NAMIC Core 3.2

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NAMIC Core 3.2

• Steven Potkin - UCI
• James Kennedy U of Toronto

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Opportunity Challenges

• Core 3.2 Goal Understand brain function in the
  context of an individuals unique genetic
  background
• It is assumed that the integration of the
  multi-modal imaging with genetics will provide
  new knowledge not otherwise obtainable knowledge
  discovery
• Requires Core 1 and 2 integrative tools to meet
  the daunting challenges

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Opportunity Challenges

• Schizophrenia as the DBP
• Heterogeneous symptoms and course
• Heritable
• Subtle differences in structure and function
• Must involve brain circuitry
• Challenges Behavior and performance, cause and
  effect, medication, structure and/or function
• Genetic background influences brain development,
  function, and structure in both specific and non
  specific ways

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A Collaborative Approach to Research
To understand the time course of the disease
why first episode patients become chronically ill
Sheitman BB, Lieberman JA. J Psychiatr Res.
1998(May-Aug)32(3-4)143-150
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Statistical Parametric Map Mai et al Human Atlas,
2001
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Average canonical anatomical DLPFC in the group
Actual site of anatomical DLPFC in this subject
COMT effects
Non-COMT effects
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Paracingulate/ precuneus
18d,19d-V2-3,V6
SMA
pulvinar
tectum
17/V1
precuneus
tectum
18d,19d-V2-3,V6
pulvinar
mesopontine reticular formation
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Implied circuitry- retinal/meso-tectal-pulvinar-pr
estriate-precuneus-SMA
Potentially an arousal related visual posterior
attention/orienting pathway
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Clozapine The First Atypical Antipsychotic
1980s

• Efficacy
• Reduction of positive and negative symptoms
• Improvements treatment refractory patient
• Reduction of suicidality in SA schizo. patients
• Side effects
• ? low EPS, ? TD
• ? risk of agranulocytosis
• ? risk of respiratory/cardiac arrest myopathy
• ? moderate-to-high weight gain
• ? potential for seizures
• Receptor binding
• Lowest D2 affinity
• Highest D1 affinity

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Potkin et al ,2003
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Clozapine Challenges Dogma

• The EPS associated with conventional
  antipsychotics led to the misconception that EPS
  were required for an antipsychotic
• Clozapines lack of EPS established that EPS are
  not a necessary for a therapeutic response

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AIMS Scores for DRD3 Msc I Polymorphism after
Typical Neuroleptic Treatment
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Corrected Mean AIMS score
1,2
2,2
1,1
n34
n53
n25
Basile et al 2000
DRD3 Genotype
F2,95 8.25, p lt 0.0005, Power 0.568,
r-square0.297
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UCI Brain Imaging Center
FDG Metabolic Changes With Haloperidol By D3
Alleles
Gly-Gly
Other Alleles
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Negative Symptom Schizophrenia
Potkin et al A J Psychiatry 2002
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The COMT Gene
PROMOTER
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COMT-MB START CODON
STOP CODON
TRANSMEMBRANE SEGMENT
COMT-S START CODON
G1947 ? A1947 ? COMT-MB/S Val158/108 ?
Met158/108
SOURCE NCBI, GEN-BANK, ACCESSION Z26491
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Dopamine terminals in striatum and in prefrontal
cortex are not the same
Striatum
DA
DA transporter
DA receptor
COMT
Prefrontal cortex
NE transporter
modified after Sesack et al J. Neurosci 1998,
Weinberger, ICOSR, 2003
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COMT Genotype Effects Executive Function
n 218
n 181
n 58

Genotype Effect (F5.41, df 2, 449) plt.004.
Egan et al PNAS 2001
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COMT Genotype and Cortical Efficiency During
fMRI Working Memory Task
Val-valgtval-metgtmet-met use more DLPFC to do same
task, SPM 99, plt.005
Egan et al PNAS 2001
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Transdisciplinary Imaging Genetics Center
Synergies With NAMIC
To identify useable endophenotypes targeted
therapeutics
Combine neuroimaging
With behavioral and clinical measures
and genetics
Neuroimaging
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Proto-endophenotypes

• Combinations of
• Imaging measures (sMRI, FMRI, PET, EEG)
• Genotypes
• Clinical profiles
• Treatment response
• Cognitive behavior
• Iterative refinements to develop endophenotypes
• Studies like these represent a wealth of
  potential information ---if they can be combined

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How many genes are needed for one disease ?

