NAMIC Core 3.2NAMIC Core 3.2

Opportunity & ChallengesOpportunity & Challenges Develop methods for combining imaging Develop methods for combining imaging

and genetic data: and genetic data: imaging geneticsimaging genetics links links two distinct forms of datatwo distinct forms of data

Goal: Understand brain function in the Goal: Understand brain function in the context of an individual’s unique genetic context of an individual’s unique genetic backgroundbackground

It is assumed that the integration of these It is assumed that the integration of these field will provide new knowledge not field will provide new knowledge not otherwise obtainable: knowledge otherwise obtainable: knowledge discoverydiscovery

Opportunity & ChallengesOpportunity & Challenges Schizophrenia as the exemplar:Schizophrenia as the exemplar:

Heterogeneous symptoms and course; Heterogeneous symptoms and course; Heritable; Heritable; Subtle differences in structure and function; Subtle differences in structure and function;

Must involve brain circuitry Must involve brain circuitry Challenges: Behavior and performance, cause Challenges: Behavior and performance, cause

and effect, medication, structure and/or functionand effect, medication, structure and/or function Genetic background influences brain Genetic background influences brain

development, function, and structure in both development, function, and structure in both specific and non specific ways specific and non specific ways

The challengesThe challenges Standard but subjective diagnostic Standard but subjective diagnostic

assessmentsassessments Time course of the diseaseTime course of the disease

– Unclear relationship between clinical profiles, Unclear relationship between clinical profiles, genotype, and disease progressiongenotype, and disease progression

– Multiple genes involvedMultiple genes involved– Multiple internal/external influencesMultiple internal/external influences

Multiple levels of study, from molecular to Multiple levels of study, from molecular to behavioralbehavioral

A Collaborative Approach to ResearchA Collaborative Approach to Research

Sheitman BB, Lieberman JA. J Psychiatr Res. 1998(May-Aug);32(3-4):143-150

Age (Years)

Good

Function

Poor15 20 30 40 50 60 70

Premorbid Progression StableRelapsingP

rod

rom

e

?Improving

•

Fir

st

Ep

isod

e

To understand the time course of the disease – why first episode patients become chronically ill

Statistical Parametric MapStatistical Parametric MapMai et al Human Atlas, 2001Mai et al Human Atlas, 2001

????????

????

??

??

????

????

Fallon’s PFC’s importance Fallon’s PFC’s importance

Implied Circuitry: visual attention and orienting

Clozapine: The First Atypical AntipsychoticClozapine: The First Atypical Antipsychotic

EfficacyEfficacy– Reduction of positive and negative symptomsReduction of positive and negative symptoms– Improvements treatment refractory patientImprovements treatment refractory patient– Reduction of suicidality in SA & schizo. patientsReduction of suicidality in SA & schizo. patients

Side effectsSide effects low EPS,low EPS, TDTD risk of agranulocytosisrisk of agranulocytosis risk of respiratory/cardiac arrest & myopathyrisk of respiratory/cardiac arrest & myopathy moderate-to-high weight gainmoderate-to-high weight gain potential for seizurespotential for seizures

Receptor bindingReceptor binding– Lowest D2 affinityLowest D2 affinity– Highest D1 affinityHighest D1 affinity

1980s1980s

Potkin et al ,2003

Clozapine Challenges DogmaClozapine Challenges DogmaClozapine Challenges DogmaClozapine Challenges Dogma

The EPS associated with The EPS associated with conventional antipsychotics led to conventional antipsychotics led to the misconception that EPS were the misconception that EPS were required for an antipsychoticrequired for an antipsychotic

Clozapine’s lack of EPS established Clozapine’s lack of EPS established that EPS are not a necessary for a that EPS are not a necessary for a therapeutic responsetherapeutic response

The EPS associated with The EPS associated with conventional antipsychotics led to conventional antipsychotics led to the misconception that EPS were the misconception that EPS were required for an antipsychoticrequired for an antipsychotic

Clozapine’s lack of EPS established Clozapine’s lack of EPS established that EPS are not a necessary for a that EPS are not a necessary for a therapeutic responsetherapeutic response

AIMS Scores for DRD3 Msc I Polymorphism after AIMS Scores for DRD3 Msc I Polymorphism after Typical Neuroleptic TreatmentTypical Neuroleptic Treatment

