Transcript Transdisciplinary Imaging Genetics Center

NAMIC Core 3.2
Steven Potkin - UCI
James Kennedy – U of Toronto
Opportunity & Challenges
Core 3.2 Goal: Understand brain function
in the context of an individual’s 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

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
A Collaborative Approach to Research
To understand the time course of the disease –
why first episode patients become chronically ill
Premorbid
Poor
15
Prodrome
Function
• First Episode
Good
20
Stable
Relapsing
Progression
30
40
50
Age (Years)
Sheitman BB, Lieberman JA. J Psychiatr Res. 1998(May-Aug);32(3-4):143-150
?
Improving
60
70
Statistical Parametric Map
Mai et al Human Atlas, 2001
??
??
Actual site of “anatomical”
DLPFC in this subject
Average canonical “anatomical”
DLPFC in the group
COMT effects
Non-COMT effects
“Physiological” DLPFC
In normal subject for one
“DLPFC Task”
“Physiological” DLPFC
In sz subject for one
“DLPFC Task”
SMA
Paracingulate/
18d,19d-V2-3,V6
precuneus
pulvinar
tectum
17/V1
precuneus
tectum
18d,19d-V2-3,V6
mesopontine reticular formation
pulvinar
Implied circuitry- retinal/meso-tectal-pulvinar-prestriate-precuneus-SMA
Potentially an arousal related visual posterior attention/orienting pathway
Clozapine: The First Atypical Antipsychotic

Efficacy
1980s
– 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
Potkin et al ,2003
Clozapine Challenges Dogma
 The
EPS associated with
conventional antipsychotics led to
the misconception that EPS were
required for an antipsychotic
 Clozapine’s
lack of EPS
established that EPS are not a
necessary for a therapeutic
response
AIMS Scores for DRD3 Msc I Polymorphism after19
Typical Neuroleptic Treatment
16
14
12
Corrected
10
Mean
8
AIMS
score
6
4
2
1,1
1,2
2,2
0
Ser/Ser
n=34
Ser/Gly
Gly/Gly
n=53
n=25
DRD3 Genotype
F[2,95] = 8.25, p < 0.0005, Power = 0.568, r-square=0.297
Basile et al 2000
UCI Brain Imaging Center
FDG Metabolic Changes With Haloperidol
By D3 Alleles
Gly-Gly
Other Alleles
Negative Symptom Schizophrenia
Failure to activate
frontal cx
Cerebellar attempt to
compensate
Potkin et al A J Psychiatry 2002
The COMT Gene
CHROMOSOME 22
22q11.23
1
22q11.22
27kb
PROMOTER
5´
COMT-MB START CODON
TRANSMEMBRANE SEGMENT
COMT-S START CODON
210 BP
5´-CTCATCACCATCGAGATCAA
NlaIII
NlaIII
5´-GATGACCCTGGTGATAGTGG
NlaIII
G1947  A1947
…CGTG…
..AGVKD..
STOP CODON
PCR

COMT-MB/S:
NlaIII
…CATG…
Val158/108  Met158/108
..AGMKD...
high-activity (3-4X)
low-activity (1X)
thermo-stable
Low Dopamine Available
thermo-labile
More Dopamine Available
SOURCE: NCBI, GEN-BANK, ACCESSION # Z26491
NlaIII
Dopamine terminals in striatum and in
prefrontal cortex are not the same
Striatum
DA
DA transporter
DA receptor
Prefrontal cortex
COMT
NE transporter
modified after: Sesack et al J. Neurosci 199
Weinberger, ICOSR, 2003
COMT Genotype Effects Executive Function
Perseverative Errors (t-scores)
WCST
60
55
50
45
40
35
sibs
30
vv
vm
mm
patients
n = 218
n = 181
controls
n = 58
COMT Genotype
Genotype Effect (F=5.41, df= 2, 449);
p<.004.
Egan et al PNAS 2001
COMT Genotype and Cortical Efficiency During
fMRI Working Memory Task
Val-val>val-met>met-met use more DLPFC to do
same task, SPM 99, p<.005
Egan et al PNAS 2001
Transdisciplinary Imaging Genetics Center
Synergies With NAMIC
Combine neuroimaging
DNA
With behavioral and
clinical measures
and genetics
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DRD1
5’
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ARIP - 20MG ARIP - 30MG RISP - 06MG PLACEBO
Treatment Group
5 6 2 8
1
+
3’
-48
A
To identify useable
endophenotypes &
targeted therapeutics
8
5’
-
3’
-48
G
+
3
Inherited
genotype
Neuroimaging
Clinical and cognitive
measures
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