• In complex traits, genes act together and we must
  understand how if we want to understand the
  biology of disease
• modelling genegene interactions the
  Epistasis effect

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Strategies for Discovering Novel Candidate Genes
Drug Targets in Schizophrenia
Knowledge of Pathophysiology of Neuronal
Circuits Candidates From Neurotransmitter
Systems Pharmacology of Disease

Candidates From Replicated Genome Wide
Microsatellite Surveys
Identifying Hotspots
and Genes in ROI
Candidate Genes

Candidates From
Candidates From Microarray Studies in
Animals Drug Models (e.g., PCP,
amphetamine) Treatment Models (e.g,
neuroleptics)
Microarray Screens
(30,000 Genes)
Plus validation with
In situ hybridization
WE Bunney
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Efficacy Negative Cognitive DM
Weight Suicide Clozapine 90 80
25 50 85 2 Asenapine 90 80
50 10 15 ? Olanzapine 80
70 20 70
90 4 Ziprasidone 85 75 30
20 10 ?
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Imaging Genetics Conference

• The First International Imaging Genetics
  Conference was held January 17 and 18, 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.

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Legacy Dataset-UCI 28

• fMRI
• PET
• Structural MRI
• Genetic - SNP
• Clinical measures
• Cognitive measures
• EEG
• 28 subjects, chronic Sz

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fMRI Working Memory

• Sternberg task

• Example Results

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PET Continuous Peformance Task

• Continuous Performance Task (CPT)
• Sustained attention
• Selective attention
• Motor control task

• PET results
• Same as fMRI except no time course data

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Structural MRI

• Cortical thickness measures in mm
• By defined region

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Genetics
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
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Clinical Scores

• PANSS
• Thirteen subscales/factors
• Positive, negative, and global summary scores
• Lindenmayer 5-factors summary
• Marder 5-factors summary

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Cognitive Scores
Immediate 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)
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Example Query of Federated Database
How can you predict which prodromal subject will
develop first episode schizophrenia ?
Integrated View
Mediator
Wrapper
Wrapper
Web
Wrapper
Wrapper
Wrapper
Wrapper
PubMed, Expasy
PET fMRI
Clinical
ERP
Receptor Density
Structure
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Anatomical Accuracy

• Operational Plan (Fallon led effort)
• Step 1. Core 3-2 will develop operational
  criteria and guidelines for differentiation of
  areas and subareas.
• Step 2. Core 3-2 will develop 10 training sets in
  which areas and subareas of BA 9 and 46 have been
  differentiated as a rulebased averaged
  functional anatomical unit applied to individual
  subjects.
• Needs to be applied to UCI 28 by Tannenbaum
• Gliches in Freesurfer, Slicer must be overcome
  and features added eg subcortical white matter
  segmentation for tractography
• Extend to visualization (Falko Kuester)
• Supplement Slicer with multiple segmentation
  programs in addition to Freesurfer

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Anatomical Accuracy

• Specified Operational Plan
• Step 3. Core 1 will develop algorithms and
  methods for defining areas based on the training
  dataset.
• Step 4. Iterations of Steps 1 through 3 will
  perfect and validate the various methods for
  defining areas.
• Step 5. The area identification methods will be
  implemented by Core 3.

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Identified 80 ROIs Relevant to DBP of
Schizophrenia
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Circuitry Analysis

• Specified Operational Plan
• Step 1. Core 3-2 will collaborate with Core 2 to
  implement algorithms for structural equation
  modeling, and the canonical variate analysis.
• Fallon Kilpatrick, piloted but as a first step
  need to better quantify and automate ROI based on
  literature, Knowledge Based Learning as a general
  tool.
• Step 2. Core 3-2 will use step 1 software to test
  Core 3-2 hypotheses.
• Step 3. Core 3-2 in collaboration with Core 2
  will extend the canonical variate analysis
  methods of Step 1 to determine images that
  distinguish among tasks, clinical symptoms, and
  cognitive performance.
• Step 4. Core 3-2 and Core 1 will collaborate to
  integrate canonical variate analyses with machine
  learning approaches for detecting circuitry.

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Genetic Analysis in Combination with Imaging Data

• Specified Operational Plan
• Step 1. Core 3 will type multiple genetic markers
  at selected genes relevant to schizophrenia and
  brain structure.
• Step 2. Core 2 will extend Toronto in-house
  Phase v2.0 software for measuring two gene-gene
  interactions to multiple genes and make the
  software more user friendly to neuroscience and
  genetic researchers in general.
• Step 3. Core 3-2 will determine linkage
  disequilibrium structure on the genetic data
  using specific programs such as Haploview, GOLD,
  and 2LD and construct haplotypes.

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Genetic Analysis in Combination with Imaging Data

• Specified Operational Plan (cont.)
• Step 4. Core 3-2 will complete genetic analyses
  on the haplotypes developed, identified by the
  Core 3-2 software in Step 3, and test for
  gene-gene interaction using refinement of Toronto
  Phase v2.0 software from Step 2.
• Step 5. Core 3-2 will collaborate with Core 1 to
  develop methods for combining genetic and imaging
  data using machine learning technologies and
  Bayesian hierarchical modeling.
• Step 6. Iterations of Step 5 will develop
  predictive models and suggest hypotheses.

