SFG (8)Ant Cingulate

(24,32)Mid Cingulate

(24,31)F5

MFG (L)Sup Parietal (7)Inf Parietal (40)Supramarginal

(40)

fMRI (Nback) i

g 2B %C

g Pearson Correlation .302**

Sig. (1-tailed) .000

2B %C

Pearson Correlation .302**


F1(SMA-temporal) Pearson Correlation .045 .115

Sig. (1-tailed) .287 .074

F2 (basal ganglia) Pearson Correlation .062


F3 (visual) Pearson Correlation .045 .076


F4 (cerebellum( Pearson Correlation -.141* -.028

Sig. (1-tailed) .037 .361

F5 (L MFG-PAR) Pearson Correlation -.033 -.006

Sig. (1-tailed) .337 .470

F6 (SFG-ACING) Pearson Correlation .264** .188**

Sig. (1-tailed) .000 .009

F7 (R MFG-IFG-PAR) Pearson Correlation -.133* -.146*

Sig. (1-tailed) .046 .032

i Nback Pearson Correlation .042 .077

Sig. (1-tailed) .300 .166

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

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

i

F1Sup Occipital (7)

Mid Occipital (39 & 19/37)

Inf Occipital (V5/MT)Fusiform (37, FFA)

F2Calcarine (17/18)

Cuneus (18, V2)Lingual (19, V3)

F3PremotorMFG (R)

IFG (44/45) (R)Sup Parietal

(7)Inf Parietal (L)

(40)

F4Hippocampus

ParahippocampusAmygdala

Inf Temporal (L) (IT)

F6Cerebellum (1)Cerebellum (6)

F5SFG (8/9/6)

MFG (L)Medial SFG (8)

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

fMRI (Faces) i

g Faces RT

g Pearson Correlation -.259**


Faces RT Pearson Correlation -.259**


F1 (higher visual - PAR) Pearson Correlation -.206** .017

Sig. (2-tailed) .009 .834

F2 (lower visual) Pearson Correlation -.019 .141

Sig. (2-tailed) .811 .073

F3 (R MFG-IFG-PAR) Pearson Correlation -.095 -.034

Sig. (2-tailed) .228 .664

F4 (Amyg-Hippo) Pearson Correlation .125 -.013

Sig. (2-tailed) .113 .867

F5 (L MFG-SFG) Pearson Correlation .095 -.087

Sig. (2-tailed) .228 .273

F6 (Cerebellum) Pearson Correlation -.044 .011

Sig. (2-tailed) .584 .886

Faces i Pearson Correlation -.060 .015

Sig. (2-tailed) .453 .849


i

F1Sup Occipital (7)



F2Calcarine (17/18)

Cuneus (18, V2)Lingual (19, V3)

F3PremotorMFG (R)

IFG (44/45) (R)

Sup Parietal (7)

Inf Parietal (L) (40)

F4Hippocampus

ParahippocampusAmygdala


F6Cerebellum (1)Cerebellum (6)

F5SFG (8/9/6)

MFG (L)Medial SFG (8)

i

F1Orbitofrontal (47)Operculum (13)

InsulaSuparmarginal (L) (40)Sup temporal (41,42)

Mid temporal (TG)

F2SFG (8/9/6)

MFGIFG (44/45)Med SFG (8)

Ant Cing (24/32)Mid Cing (24/31)

F3HippocampusParahippocam

pus AmygdalaCaudate PutamenPallidumThalamus

F4Inf Occipital

Fusiform Sup Cerebellum

F6Calcarine (17/18)Cuneus (18, V2)Lingual (19, V3)

Sup Occipital (7)

F5Inf

Cerbellum

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

fMRI (DSVT) i

g DSVT RT

g Pearson Correlation 1 .304**


DSVT RT Pearson Correlation .304** 1


F1(Orbito-Temp) Pearson Correlation -.057 -.008

Sig. (1-tailed) .237 .458

F2 (PFC-CING) Pearson Correlation .033 -.076

Sig. (1-tailed) .340 .170

F3 (Hippo-BG) Pearson Correlation .039 .076

Sig. (1-tailed) .310 .170

F4 (Lat Occ-Sup Cere) Pearson Correlation -.063 -.172*

Sig. (1-tailed) .214 .014

F5 (Inf Cerebellum) Pearson Correlation -.134* -.168*

Sig. (1-tailed) .045 .017

F6 (Visual-PAR) Pearson Correlation .122 .076

Sig. (1-tailed) .062 .168

G_med_dsvt Pearson Correlation -.025 -.111

Sig. (1-tailed) .378 .081



i

F1Orbitofrontal (47)Operculum (13)

InsulaSuparmarginal (L) (40)Sup temporal (41,42)

Mid temporal (TG)

F2SFG (8/9/6)

MFGIFG (44/45)Med SFG (8)

Ant Cing (24/32)Mid Cing (24/31)

F3HippocampusParahippocam

pus AmygdalaCaudate PutamenPallidum

Thalamus

F4Inf Occipital

Fusiform Sup Cerebellum

F6Calcarine (17/18)Cuneus (18, V2)Lingual (19, V3)

Sup Occipital (7)

F5Inf

Cerbellum

iF1

Calcarine (17/18)Cuneus (18, V2)Lingual (19, V3)Sup Occipital (7)



Inf Temporal (R) (IT)

F2Caudate PutamenPallidumThalamus

F3SFG (8/9/6)

MFGAngular (R) (39)

F4Insula

Sup Temporal (TG)

