bic.uci
DESCRIPTION
Core 3.2 Activities University of California, Irvine—Brain Imaging Center Steven Potkin, Padhraic Smyth, James Fallon University of Toronto—Neurogenetics Section, Center for Addiction and Mental Health James Kennedy & Aristotle Voineskos. http://www.bic.uci.edu. Overview –. - PowerPoint PPT PresentationTRANSCRIPT
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NA-MICNational Alliance for Medical Image Computing http://na-mic.org
Core 3.2 Activities
University of California, Irvine—Brain Imaging CenterSteven Potkin, Padhraic Smyth, James FallonUniversity of Toronto—Neurogenetics Section, Center for Addiction and Mental Health James Kennedy & Aristotle Voineskos
http://www.bic.uci.edu
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National Alliance for Medical Image Computing http://na-mic.org
Overview –
• Genetics Activities
• Circuitry and other statistical analyses
• Anatomical Accuracy for shape analysis and cortical and subcortical segmentation
• DTI Activities
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National Alliance for Medical Image Computing http://na-mic.org
Datasets Available
• (1) Toronto genetic data on 300 schizophrenic patient and matched controls;
• (2) Vancouver, 47 first episode schizophrenia patients with structural MRI scans, cognitive testing, and genetic; and
• (3) Irvine 25 schizophrenic patients with fMRI, PET, EEG and 100k SNP genetic data.
• Issues with the NAMIC Toolkit
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James L Kennedy MD, FRCPCJames 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, A Voineskos, D Mueller, M Masellis,
N Potapova, F Macciardi
Genetics and Neuroimaging in Genetics and Neuroimaging in Schizophrenia Schizophrenia UpdateUpdate
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Genetics Summary• SNAP25 gene associated with schizophrenia in
Potkin sample, and Toronto sample• BDNF gene candidate for grey matter vol and fxn• Serotonin transporter gene for amygdala function• DISC1 gene for cortical thickness• NMDA, GRIN1 and 2B genes for grey matter• Newest data: MOG gene associated with total
brain white matter (as hypothesized in grant app)• Relational database developed for organizing
genetic + clinical + imaging data• Training available in genetics
National Alliance for Medical Imaging and Computing NAMIC www.na-mic.org
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Molecular Genetic Approach
Gene Variants
Pharmacology
Phenotype (Neuroimaging)
Sub-pheno
Endophenotype
Neurobiology
Pharmacogenetics
Gene Expression
-Psychophysiology
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EXTRACTING DATA FOR ANALYSISData are returned in a format suitable for association-type studies (m-link or case-control). Additional formats may be designed as needed (such as vertical haplotypes { } ). Data may be transcribed and converted to document formats supported by the analysis program (tab de-limited text, etc…).
1 2 2 1 1 2 2 22 1 1 1 2 2 1 1
With access to source codes, or by invoking special features in downstream applications, the database can include automated running of analyses or transfer of data to other spreadsheets/databases.
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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
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DISC-1 Gene Knock-Down (mouse)DISC1 gene knock down with inhibitory RNA in mouse cortex:
Result: migration of neurons from ventricular zone during fetal development is impaired by DISC1 knockdown. Morphology resembles schizophrenia pathology
Marginal Zone
Cortical Plate
Intermediate and SubVentricular Zone Ventricular side
Strongest inhibition
Kamiya et al, Nature Cell Biol 2005
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DISC1(Leu607Phe) Genotype in Schizophrenia vs Controls
024
6
810
1214
1618
20
phe,phe phe,leu leu,leu
SchizContr
Chi-sq = 0.61; df=2; p=0.74 Potkin
sample
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Will the Brain Derived Neurotrophic Factor (BDNF) Gene Predict Grey Matter Volume?
