NAMIC Core 3.2

1. NAMIC Core 3.2

2. Opportunity & Challenges • Develop methods for combining imaging and genetic data: imaging genetics links two distinct forms of data • Goal: Understand brain function in the context of an individual’s unique genetic background • It is assumed that the integration of these field will provide new knowledge not otherwise obtainable: knowledge discovery

3. Opportunity & Challenges • Schizophrenia as the exemplar: 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

4. The challenges • Standard but subjective diagnostic assessments • Time course of the disease • Unclear relationship between clinical profiles, genotype, and disease progression • Multiple genes involved • Multiple internal/external influences • Multiple levels of study, from molecular to behavioral

5. Good • First Episode Function Prodrome Stable Relapsing ? Improving Premorbid Progression Poor 15 20 30 40 50 60 70 Age (Years) 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

6. ?? ?? ?? ?? ?? ?? Statistical Parametric Map Mai et al Human Atlas, 2001

7. Fallon’s PFC’s importance

8. Implied Circuitry: visual attention and orienting

9. 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

10. Potkin et al ,2003

11. 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

12. 19 AIMS Scores for DRD3 Msc I Polymorphism after Typical Neuroleptic Treatment Corrected Mean AIMS score 1,2 2,2 1,1 n=34 n=53 n=25 Basile et al 2000 DRD3 Genotype F[2,95] = 8.25, p < 0.0005, Power = 0.568, r-square=0.297

13. UCI BrainImaging Center FDG Metabolic Changes With Haloperidol By D3 Alleles Gly-Gly Other Alleles

14. Failure to activate frontal cx Cerebellar attempt to compensate Negative Symptom Schizophrenia Potkin et al A J Psychiatry 2002

15. 22q11.23 22q11.22 CHROMOSOME 22 1 27kb PCR 210 BP 5´-CTCATCACCATCGAGATCAA 5´-GATGACCCTGGTGATAGTGG NlaIII NlaIII NlaIII NlaIII NlaIII …CATG… …CGTG… ..AGVKD.. ..AGMKD... high-activity (3-4X) thermo-stable Low Dopamine Available low-activity (1X) thermo-labile More Dopamine Available The COMT Gene PROMOTER 5´ 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

16. 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

17. COMT Genotype Effects Executive Function n = 218 n = 181 n = 58 Genotype Effect (F=5.41, df= 2, 449); p<.004. Egan et al PNAS 2001

18. 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

19. 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

20. DNA DRD1 5’ 3’ -48 A - 5’ 3’ -48 G - Inherited genotype Clinicalandcognitive measures Goals To identify useable endophenotypes & targeted therapeutics Combine neuroimaging With behavioral and clinical measures and genetics Neuroimaging

21. Gene A Gene B + + + + + + + + + + + + + + + + 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

22. G72 / 13q DAAO / 12q MDAAO-5 M-22 p value=0.01 p value=0.01 p value=0.05 p value=0.05 106.4 Kb 120.7 Kb

23. 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 (30,000Genes) Plus validation with In situ hybridization WE Bunney

24. Computer analysis Probabilities of medication response and development of side-effects Neuroarray WWW: Analyze Image 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 ?

25. Aim 1: 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.

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

27. Sternberg task: Example Results 5 6 2 8 1 8 3 + + fMRI: Working Memory

28. Continuous Performance Task (CPT) Sustained attention Selective attention Motor control task PET results: Same as fMRI except no time course data + + 0 9 PET: Continuous Peformance Task

29. Structural MRI • Cortical thickness measures in mm • By defined region

30. Genetics

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

32. Cognitive Scores

33. 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

34. Anatomical Accuracy

35. Anatomical Accuracy

36. 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 visualiztion (Falco Kuester) • Supplement Slicer with multiple segmentation programs in addition to Freesurfer

37. 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. • Step 6. Validation of the methods by Core 3-2 on new set of subjects.

38. Identified 80 ROIs Relevant to DBP of Schizophrenia

39. 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.

40. 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.

41. Genetic Analysis in Combinatin 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.