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 gene^gene interactions – the Epistasis effect
Gene A
Gene B
+
+ +
+ + +
+ +
+
+++++++
Strategies for Discovering Novel Candidate
Genes & Drug Targets in Schizophrenia
Candidates From
Replicated Genome Wide
Microsatellite Surveys
Identifying “Hotspots” &
and Genes in ROI
Candidate
Genes
Candidates From
Microarray Screens
(30,000 Genes)
Plus validation with
In situ hybridization
Knowledge of
Pathophysiology of
Neuronal Circuits
Candidates From
Neurotransmitter Systems
Pharmacology of Disease
Candidates From
Microarray Studies in Animals
Drug Models
(e.g., PCP, amphetamine)
Treatment Models
(e.g, neuroleptics)
Computer analysis
Probabilities of
medication response
and development of
side-effects
Neuroarray
WWW:
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
?
Analyze Image
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.
Legacy Dataset-UCI 28
 fMRI
 PET
 Structural
MRI
 Genetic - SNP
 Clinical measures
 Cognitive measures
 EEG
– 28 subjects, chronic Sz
fMRI: Working Memory

Sternberg task:
5 6 2 8 1
+
8
+
3

Example Results
PET: Continuous Peformance Task

Continuous
Performance Task
(CPT)
– Sustained attention
– Selective attention
– Motor control task
+
0
+
9