Val-66-met
(GT)n repeat (function? mRNA stability)
Exon 11
BDNF-1 SNP BDNF-2 BDNF-3 BDNF-4
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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 (n=8) and val/met (n=5) 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
2
7
26
10
5 5 6
12
0
5
10
15
20
25
30
TransmissionsNon Trans
** HTDT for 170-val66
2 = 7.11; 1 df; p = 0.007
Muglia et al, (2002)
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BDNF(val66met) Genotype in Schizophrenia vs Controls
0
2
4
6
8
10
12
14
Met/Met Met/Val Val/Val
SchizContr
Chi-sq = 0.59; df=2; p=0.74 Potkin
sample
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HTTLPR (ins/del) in Schizophrenia (following: Hariri et al 2002 => predicts 25% of amygdala fxn)
012
3
45
67
89
10
L L/S S/S
SchizContr
Chi-sq = 3.3; df=2; p=0.19
Note: L= LA, and LG functions as S so grouped together under S
Potkin sample
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Mochida, 2000
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SNAP25 Genotype in Schizophrenia vs Controls
0
2
4
6
8
10
12
14
16
18
1,1 1,2 2,2
SchizContr
Chi-sq = 9.4; df=2; p=0.009 Potkin
sample
Not for distribution
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may function as: a cellular adhesion molecule a regulator of oligodendrocyte microtubule stability a mediator of interactions between myelin and the immune system, particularly as an activator of the classical complement cascade via activation of C1q (Johns and Bernard, 1997).
The 2 polymorphisms examined are: a dinucleotide repeat “MOG-(CA)n” located upstream from the MOG transcription start site (Roth et al., 1995; Barr et al., 2001). a tetranucleotide repeat “MOG-(TAAA)n” located in the 3’ untranslated region (Roth et al., 1995; Malfroy et al., 1995).
Myelin Oligodendrocyte Glycoprotein (MOG)
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(CA)n (TAAA)n
Location of MOG Gene in 6p21.3 Region (MHC Region)
GA
BA
BR
1
MO
GH
LA
-F
HL
A-G
HL
A-A
HL
A-C
HL
A-B
TN
F
C4A, C4B, C2, factor B,
21-OHase DR
Class I Class III Class II
DQ
DO
LM
P/T
AP
DM
DN
DP
NO
TC
H4
His
tone
F
amil
y
SC
A1
DT
NB
P1
telomere centromere
~ 2.6 MbFigure 2. Human MHC region and genes within the region.
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Hypothesized Autoimmune Mechanism in Schizophrenia
B-Lymphocyte
Antibodies
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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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).
Prefrontal fMRI activity & myelin reduced in schizophrenia: Core 3.1
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Will MOG gene variants predict white matter abnormalities?
(CA) repeat (TAAA) repeat
Start codon
Coding region
C1334T C10991T
(diagram not to scale)
Promoter region
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TDT and Haplotype Samples: 113 schiz proband small nuclear families from Toronto => MOG-(CA)n & MOG-(TAAA)n
Statistics: TDT/S-TDT and haplotype analysis using TRANSMITResults negative for diagnosis of schizophrenia
MOG in Toronto Schiz sample
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Haplotype (CA.TAAA) Observed Expected Var (O-E) Chi-Square Prob.*13.3 16.265 12.969 4.8343 2.2468 0.1339*11.4 15.063 14.58 5.7084 0.040946 0.8396*12.4 22.178 21.11 7.8274 0.14573 0.7026*13.4 15.762 19.038 5.6831 1.8875 0.1695*11.5 21.607 22.025 8.6116 0.020206 0.887*12.5 9.7276 12.264 5.0056 1.2847 0.257*9.7 13.583 13.164 5.3076 0.033194 0.8554
Haplotype Analysis: MOG in SCZ
Global Chi-Square Value = 1.013; 6 df; P = 0.985 (* indicate allele frequencies used in the global chi-square test)
Haplotype analysis between MOG-(CA)n and MOG-(TAAA)n.