0

2

4

6

8

10

12

14

16

Ser/Ser Ser/Gly Gly/Gly

CorrectedCorrectedMean Mean AIMSAIMSscorescore

DRD3 GenotypeDRD3 GenotypeF[2,95] = 8.25, p < 0.0005, Power = 0.568, r-square=0.297F[2,95] = 8.25, p < 0.0005, Power = 0.568, r-square=0.297

n=34n=34 n=53n=53 n=25n=25

19

1,1 1,2 2,2

Basile et al 2000

FDG Metabolic Changes With Haloperidol FDG Metabolic Changes With Haloperidol By DBy D33 Alleles Alleles

Gly-Gly Other AllelesOther Alleles

UCI Brain Imaging Center

Negative Symptom SchizophreniaNegative Symptom Schizophrenia

Potkin et al A J Psychiatry 2002

Failure to activate Failure to activate frontal cxfrontal cx

Cerebellar attempt toCerebellar attempt to compensatecompensate

1 27kb

COMT-S START CODON

COMT-MB START CODONTRANSMEMBRANE SEGMENT

STOP CODON

PROMOTER

5´

22q11.2222q11.23CHROMOSOME 22

NlaIIINlaIII NlaIIINlaIIINlaIII

5´-GATGACCCTGGTGATAGTGG5´-CTCATCACCATCGAGATCAA

210 BP

PCR

…CATG…

..AGMKD...

…CGTG…

..AGVKD..

high-activity (3-4X)thermo-stable

Low Dopamine Available

low-activity (1X)thermo-labile

More Dopamine Available

G1947 A1947

COMT-MB/S:

Val158/108 Met158/108

SOURCE: NCBI, GEN-BANK, ACCESSION # Z26491

The COMT GeneThe COMT Gene

Dopamine terminals in striatum and in Dopamine terminals in striatum and in prefrontal cortex are not the sameprefrontal cortex are not the same

modified after: Sesack et al modified after: Sesack et al J. NeurosciJ. Neurosci 1998, 1998, Weinberger, ICOSR, 2003Weinberger, ICOSR, 2003

StriatumStriatum

Prefrontal cortexPrefrontal cortex

DADA

DA transporterDA transporter

DA receptorDA receptor

COMTCOMT

NE transporterNE transporter

Genotype Effect (F=5.41, df= 2, 449); Genotype Effect (F=5.41, df= 2, 449);

p<.004p<.004..

COMT Genotype Effects Executive FunctionCOMT Genotype Effects Executive Function

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

Egan et al Egan et al PNASPNAS 2001 2001

n = 218

n = 181

n = 58

COMT Genotype and Cortical Efficiency During COMT Genotype and Cortical Efficiency During fMRI Working Memory TaskfMRI Working Memory Task

Val-val>val-met>met-met use more DLPFC to do same task, SPM 99, p<.005

Egan et al Egan et al PNASPNAS 2001 2001

Proto-endophenotypesProto-endophenotypes Combinations of Combinations of

– Imaging measures (sMRI, FMRI, PET, EEG)Imaging measures (sMRI, FMRI, PET, EEG)– GenotypesGenotypes– Clinical profilesClinical profiles– Treatment responseTreatment response– Cognitive behaviorCognitive behavior

Iterative refinements to develop Iterative refinements to develop endophenotypesendophenotypes

Studies like these represent a wealth of Studies like these represent a wealth of potential information ---if they can be potential information ---if they can be combinedcombined

GoalsGoals

Neuroimaging

-48-48 GG

Inheritedgenotype

-48-48 AA

3’3’5’5’--

3’3’5’5’--

DRD1

Clinical and cognitive measures

0.150.18

0.140.11

-0.14-0.10-0.06-0.020.020.060.100.140.180.220.260.30

ARIP - 20MG ARIP - 30MG RISP - 06MG PLACEBOTreatment Group

5 6 2 8 1

+

8

+

3

5 6 2 8 15 6 2 8 1

++

88

++

33

Combine neuroimaging

With behavioral and clinical measuresand genetics

To identify useable endophenotypes & targeted therapeutics

DNA

How many genes are needed for one disease ?How many genes are needed for one disease ?