PET results:
– Same as fMRI except no
time course data
Structural MRI
 Cortical
thickness measures in mm
 By defined region
Genetics
5HT2A
(T102
C)
DRD2(B DRD2(T DRD2_r
DRD1(D
stNI)
aq1A
s179
deI)
_141
)
9978
DRD2_r
s180
0498
DRD2_r
s464
8317
5058
22
11
22
12
11
11
11
5059
12
11
22
11
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22
11
5061
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12
12
22
11
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5024
22
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5028
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22
22
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11
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12
22
22
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11
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22
5034
12
12
12
12
11
12
5035
12
11
22
22
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11
11
5037
12
12
22
11
11
12
11
Clinical Scores
 PANSS
– Thirteen subscales/factors
– Positive, negative, and global summary
scores
– Lindenmayer 5-factors summary
– Marder 5-factors summary
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)
Example Query of Federated Database
How can you predict which prodromal subject will develop
first episode schizophrenia ?
Integrated
View
Mediator
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PET & fMRI
PubMed,
Expasy
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ARIP - 20MG ARIP - 30MG RISP - 06MG PLACEBO
Treatment Group
Structure
Receptor Density
ERP
Web
Clinical
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
rule–based 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
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.
Identified 80 ROIs Relevant to DBP
of 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*
LEFT ANTERIOR NUCLEUS.txt*
RIGHT ANTERIOR COMMISSURE.txt*
RIGHT ANTERIOR NUCLEUS.txt*
LEFT BRODMANN AREA 10.txt*
LEFT BRODMANN AREA 11.txt*
LEFT BRODMANN AREA 13.txt*
RIGHT BRODMANN AREA 10.txt*
RIGHT BRODMANN AREA 11.txt*
RIGHT BRODMANN AREA 13.txt*
LEFT BRODMANN AREA
LEFT BRODMANN AREA
LEFT BRODMANN AREA
LEFT BRODMANN AREA
LEFT BRODMANN AREA
LEFT BRODMANN AREA
RIGHT BRODMANN AREA
RIGHT BRODMANN AREA
RIGHT BRODMANN AREA
RIGHT BRODMANN AREA
RIGHT BRODMANN AREA
RIGHT BRODMANN AREA
17.txt*
18.txt*
19.txt*
1.txt*
20.txt*
21.txt*
LEFT BRODMANN AREA 22.txt*
LEFT BRODMANN AREA 23.txt*
LEFT BRODMANN AREA 24.txt*
LEFT BRODMANN AREA 25.txt*
17.txt*
18.txt*
19.txt*
1.txt*
20.txt*
21.txt*
RIGHT BRODMANN AREA 22.txt*
RIGHT BRODMANN AREA 23.txt*
RIGHT BRODMANN AREA 24.txt*
RIGHT BRODMANN AREA 25.txt*
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 32 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.
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 genegene 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.
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.
Genetics and Neuroimaging:
Current Findings and Future Strategies
James L Kennedy MD, FRCPC
I’Anson 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
How do genes determine brain characteristics?
Molecular Genetic Approach
Gene
Gene Expression
Pharmacogenetics
Variants
Pharmacology
Neurobiology
Phenotype
-Psychophysiology;
Neuroimaging
Endophenotype
Sub-pheno
Cytoarchitectural abnormalities
Control
Schizophrenia
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
Will the Brain Derived Neurotrophic Factor
(BDNF) Gene Predict Grey Matter Volume?
BDNF-1 SNP
BDNF-2
Exon 11
Val-66-met
(GT)n repeat
(function? mRNA
stability)
BDNF-3
BDNF-4
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 (n=8) and
val/met (n=5) subjects
while performing a working
memory task. Subjects with
the met allele had more
abnormal function.
Haplotype TDT: BDNF (GT)n repeat &
val66met in schizophrenia
*
26
30
25
20
Transmissions
Non Trans
15
12
10
10
5
7
5
5
6
2
0
-2
-2
-1
-1
1
3
3
1
lo
lo
lo
lo
p
p
p
p
Ha
Ha
Ha
Ha
* HTDT for 170-val66
c2 = 7.11; 1 df;
p = 0.007
Muglia et al, (2002)
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).
Neuroanatomical Distributions of
Dopamine Receptors
(Seeman etal, 1995)
Dopamine D2 Receptor:
5 Genetic Markers Studied
1) -241 A/G 3) TaqIB
4) C957T
1
2) –141 Ins/Del
2
3
4
5
6
7
8
5) TaqIA
Dopamine D2 Gene LD:
Potkin new SCZ sample (N=28)
• Linkage
Disequilibrium
map
(Haploview)
• 5 markers across
the DRD2 gene
DRD2 Schiz Responder/Non-Resp. (chi2) Potkin N=48
SNP
Genotype
Res (Freq)
No-Res (Freq)
P-Value
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)