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Genetics and Neuroimaging Current Findings and
Future Strategies
James L Kennedy MD, FRCPC
IAnson Professor of Psychiatry and Medical
Science Head, Neurogenetics Section, Clarke
Division, Director, Department of Neuroscience
Research Centre for Addiction and Mental Health
(CAMH), University of Toronto SG Potkin, D
Mueller, M Masellis, N Potapova, F Macciardi
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How do genes determine brain characteristics?
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Molecular Genetic Approach
Pharmacogenetics
Gene Expression
Pharmacology
Neurobiology
Phenotype
Endophenotype
-Psychophysiology Neuroimaging
Sub-pheno
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Cytoarchitectural abnormalities
Control
Comparison of hippocampal pyramids at the CA1 and
CA2 interface between control and
schizophrenic. Cresyl violet stain,
original magnification X250 Conrad et
al. (1991) Arch Gen
Psychiatry
Schizophrenia
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Will the Brain Derived Neurotrophic Factor (BDNF)
Gene Predict Grey Matter Volume?
BDNF-1 SNP BDNF-2 BDNF-3 BDNF-4
Exon 11
Val-66-met
(GT)n repeat (function? mRNA stability)
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BDNF val66met MRI functional brain imaging (Egan
et al, Cell 2003)
The red/yellow areas indicate brain regions
(primarily hippocampus) that function differently
between val/val (n8) and val/met (n5) subjects
while performing a working memory task. Subjects
with the met allele had more abnormal function.
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Haplotype TDT BDNF (GT)n repeat val66met in
schizophrenia

HTDT for 170-val66 c2 7.11 1 df p
0.007 Muglia et al, (2002)
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Hippocampal shape as a phenotype for genetic
studies
Figure 1d Principal deformation for the right
hippocampus for normal controls (top) and
schizophrenia patients (bottom). Four views
(front, lateral, back, medial) of each shape are
shown. The color indicates the direction and the
magnitude of the deformation, changing from blue
(inwards) to green (no deformation) to red
(outwards).
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Dopamine D2 Receptor 5 Genetic Markers Studied
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Dopamine D2 Gene LD Potkin new SCZ sample
(N28)

• Linkage Disequilibrium map (Haploview)
• 5 markers across the DRD2 gene

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DRD2 Schiz Responder/Non-Resp. (chi2) Potkin N48

SNP Genotype Res (Freq) No-Res (Freq) P-Value
-241 A/G 11 11 (0.79) 16 (0.80) 0.151
1 A 12 1 (0.07) 4 (0.20)
2 G 22 2 (0.14) 0 (0.00)
-141C Ins/Del 11 0 (0.00) 2 (0.10) 0.050
1 Del 12 2 (0.14) 9 (0.45)
2 Ins 22 12 (0.86) 9 (0.45)
TaqIB C/T 11 1 (0.07) 0 (0.00) 0.475
1 C 12 3 (0.21) 5 (0.25)
2 T 22 10 (0.72) 15 (0.75)
C957T C/T 11 6 (0.42) 7 (0.35) 0.885
1 C 12 4 (0.29) 6 (0.30)
2 T 22 4 (0.29) 7 (0.35)
TaqIA T/C 11 1 (0.07) 0 (0.00) 0.290
1 T 12 3 (0.21) 8 (0.40)
2 C 22 10 (0.72) 12 (0.60)
Del -gt Non-Responder
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DRD2 Quantitative Data Total BPRS (ANCOVA)
Potkin N-48
SNP Genotype (N) Mean (SD, 95CI) P-Value
-241 A/G 11 (27) -5.33 (11.9, -10.0/-0.6) 0.307
1 A 12 (5) -4.20 (8.7, -15.0/6.6)
2 G 22 (2) -24.50 (6.4, -81.7/32.7)
-141C Ins/Del 11 (2) 4.50 (12, -103/112) 0.128
1 Del 12 (11) -0.73 (9.5, -7.1/5.6)
2 Ins 22 (21) -10.24 (11.9, -15.6/-4.8)
TaqIB C/T 11 (1) -20.00 --- 0.378
1 C 12 (8) -6.00 (14.7, -18.3/6.3)
2 T 22 (25) -5.84 (11.3, -10.5/-1.2)
C957T C/T 11 (13) -7.15 (13.3, -15.2/0.9) 0.882
1 C 12 (10) -6.00 (9.9, -13.1/1.1)
2 T 22 (11) -5.55 (13.2, -14.4/3.3)
TaqIA T/C 11 (1) -20.00 --- 0.035
1 T 12 (11) -1.18 (11.8, -9.1/6.7)
2 C 22 (22) -8.23 (11.6, -13.4/-3.1)
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D2 TaqIA Genotypes vs. total BPRS response
score(p 0.035) Potkin N48
1,1 1,2 2,2
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D2 TaqIA vs. Positive Symptoms (ANCOVA p 0.07)
Potkin N48
1,1 1,2 2,2
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Migrating Window DRD2 Haplotype Analysis
(COCAPhase) Potkin N48
Window Global P-value
1-2-3 0.019
2-3-4 0.041
3-4-5 0.924
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Individual D2 Haplotype Tests Within Window 1-2-3
(global p 0.019 COCAPhase Potkin N48)
Haplotype Resp. (Freq.) Non-Resp. (Freq.) P-value
1-1-2 1 (0.04) 13 (0.33) 0.007
1-2-1 3 (0.11) 5 (0.13) 0.820
1-2-2 19 (0.67) 18 (0.45) 0.115
2-1-2 1 (0.03) 0 (0.00) 1.000
2-2-1 2 (0.07) 0 (0.00) 0.057
2-2-2 2 (0.08) 4 (0.10) 0.924
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Mochida, 2000
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SNAP-25 Gene Marker LDPotkin new sample N28
The darker red color denotes stronger
relationship (linkage) between any two markers
. Above the diagonal is D and below is
correlation, r.
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SNAP-25 Gene vs SchizophreniaPotkin N28 Cases
versus controls (chi-sq)
0 control, 1 schizophrenia SNAP-25 DdelI
0 control, 1 schizophrenia SNAP-25 MnlI
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Gene-Gene Interactions in Schizophrenia First
Steps