In complex traits, genes act together and we must In complex traits, genes act together and we must understand “how” if we want to understand the biology of understand “how” if we want to understand the biology of disease: disease:

modelling gene^gene interactions – the Epistasis effectmodelling gene^gene interactions – the Epistasis effect

Gene A Gene B

+++

++++

+

+ +++

++++

0

1

2

3

p value=0.01

120.7 Kb

p value=0.05

DAAO / 12q

MDAAO-5

0

1

2

3

106.4 Kb

p value=0.05

p value=0.01

G72 / 13q

M-22

ModelRisk genotypes

Odds Ratio

Standard error z P>|z| [95%C.I.forO.R]

G72-DAAO M-22_AA 1.89 0.76 1.59 0.11 0.86 - 4.14M-22_AG 1.82 0.72 1.52 0.13 0.84 - 3.95

MDAAO-5_TT 1.05 0.75 0.06 0.95 0.26 - 4.24M-22_AA*/ MDAAO-5_TT 5.02 3.95 2.05 0.04 1.08 - 23.45M-22_AG*/ MDAAO-5_TT 1.73 1.30 0.73 0.47 0.40 - 7.52

C.I. : confident interval ;O.R. : Odd RatioOdds Ratio

Strategies for Discovering Novel Candidate Strategies for Discovering Novel Candidate Genes & Drug Targets in SchizophreniaGenes & Drug Targets in Schizophrenia

Candidates FromMicroarray Studies in

AnimalsDrug Models

(e.g., PCP, amphetamine)Treatment Models (e.g, neuroleptics)

Knowledge of Pathophysiology of Neuronal CircuitsCandidates FromNeurotransmitter

SystemsPharmacology of

DiseaseCandidate

Genes

Candidates From

Microarray Screens(30,000 Genes)

Plus validation with

In situ hybridization

Microsatellite Surveys

Identifying “Hotspots” &and Genes in ROI

Candidates FromReplicated Genome Wide

WE Bunney

Computer analysis

NeuroarrayWWW:

Analyze Image

Probabilities of medication

response and development of

side-effects

Efficacy Negative Cognitive DM Weight Suicide

Clozapine 90 80 25 50 852Asenapine 90 80 50 10 15?Olanzapine 80 70 20 70 904Ziprasidone 85 75 30 20 10?

Aim 1: Imaging Genetics ConferenceAim 1: Imaging Genetics Conference The First International Imaging Genetics The First International Imaging Genetics

Conference was held January 17 and 18, Conference was held January 17 and 18, 2005.2005.

To assess the state of the art in the various established fields of genetics and imaging, and to facilitate the transdisciplinary fusion needed to optimize the development of the emerging field of Imaging Genetics.

Legacy DatasetLegacy Dataset

fMRIfMRI PETPET Structural MRIStructural MRI Genetic - SNPGenetic - SNP Clinical measuresClinical measures Cognitive measuresCognitive measures EEG EEG

– 28 subjects, chronic Sz 28 subjects, chronic Sz

fMRI: Working MemoryfMRI: Working Memory

Sternberg task: Sternberg task: Example ResultsExample Results

5 6 2 8 1

+

8

+

3

PET: Continuous Peformance TaskPET: Continuous Peformance Task

Continuous Continuous Performance Task Performance Task (CPT)(CPT)– Sustained attentionSustained attention– Selective attentionSelective attention– Motor control taskMotor control task

+ 0

+9

PET results: PET results: – Same as fMRI except no Same as fMRI except no

time course datatime course data

Structural MRIStructural MRI

Cortical thickness measures in mmCortical thickness measures in mm By defined regionBy defined region

GeneticsGenetics

5HT2A (T102C)

DRD1(DdeI)

DRD2(BstNI) _141

DRD2(Taq1A)

DRD2_rs1799978

DRD2_rs1800498

DRD2_rs4648317

5058 2 2 1 1 2 2 1 2 1 1 1 1 1 1

5059 1 2 1 1 2 2 1 1 1 1 2 2 1 1

5061 1 2 1 1 2 2 1 2 1 1 1 2 1 1

5064 1 2 1 2 2 2 1 1 1 1 1 1 1 1

5024 2 2 1 1 1 2 1 2 1 1 1 2 1 2

5028 2 2 2 2 2 2 1 1 1 1 1 2 1 1

5030 1 2 2 2 2 2 1 1 1 1 1 2 2 2

5034 1 2 1 2 1 2 1 2 1 1 1 2  

5035 1 2 1 1 2 2 2 2 1 1 1 1 1 1

5037 1 2 1 2 2 2 1 1 1 1 1 2 1 1

Clinical ScoresClinical Scores

PANSSPANSS– Thirteen subscales/factorsThirteen subscales/factors– Positive, negative, and global summary Positive, negative, and global summary

scoresscores– Lindenmayer 5-factors summaryLindenmayer 5-factors summary– Marder 5-factors summaryMarder 5-factors summary

Cognitive ScoresCognitive ScoresImmediate Word List Recall Total (total words recalled across all 3 trials)

Delayed Word List Recall Total (total words recalled from the 15 presented, after ~25 min delay)

Delayed Word List Recognition Total (total words correctly identified, when presented visually with 35 distractor words after ~25 min delay)

Visual Recognition Correct (total correct hits; pt is shown 15 geometric shapes, then those are mixed with 15 similar, distractor, shapes, and pt says 'Yes, I saw that one', or 'No, I didn't see that one'.