11
0 (0.00)
2 (0.10)
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)
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)
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)
1=T
12
3 (0.21)
8 (0.40)
2=C
22
10 (0.72)
12 (0.60)
-241 A/G
-141C Ins/Del
0.050
Del -> NonResponder
0.475
0.885
0.290
DRD2 Quantitative Data: Total BPRS (ANCOVA) Potkin N-48
SNP
Genotype
(N)
11
(27)
-5.33 (11.9, -10.0/-0.6)
1=A
12
(5)
-4.20
2=G
22
(2)
-141C Ins/Del
11
(2)
4.50
(12, -103/112)
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
1=C
12
(8)
-6.00 (14.7, -18.3/6.3)
2=T
22
(25)
-5.84 (11.3, -10.5/-1.2)
11
(13)
-7.15 (13.3, -15.2/0.9)
1=C
12
(10)
-6.00
2=T
22
(11)
-5.55 (13.2, -14.4/3.3)
11
(1)
-20.00
---
1=T
12
(11)
-1.18
(11.8, -9.1/6.7)
2=C
22
(22)
-8.23 (11.6, -13.4/-3.1)
-241 A/G
C957T C/T
TaqIA T/C
Mean (SD, 95%CI)
P-Value
0.307
(8.7, -15.0/6.6)
-24.50 (6.4, -81.7/32.7)
---
0.128
0.378
0.882
(9.9, -13.1/1.1)
*0.035
BPRS6MOD
D2 TaqIA Genotypes vs. total BPRS response score
(p = 0.035) Potkin N=48
20
10
0
-10
-20
-30
-40
N=
1
11.00
1,1
D2TAQ1A
11
22
12.00
22.00
1,2
2,2
D2 TaqIA vs. Positive Symptoms (ANCOVA; p = 0.07) Potkin N=48
20
10
43
44
0
-10
-20
N=
1
11.00
1,1
D2TAQ1A
11
22
1,2
22.00
12.00
2,2
Migrating Window DRD2 Haplotype Analysis
(COCAPhase) Potkin N=48
Window
Global P-value
1-2-3
0.019
2-3-4
0.041
3-4-5
0.924
1) -241 A/G
1
2) –141 Ins/Del
3) TaqIB
2
4) C957T
3
4
5
6
7
8
5) TaqIA
Individual D2 Haplotype Tests Within Window 1-2-3
(global p = 0.019; COCAPhase; Potkin N=48)
Haplotype
Non-Resp.
(Freq.)
13 (0.33)
P-value
1-1-2
Resp.
(Freq.)
1 (0.04)
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
*0.007
Mochida, 2000
SNAP-25 Gene Marker LD
Potkin new sample N=28
The darker red
color denotes
stronger
relationship
(linkage)
between any
two markers .
Above the
diagonal is D’
and below is
correlation, r.
SNAP-25 Gene vs Schizophrenia
Potkin N=28 Cases versus controls (chi-sq)
0 = control, 1 = schizophrenia * SNAP-25 DdelI
Crosstab
Count
0 = control, 1 = ,00
sc hizophrenia
1,00
Total
11,00
278
16
294
SNAP-25 DdelI
12,00
22,00
167
22
8
1
175
23
Chi-Square Te sts
Pearson Chi-Square
Lik elihood Ratio
Linear-by-Linear
As soc iation
N of Valid Cases
Value
,199a
,202
,060
Total
467
25
492
0 = control, 1 = schizophrenia * SNAP-25 MnlI
Crosstab
Count
2
2
As ymp. Sig.
(2-sided)
,905
,904
1
,806
df
0 = control, 1 = ,00
sc hizophrenia
1,00
Total
11,00
197
8
205
SNAP-25 MnlI
12,00
224
15
239
22,00
56
2
58
Chi-Square Te sts
492
a. 1 c ells (16,7%) have ex pec ted c ount les s than 5. The
minimum expected count is 1,17.
Pearson Chi-Square
Lik elihood Ratio
Linear-by-Linear
As soc iation
N of Valid Cases
Value
1,639a
1,649
,164
2
2
As ymp. Sig.
(2-sided)
,441
,438
1
,685
df
502
a. 1 c ells (16,7%) have ex pec ted c ount les s than 5. The
minimum expected count is 2,89.
Total
477
25
502
Gene-Gene Interactions in
Schizophrenia:
First Steps
M Lanktree, J Grigull, D Mueller, P
Muglia, FM Macciardi, JL Kennedy
BIOINFORMATICS APPLICATIONS Vol. 20 no. 0 2004, pages 1–2
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.
TDT
Polymorphism
BDNF(Eco) A2
BDNF(GT) A3*
DRD1(Bsp) A1
DRD1(Ddel) A2
DRD1(Hae) A2
DRD4 A4*
NMDA(Bfa) A2
NMDA(Bse) A2
NMDA(Msp) A1
TDT
T
NT
c2
118
88
114
128
110
100
32
42
117
72
57
105
116
97
79
15
26
100
11.137
6.628
0.370
0.590
0.816
2.464
6.149
3.765
1.332
p
0.000850
0.010058
0.543026
0.442428
0.366346
0.116476
0.013170
0.052350
0.248430
C-TDT Results D4 & D1
Bsp
11
12
22
not 1 1
Global
T
27
56
11
67
DRD4
2
NT c
33 0.60
38 3.45
8 0.47
46 3.90
7.72
p
0.439
0.063
0.491
0.048
0.100
Dde1
11
12
22
not 2 2
Global
T
8
55
33
63
DRD4
2
NT c
10 0.22
36 3.97
31 0.06
46 2.65
4.99
p
0.637
0.046
0.803
0.103
0.288
Will MOG gene variants predict
white matter abnormalities?
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).
TDT: MOG-(TAAA)n in SCZ
30
Transmitted
Not Transmitted
25
Count
20
15
10
5
0
Allele
*2
c2:
0.727
P Value:
0.394
*3
*4
*5
*6
*7
0.947
0.080
1.195
0.000
0.600
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.
Prefrontal fMRI activity and myelin reduced in schizophrenia
Figure 3:1-4: 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 (n=14) compared with controls (n=15).
Fractional Anisotropy
UNC
clustering
Bundle
selection
Hypothesis: MOG, MAG, MBP
genes will predict quantity or
distribution of myelinated tracts
Measurement
along tract
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)
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)?
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