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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 0.30 0.26 0.22 0.18 0.14 0.10 0.06 0.02 -0.02 -0.06 -0.10 -0.14 DRD1 5’ - 0.18 0.15 0.14 0.11 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 11 22 11 5061 12 11 22 12 11 12 11 5064 12 12 22 11 11 11 11 5024 22 11 12 12 11 12 12 5028 22 22 22 11 11 12 11 5030 12 22 22 11 11 12 22 5034 12 12 12 12 11 12 5035 12 11 22 22 11 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 Wrapper Wrapper Wrapper Wrapper Wrapper Wrapper 0.30 0.26 0.22 0.18 0.14 0.10 0.06 0.02 -0.02 -0.06 -0.10 -0.14 PET & fMRI PubMed, Expasy 0.18 0.15 0.14 0.11 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 SNAP-25 DdelI 11,00 12,00 22,00 278 167 22 16 8 1 294 175 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