NA-MIC National Alliance for Medical Image Computing 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

National Alliance for Medical Image Computing Overview – Genetics Activities Circuitry and other statistical analyses Anatomical Accuracy for shape analysis and cortical and subcortical segmentation DTI Activities

National Alliance for Medical Image Computing 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

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, A Voineskos, D Mueller, M Masellis, N Potapova, F Macciardi Genetics and Neuroimaging in Schizophrenia Update

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

Molecular Genetic Approach Gene Variants Pharmacology Phenotype (Neuroimaging) Sub-pheno Endophenotype Neurobiology Pharmacogenetics Gene Expression -Psychophysiology

EXTRACTING DATA FOR ANALYSIS Data 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…) 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.

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

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

DISC1(Leu607Phe) Genotype in Schizophrenia vs Controls Chi-sq = 0.61; df=2; p=0.74 Potkin sample

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-3BDNF-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 * * HTDT for 170-val 66  2 = 7.11; 1 df; p = Muglia et al, (2002)

BDNF(val66met) Genotype in Schizophrenia vs Controls Chi-sq = 0.59; df=2; p=0.74 Potkin sample

HTTLPR (ins/del) in Schizophrenia (following: Hariri et al 2002 => predicts 25% of amygdala fxn) Chi-sq = 3.3; df=2; p=0.19 Note: L= L A, and L G functions as S so grouped together under S Potkin sample

Mochida, 2000

SNAP25 Genotype in Schizophrenia vs Controls Chi-sq = 9.4; df=2; p=0.009 Potkin sample Not for distribution

 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)

(CA)n(TAAA)n Location of MOG Gene in 6p21.3 Region (MHC Region) GABABR1 MOG HLA-FHLA-GHLA-AHLA-CHLA-B TNF C4A, C4B, C2, factor B, 21-OHase DR  Class IClass IIIClass II DQDO LMP/TAP DM DN DP NOTCH4 Histone Family SCA1 DTNBP1 telomerecentromere ~ 2.6 Mb Figure 2. Human MHC region and genes within the region.

Hypothesized Autoimmune Mechanism in Schizophrenia B-Lymphocyte Antibodies Inflammation Mast Cell Chemokines Illustration taken from 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).

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

Will MOG gene variants predict white matter abnormalities? (CA) repeat(TAAA) repeat Start codon Coding region C1334T C10991T (diagram not to scale) Promoter region

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 TRANSMIT  Results negative for diagnosis of schizophrenia MOG in Toronto Schiz sample

Haplotype analysis between MOG-(CA)n and MOG-(TAAA)n. Haplotype Analysis of MOG polymorphisms in SCZ

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

Dopamine System Genes Presented by Aristotle Voineskos MD COMT – Catechol-O-methyl transferase DRD3 – Dopamine receptor (D3) DRD2 – Dopamine receptor (D2)

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

COMT (val158met) Genotype in Schizophrenia vs Controls Chi-sq = 2.6; df=2; p=0.27 Potkin sample

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

Baseline Haloperidol (5wks) Baseline Gly-Gly (n=5) Gly-Ser & Ser-Ser (n=9) Brain Metabolism Following Haloperidol Treatment by D 3 Genotype (FDG, n=14) ( UCI Brain Imaging Centre; Potkin, Kennedy & Basile, 2003 )

Dopamine D3 Receptor Gene Potkin sample N=25

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

D2 Linkage Disequilibrium in Caucasians 11) TaqIA ) rs ) rs ) Taq1D 5) TaqIB 2) –141 Ins/Del 4) rs ) rs ) -241 A/G 9) rs ) NcoI ( Haploview)

DRD2 (-141C ins/del) Genotype in Schizophrenia vs Controls Chi-sq = 0.61; df=2; p=0.74 Potkin sample

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

Can Alleles Predict Circuitry? Need for anatomical accuracy D1 alleles predictions in schizophrenia –Clinical response to clozapine –Circuitry used in working memory task

Core 3.2 and Core 1: Anatomical Accuracy Sternberg task: Five Two Five items compared to Two

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

Statistical Parametric Map - GE-2048 Resolution Mai et al Human Atlas, 2001 ?? ?? ?? ??

Improved Circuit Specification Motor Circuit (BA6) OrbitalCortex Amygdala

Potkin et al,2003

Circuitry via Path Analysis: PLS Circuitry in a Working Memory task (5-2 load) by DRD1 genotype in schizophrenia

Spatial fMRI Activation Patterns Padhraic Smyth, UC Irvine

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

Subject 3 Estimated parameters for activation centers + : 4 runs within visit 1 O : 4 runs within visit 2

Detecting Spatial fMRI Activation Patterns beta map fBIRN phantom sensorimotor task z=30 slice Activation patterns estimated by mixture model (Kim, et al, 2005) Thresholded voxels (p<0.05) Not for distribution

National Alliance for Medical Image Computing 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

NA-MIC National Alliance for Medical Image Computing Blumenfeld Fig 2-15 pg 32 Cytoarchitectonics- Brodmann areas

Classical Approaches to Cytoarchitectonic Mapping of Human Prefrontal Cortex All pictures/drawings are from Rajkowska, G. & Goldman-Rakic, P.S. (1995). Cerebral Cortex 5:

Central Postcentral Precentral Intraparietal Parieto- occipital Superior frontal Inferior frontal Orbital Superior temporal Middle temporal Inferior temporal Idealized sulci

Fallon OccipOccip Heschl’s Frontal poleFrontal pole 7 ITG STG CB DMPFC DLPFC VMPFC LOF IFG Critical samples in BOLD

Fallon OccipOccip Heschl’s Frontal poleFrontal pole 7 ITG STG CB DMPFC DLPFC VMPFC LOF IFG Critical samples in BOLD

DMPFC DLPFC VMPFC LOF 25 ACd ACv IFG

National Alliance for Medical Image Computing Cases from Rajkowska & Goldman-Rakic's (1995) series showing individual variability of 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.

National Alliance for Medical Image Computing

Tip temporal lobeTip frontal pole ~50 mm 20mm 20% (~10mm) 40% (~20mm) DLPFC

Case 1 Anterior View Anterior-Inferior View Case 7 Case 12

NA-MIC National Alliance for Medical Image Computing wm cx “Thumbs”

beta map fBIRN phantom sensorimotor task Activation patterns mixture model (Kim, et al, 2005) Thresholded voxels (p<0.05) Add 20% “gutter region” around each strictly defined area (eg DLPFC) to capture “rogue” functional activations in different subject and patient Populations…”DLPC PLUS”

DLPFC BA 46 BA 7 SLF-2

NA-MIC National Alliance for Medical Image Computing

McCarthy, 2004

National Alliance for Medical Image Computing 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.

National Alliance for Medical Image Computing Publications Ramsey Al-Hakim, James Fallon, Delphine Nain, John Melonakos, and Allen Tannenbaum. “A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter.” Proc SPIE Medical Imaging, 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).

National Alliance for Medical Image Computing

Post-doc Position at UCI Computer Science Department working on Brain Imaging Speak to P Smyth