• M Lanktree, J Grigull, D Mueller, P Muglia, FM
  Macciardi, JL Kennedy

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BIOINFORMATICS APPLICATIONS Vol. 20 no. 0 2004,
pages 12 PedSplit pedigree management for
stratified analysis M. B. Lanktree1,., L.
VanderBeek1, F. M. Macciardi1,2 and J. L.
Kennedy1 1Neurogenetics Section, Centre for
Addiction and Mental Health, Department
of Psychiatry, University of Toronto, 250 College
Street, Toronto M5T 1R8, Canada and 2Department
of Human Genetics, University of Milan, Italy
PEDSPLIT is a simple pedigee arrangement software
that stratifies the sample conditioned on factors
including the proband's sex and genotype status
in order to assist investigations into gene-gene
interaction, haplotype relative risk, and
sexually dimorphic effects.
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TDT
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C-TDT Results D4 D1
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Will MOG gene variants predict white matter
abnormalities?
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Hypothesized Autoimmune Mechanism in Schizophrenia
Antibodies
B-Lymphocyte
Inflammation
Mast Cell
Chemokines
Illustration taken from www.phototakeusa.com.
Autoantibodies cross-react with neuronal proteins
(eg myelin?) during fetal brain development,
causing subtle damage to the CNS, leading to SCZ
in early adulthood (Swedo, 1994).
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Allele
?2 0.727 0.947
0.080 1.195 0.000
0.600
P Value 0.394 0.330
0.777 0.274 1.000
0.439
Figure 7. TDT for MOG-(TAAA)n. Global Chi-Square
3.550 5 d.f. P 0.726.
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Prefrontal fMRI activity and myelin reduced in
schizophrenia
Figure 31-4 Statistical parametric maps of the
fractional anisotropy (FA) (left) and Magnetic
Transfer Ratio (MTR) (myelin) (right) group
comparison. Similar areas in yellow on both maps
correspond to the location of both the internal
capsule and prefrontal white matter, and indicate
smaller values of FA and myelin in schizophrenia
patients (n14) compared with controls (n15).
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UNC
Fractional Anisotropy
Hypothesis MOG, MAG, MBP genes will predict
quantity or distribution of myelinated tracts
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DTI New MRI Imaging Technique Reveals Brain
Circuits
Cingulum
Corpus callosum
Dorsal stream
Frontal striatial projections
Fornix
Actual white matter tracks in schizophrenic
patient revealed by DTI (colors and location by
J. Fallon)
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Complexities in Genetics Neuroimaging

• Genetic variants express themselves in many ways
  singularly, or combined (haplotypes, epistasis,
  partial penetrance)
• What are the appropriate phenotypes to use from
  brain imaging data?
• How to control massive multiple testing of genome
  scan x brain voxels (millions x millions)?

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Summary

• D2 role in schizophrenia and clozapine response?
• SNAP-25 gene involved in Schizophrenia and
  neurodevelopment?
• BDNF gene candidate for grey matter measures?
• MOG gene candidate for white matter?
• Vast expanses of quality data await us we only
  need to develop our informatics sophistication
• National Alliance for Medical Imaging and
  Computing
• NAMIC
• www.na-mic.org

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