Haplotype Analysis of MOG polymorphisms in SCZ
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MOG vs Total Brain White Matter
• Sample: Dr. Honer UBC – 47 schiz, 24 cont• Phenotype: automated output from standard structural MRI –
total grey and white matter• MRI=> 3D SPGR: FOV 26cm TE 11.2ms TR 2.1ms Matrix 256 x 256 Thickness 1.5 mm Angle - perpendicular to AC-PC line Acquisition time - 6 minutes• C1334T marker genotype associated with white matter volume
(P=0.003)• Other MOG markers negative• All MOG markers negative for total grey matter volume
Not for distribution
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Dopamine System Genes
Presented by Aristotle Voineskos MD
• COMT – Catechol-O-methyl transferase
• DRD3 – Dopamine receptor (D3)
• DRD2 – Dopamine receptor (D2)
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COMT Gene
• Principal metabolizer of dopamine in frontal cortex
• Functional genetic variant: val vs. met• Val reduces dopamine levels • Val associated with poorer working memory
(Wienberger group)
• Ultimate hypothesis: cortical efficiency (fMRI) impaired in val carriers
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COMT (val158met) Genotype in Schizophrenia vs Controls
0
1
2
3
4
5
6
7
8
9
Val/Val Val/Met Met/Met
SchizContr
Chi-sq = 2.6; df=2; p=0.27 Potkin
sample
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Neuroanatomical Distributions of Neuroanatomical Distributions of Dopamine ReceptorsDopamine Receptors
(Seeman etal, 1995)
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DRD3 Gene
• Upregulation of D3 receptors and D3 mRNA following antipsychotic in rat brain
• Gly vs Ser variant reveal differences in affinity for dopamine
• Gly variant leads to increased striatal activity following haldol administration (Potkin et al ’03)
• Preliminary: gly variant incr in schizophrenia
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BaselineHaloperidol (5wks) Baseline
Gly-Gly (n=5)
Gly-Ser & Ser-Ser (n=9)
Brain Metabolism Following Haloperidol Treatment by D3 Genotype (FDG, n=14)
(UCI Brain Imaging Centre; Potkin, Kennedy & Basile, 2003)
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Dopamine D3 Receptor GenePotkin sample N=25
0
2
4
6
8
10
12
ser,ser ser,gly gly,gly
SchizContr
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Intro to Dopamine D2 Receptor
• D2 gene is the most established candidate gene
• All antipsychotic meds bind to D2 receptor; these meds treat positive sx successfully (hallucinations, delusions)
• D2 receptor should be involved at some level in pathophysiology of disease
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D2 Linkage Disequilibrium in Caucasians
11) TaqIA1 2 3 4 5 6 7 8
10) rs22425938) rs2242591
6) Taq1D
5) TaqIB
2) –141 Ins/Del4) rs1125394
3) rs4648317
1) -241 A/G
9) rs2242592
7) NcoI
(Haploview)
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DRD2 (-141C ins/del) Genotype in Schizophrenia vs Controls
024
6
810
1214
1618
20
del,del del,ins ins,ins
SchizContr
Chi-sq = 0.61; df=2; p=0.74
Potkin sample
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Genetics Summary• SNAP25 gene associated with schizophrenia in
Potkin sample, and Toronto sample• BDNF gene candidate for grey matter vol and fxn• Serotonin transporter gene for amygdala function• DISC1 gene for cortical thickness• Dopamine genes predict cortical & striatal fxn?• Newest data: MOG gene associated with total
brain white matter (as hypothesized in grant app)• Relational database developed for organizing
genetic + clinical + imaging data• Training available in genetics
National Alliance for Medical Imaging and Computing NAMIC www.na-mic.org
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Can Alleles Predict Circuitry?
• Need for anatomical accuracy• D1 alleles predictions in schizophrenia – Clinical response to clozapine– Circuitry used in working memory task
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Core 3.2 and Core 1: Anatomical Accuracy
• Sternberg task: Five Two
5 6 2 8 1
+
8
+
3
0 9
+
6
+
9
Five items compared to Two
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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
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Statistical Parametric Map - GE-2048 ResolutionStatistical Parametric Map - GE-2048 ResolutionMai et al Human Atlas, 2001Mai et al Human Atlas, 2001
????
????
??
??
????
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Improved Circuit SpecificationImproved Circuit Specification
Motor Circuit (BA6)Motor Circuit (BA6)
OrbitalOrbitalCortexCortex
AmygdalaAmygdala
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Potkin et al ,2003
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Circuitry via Path Analysis: PLS
Circuitry in a Working Memory task (5-2 load) by DRD1 genotype in
schizophrenia
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Spatial fMRI Activation Patterns
•
Padhraic Smyth, UC Irvine
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fMRI Activation Surface Modeling
• Model activation response surface (beta-maps,…)
• Analyze variability of the features
A 2-dimensional slice of right precentral gyrus at z=53
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Subject 3• Estimated parameters for activation centers
+ : 4 runs within visit 1
O : 4 runs within visit 2
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Detecting Spatial fMRI Activation Patterns
beta mapfBIRN phantom
sensorimotor taskz=30 slice
Activation patterns estimatedby mixture model (Kim, et al, 2005)
Thresholded voxels (p<0.05)
Not for distribution
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National Alliance for Medical Image Computing http://na-mic.org
Core 1, 2, and Core 3.2 Activities
• Anatomical Accuracy and Flexibility for Integration of Imaging Modalities (e.g. MRI, DTI, fMRI, PET and EEG) and statistical analyses
• Slicer development in tractography: Alpha and Beta testing.• Development of new visualization techniques and visual
analytics.• Bug reporting and tracking.• Prototype testing.• Feature requests.