Mid Temporal (R) (TG)

F6Precentral (L)IFG (44/45)IFG (45/46)

Postcentral (L)Sup Parietal (L) (7)Inf Parietal (L) (40)Supramarginal (L)

(40)

F5Hippocampus

Parahippocampus


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

fMRI (MTL) i

g MTL RT

g Pearson Correlation 1 -.071


MTL RT Pearson Correlation -.071 1


F1 (Visual) Pearson Correlation -.077 -.038

Sig. (1-tailed) .165 .317

F2 (Thalamus-BG) Pearson Correlation .065 -.031

Sig. (1-tailed) .205 .347

F3 (MFG-SFG-PAR) Pearson Correlation -.023 .051

Sig. (1-tailed) .388 .259

F4 (Insula-Temp) Pearson Correlation -.163* .091

Sig. (1-tailed) .020 .126

F5 (Hippo-Para) Pearson Correlation -.099 .249**

Sig. (1-tailed) .105 .001

F6 (IFG-L PAR) Pearson Correlation -.238** .120

Sig. (1-tailed) .001 .065

MTL i Pearson Correlation -.210** .174*

Sig. (1-tailed) .004 .014



iF1


Mid Occipital (39 & 19/37)Inf Occipital (V5/MT)Fusiform (37, FFA)

Inf Temporal (R) (IT)

F2Caudate PutamenPallidum

Thalamus

F3SFG (8/9/6)

MFGAngular (R) (39)

F4Insula

Sup Temporal (TG)Mid Temporal (R)

(TG)


Postcentral (L)Sup Parietal (L) (7)Inf Parietal (L) (40)Supramarginal (L)

(40)

F5Hippocampus

Parahippocampus


An i by any other name…

g Faces 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



Structural MRI

F1thick F2thick F3thick sMRI i

F1thick Pearson Correlation 1 -.580** .665** .668**

Sig. (2-tailed) .000 .000 .000

F2thick Pearson Correlation -.580** 1 -.386** .077

Sig. (2-tailed) .000 .000 .385

F3thick Pearson Correlation .665** -.386** 1 .802**

Sig. (2-tailed) .000 .000 .000

sMRI i Pearson Correlation .668** .077 .802** 1

Sig. (2-tailed) .000 .385 .000

Little_g Pearson Correlation -.037 -.072 -.031 -.094

Sig. (2-tailed) .679 .421 .727 .291

Big_g Pearson Correlation -.090 -.029 -.083 -.131

Sig. (2-tailed) .311 .749 .354 .140

Trans_Factor1_VerbalMemory Pearson Correlation .049 -.036 -.018 -.005

Sig. (2-tailed) .576 .682 .837 .955

Trans_Factor2_Nback Pearson Correlation .029 -.140 -.021 -.093

Sig. (2-tailed) .751 .123 .814 .309

Trans_Factor3_VisualMemory Pearson Correlation -.089 .044 -.077 -.076

Sig. (2-tailed) .339 .639 .411 .418

Trans_Factor4_ProcessingSpee

d

Pearson Correlation -.018 -.030 .027 -.015

Sig. (2-tailed) .842 .736 .761 .863

Trans_Factor5_CardSorting Pearson Correlation -.031 -.019 -.079 -.084

Sig. (2-tailed) .735 .835 .380 .350

Trans_Factor6_Span Pearson Correlation -.047 .007 -.019 -.037

Sig. (2-tailed) .600 .935 .833 .679


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

Summary:

iF1




Inf Temporal (R) (IT)F2

Caudate PutamenPallidumThalamus

F3SFG (8/9/6)

MFGAngular (R)

(39)

F4Insula

Sup Temporal (TG)



Postcentral (L)Sup Parietal (L)

(7)Inf Parietal (L)

(40)Supramarginal

(L) (40)F5

HippocampusParahippocam

pusInf Temporal

(L) (IT)


I. Imaging genetics & schizophreniaI. Imaging genetics easier in an

age of data sharing and public databases

II. BOLD fMRI (IMHO) has never been about diagnosis = hello RDoC!

III. Multivariate analyses of fMRI: novel findings, novel questions

II. General vs specific factors in dataI. g inspires a straight-forward,

replicable multivariate analysis of fMRI (Factor analytic approach) (Dickinson et al., Biol Psych 2008; JAMA Psych 2014, numerous)

II. “i” : factor analytic solution of general factors in fMRI task data

III. Multivariate analyses of fMRI redux: I. Data reduction writ largeII. Replication across tasks, labs, designs?

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 method

Not g Complicated, but factor solution may be

informed by other data Structural MRI factor solution, no relationship

to g or other cognitive factors g holds special relationship to fMRI data

Test whether reduced factor structure more related to genes, other MRI, clinical measures

iF1




Inf Temporal (R) (IT)F2

Caudate PutamenPallidumThalamus

F3SFG (8/9/6)

MFGAngular (R)

(39)

F4Insula

Sup Temporal (TG)



Postcentral (L)Sup Parietal (L)

(7)Inf Parietal (L)

(40)Supramarginal

(L) (40)F5

HippocampusParahippocam

pusInf Temporal

(L) (IT)

Thanks:Dwight Dickinson

Sue TongJessica Ihne

Karen Berman

Barbara SpencerGraham Baum

Morgan BartholomewAmanda Zheutlin

CTNB clinical staff

Documents

Multivariate analyses in clinical populations: General factors & neuroimaging Joseph Callicott, MD fMRI/MRI Summer Course 6/20/14