Visual Recognition Correct (total false alarms; pt says 'yes', when he should've said 'no')

Visual Retention Ratio (calculated as Vrcor/Vrfa)

Letter Number Span (total correct; pt hears mixed up numbers and letters, which they must recite in order--numbers, small to large and then letters--alphabetically.)

Trails A (time to complete a task of connecting numbered circles in order)

Trails A Errors (incorrect numbers connected)

Trails B (time to complete a task of connecting alternating numbered and lettered circles in order)

Trails B Errors (incorrect numbers or letters connected)

Example Query of Federated DatabaseExample Query of Federated Database

PET & fMRI

How can you predict which prodromal subject will develop first episode schizophrenia ?

Integrated View

Receptor Density ERP

Web

PubMed, Expasy

Wrapper

WrapperWrapper

Wrapper

Structure

Wrapper

Clinical

Wrapper

MediatorMediator

0.150.18

0.140.11

-0.14-0.10-0.06-0.020.020.060.100.140.180.220.260.30

ARIP - 20MG ARIP - 30MG RISP - 06MG PLACEBOTreatment Group

Anatomical AccuracyAnatomical Accuracy

Anatomical AccuracyAnatomical Accuracy

Anatomical AccuracyAnatomical Accuracy Operational Plan (Fallon led effort)Operational Plan (Fallon led effort)

– Step 1. Core 3-2 will develop operational criteria and Step 1. Core 3-2 will develop operational criteria and guidelines for differentiation of areas and subareas.guidelines for differentiation of areas and subareas.

– Step 2. Core 3-2 will develop 10 training sets in which areas Step 2. Core 3-2 will develop 10 training sets in which areas and subareas of BA 9 and 46 have been differentiated and subareas of BA 9 and 46 have been differentiated as a as a rule–based averaged functional anatomical unit rule–based averaged functional anatomical unit applied to individual subjects. applied to individual subjects.

Needs to be applied to UCI 28 by TannenbaumNeeds to be applied to UCI 28 by Tannenbaum Gliches in Freesurfer, Slicer must be overcome and Gliches in Freesurfer, Slicer must be overcome and

features added eg subcortical white matter features added eg subcortical white matter segmentation for tractographysegmentation for tractography

Extend to visualiztion (Falco Kuester)Extend to visualiztion (Falco Kuester) Supplement Slicer with multiple segmentation programs Supplement Slicer with multiple segmentation programs

in addition to Freesurferin addition to Freesurfer

Anatomical AccuracyAnatomical Accuracy

Specified Operational PlanSpecified Operational Plan– Step 3. Core 1 will develop algorithms and Step 3. Core 1 will develop algorithms and

methods for defining areas based on the methods for defining areas based on the training dataset.training dataset.

– Step 4. Iterations of Steps 1 through 3 will Step 4. Iterations of Steps 1 through 3 will perfect and validate the various methods for perfect and validate the various methods for defining areas.defining areas.

– Step 5. The area identification methods will be Step 5. The area identification methods will be implemented by Core 3. implemented by Core 3.

– Step 6. Validation of the methods by Core 3-2 Step 6. Validation of the methods by Core 3-2 on new set of subjects.on new set of subjects.

Identified 80 ROIs Relevant to DBP Identified 80 ROIs Relevant to DBP of Schizophreniaof Schizophrenia

LEFT AMYGDALA.txt* RIGHT AMYGDALA.txt*LEFT ANGULAR GYRUS.txt* RIGHT ANGULAR GYRUS.txt*

LEFT ANTERIOR CINGULATE.txt* RIGHT ANTERIOR CINGULATE.txt*LEFT ANTERIOR COMMISSURE.txt* RIGHT ANTERIOR COMMISSURE.txt*LEFT ANTERIOR NUCLEUS.txt* RIGHT ANTERIOR NUCLEUS.txt*