• Participants: Core 1: Allan Tannenbaum lab (GT), Guido Gerig lab
(UNC)Core 2: Ron Kikinis and Steve Pieper labs
UCI: Jim Fallon, Martina Panzenboeck, Vid Petrovic, Falko Kuester
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NA-MICNational Alliance for Medical Image Computing http://na-mic.org
Blumenfeld Fig 2-15 pg 32
Cytoarchitectonics-Brodmann areas
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Classical Approaches to Cytoarchitectonic Mapping of Human Prefrontal Cortex
All pictures/drawings are from Rajkowska, G. & Goldman-Rakic, P.S. (1995). Cerebral Cortex 5:323-337.
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Central PostcentralPrecentral
Intraparietal
Parieto-occipitalSuperior
frontal
Inferiorfrontal
OrbitalSuperior temporal
Middle temporalInferior temporal
Idealized sulci
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Fallon
Occip
Heschl’s
Frontal pole
7
ITG
STG
CB
DMPFC
DLPFC
VMPFC
LOF
IFG
Critical samples in BOLD
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Fallon
Occip
Heschl’s
Frontal pole
7
ITG
STG
CB
DMPFC
DLPFC
VMPFC
LOF
IFG
Critical samples in BOLD
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DMPFCDLPFC
VMPFCLOF
25
ACd
ACvIFG
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National Alliance for Medical Image Computing http://na-mic.org
Cases from Rajkowska & Goldman-Rakic's (1995) series showing individual variabilityof gyral and sulcal anatomy as well as spatial variations in cytoarchitectural dispersion.Variations will be seen more clearly on the succeeding slide of two different cases.
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National Alliance for Medical Image Computing http://na-mic.org
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Tip temporal lobeTip frontal pole~50 mm
20mm
20% (~10mm)40% (~20mm)DLPFC
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Case 1
Anterior ViewAnterior-Inferior View
Case 7
Case 12
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NA-MICNational Alliance for Medical Image Computing http://na-mic.org
wm
cx
“Thumbs”
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beta mapfBIRN phantom
sensorimotor task
Activation patternsmixture model (Kim, et al, 2005)
Thresholded voxels (p<0.05)
Add 20% “gutter region” around eachstrictly defined area(eg DLPFC) to capture “rogue” functional activations in different subject and patientPopulations…”DLPC PLUS”
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DLPFCBA 46
BA 7
SLF-2
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NA-MICNational Alliance for Medical Image Computing http://na-mic.org
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McCarthy, 2004
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QuickTime™ and aMPEG-4 Video decompressor
are needed to see this picture.
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National Alliance for Medical Image Computing http://na-mic.org
Core 5 and Core 3.2 Activities
• Contributed to training material.• Participated in training sessions both as trainer and
trainee.• Hosted one-on-one advanced training sessions.• Training in neuroanatomy and circuitry and genetics.• UCI: Jim Fallon; UT: James Kennedy, Fabio Macciardi• At NAMIC meetings, UCI, GA Tech and UNC.
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National Alliance for Medical Image Computing http://na-mic.org
Publications
• Ramsey Al-Hakim, James Fallon, Delphine Nain, John Melonakos, and Allen Tannenbaum. “A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter.” Proc SPIE Medical Imaging, 2006.
• Kim, S. Smyth, P., Stern, H., Turner, J., FIRST BIRN. (2005) Parametric response surface models for analysis of multi-site fMRI data. Proceedings of the 8th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg New York, 3749, .
• Turner, J., Smyth, P., Fallon, J.F., Kennedy, J.L., Potkin, S.G., FIRST BIRN (2005). Imaging and genetics in schizophrenia. Neuroinformatics, in press
• Keator, D; Gadde, S; Grethe, J ; Taylor, D; FIRST BIRN; Potkin, S. A. (2005). General XML Schema and Associated SPM Toolbox for Storage and Retrieval of Neuro-Imaging Results and Anatomical Labels. Neuroinformatics, in press.
• Martucci L, Wong AHC, De Luca V, Likhodi O, Wong GWH, King N, Kennedy JL. NMDA receptor subunit gene GRIN2B in schizophrenia and bipolar disorder. Schizophrenia Research (in press).
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National Alliance for Medical Image Computing http://na-mic.org
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National Alliance for Medical Image Computing http://na-mic.org
Post-doc Position at UCI
• Computer Science Department working on Brain Imaging
• Speak to P Smyth