LEFT BRODMANN AREA 10.txt* RIGHT BRODMANN AREA 10.txt*LEFT BRODMANN AREA 11.txt* RIGHT BRODMANN AREA 11.txt*LEFT BRODMANN AREA 13.txt* RIGHT BRODMANN AREA 13.txt*LEFT BRODMANN AREA 17.txt* RIGHT BRODMANN AREA 17.txt*LEFT BRODMANN AREA 18.txt* RIGHT BRODMANN AREA 18.txt*LEFT BRODMANN AREA 19.txt* RIGHT BRODMANN AREA 19.txt*LEFT BRODMANN AREA 1.txt* RIGHT BRODMANN AREA 1.txt*LEFT BRODMANN AREA 20.txt* RIGHT BRODMANN AREA 20.txt*LEFT BRODMANN AREA 21.txt* RIGHT BRODMANN AREA 21.txt*

LEFT BRODMANN AREA 22.txt* RIGHT BRODMANN AREA 22.txt*LEFT BRODMANN AREA 23.txt* RIGHT BRODMANN AREA 23.txt*LEFT BRODMANN AREA 24.txt* RIGHT BRODMANN AREA 24.txt*LEFT BRODMANN AREA 25.txt* RIGHT BRODMANN AREA 25.txt*

Circuitry AnalysisCircuitry Analysis Specified Operational PlanSpecified Operational Plan

– Step 1. Core 3-2 will collaborate with Core 2 to Step 1. Core 3-2 will collaborate with Core 2 to implement algorithms for structural equation modeling, implement algorithms for structural equation modeling, and the canonical variate analysis. and the canonical variate analysis.

Fallon & Kilpatrick, piloted but as a first step need to better Fallon & Kilpatrick, piloted but as a first step need to better quantify and automate ROI based on literature, Knowledge quantify and automate ROI based on literature, Knowledge Based Learning as a general tool.Based Learning as a general tool.

– Step 2. Core 3-2 will use step 1 software to test Core 3-2 Step 2. Core 3-2 will use step 1 software to test Core 3-2 hypotheses.hypotheses.

– Step 3. Core 3-2 in collaboration with Core 2 will extend Step 3. Core 3-2 in collaboration with Core 2 will extend the canonical variate analysis methods of Step 1 to the canonical variate analysis methods of Step 1 to determine images that distinguish among tasks, clinical determine images that distinguish among tasks, clinical symptoms, and cognitive performance.symptoms, and cognitive performance.

– Step 4. Core 3-2 and Core 1 will collaborate to integrate Step 4. Core 3-2 and Core 1 will collaborate to integrate canonical variate analyses with machine learning canonical variate analyses with machine learning approaches for detecting circuitry.approaches for detecting circuitry.

Genetic Analysis in Combination Genetic Analysis in Combination with Imaging Datawith Imaging Data

Specified Operational PlanSpecified Operational Plan– Step 1. Core 3 will type multiple genetic Step 1. Core 3 will type multiple genetic

markers at selected genes relevant to markers at selected genes relevant to schizophrenia and brain structure.schizophrenia and brain structure.

– Step 2. Core 2 will extend Toronto “in-house” Step 2. Core 2 will extend Toronto “in-house” Phase v2.0 software for measuring two gene-Phase v2.0 software for measuring two gene-gene interactions to multiple genes and make gene interactions to multiple genes and make the software more user friendly to the software more user friendly to neuroscience and genetic researchers in neuroscience and genetic researchers in general.general.

– Step 3. Core 3-2 will determine linkage Step 3. Core 3-2 will determine linkage disequilibrium structure on the genetic data disequilibrium structure on the genetic data using specific programs such as Haploview, using specific programs such as Haploview, GOLD, and 2LD and construct haplotypes.GOLD, and 2LD and construct haplotypes.

Genetic Analysis in Combinatin Genetic Analysis in Combinatin with Imaging Datawith Imaging Data

Specified Operational Plan (cont.)Specified Operational Plan (cont.)– Step 4. Core 3-2 will complete genetic analyses Step 4. Core 3-2 will complete genetic analyses

on the haplotypes developed, identified by the on the haplotypes developed, identified by the Core 3-2 software in Step 3, and test for gene-Core 3-2 software in Step 3, and test for gene-gene interaction using refinement of Toronto gene interaction using refinement of Toronto Phase v2.0 software from Step 2.Phase v2.0 software from Step 2.

– Step 5. Core 3-2 will collaborate with Core 1 to Step 5. Core 3-2 will collaborate with Core 1 to develop methods for combining genetic and develop methods for combining genetic and imaging data using machine learning imaging data using machine learning technologies and Bayesian hierarchical technologies and Bayesian hierarchical modeling.modeling.

– Step 6. Iterations of Step 5 will develop Step 6. Iterations of Step 5 will develop predictive models and suggest hypotheses.predictive models and suggest hypotheses.