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. A Collaborative Approach to Research Sheitman BB, Lieberman JA. J Psychiatr Res. 1998(May-Aug);32(3-4):143-150 Age (Years) Good Function Poor 15203040506070 Premorbid Progression Stable Relapsing Prodrome ? Improving First Episode To understand the time course of the disease – why first episode patients become chronically ill

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  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 1980s

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

13. FDG Metabolic Changes With Haloperidol By D 3 Alleles Gly-Gly Other Alleles UCI Brain Imaging Center

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

15. 127kb COMT-S START CODON COMT-MB START CODON TRANSMEMBRANE SEGMENT STOP CODON PROMOTER 5´5´ 22q11.22 22q11.23 CHROMOSOME 22 NlaIII 5´-GATGACCCTGGTGATAGTGG5´-CTCATCACCATCGAGATCAA 210 BP PCR … C A TG …..AG M KD... … C G TG ….. AG V KD.. high-activity (3-4X) thermo-stable Low Dopamine Available low-activity (1X) thermo-labile More Dopamine Available G 1947  A 1947  COMT-MB/S: Val 158/108  Met 158/108 SOURCE: NCBI, GEN-BANK, ACCESSION # Z26491 The COMT Gene

16. Dopamine terminals in striatum and in prefrontal cortex are not the same modified after: Sesack et al J. Neurosci 1998, Weinberger, ICOSR, 2003 Weinberger, ICOSR, 2003 Striatum Prefrontal cortex DA DA transporter DA receptor COMT NE transporter

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

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

21. 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 modelling gene^gene interactions – the Epistasis effect Gene AGene B + + + + + + + + + + + + + + + +

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

23. Strategies for Discovering Novel Candidate Genes & Drug Targets in Schizophrenia Candidates From Microarray Studies in Animals Drug Models (e.g., PCP, amphetamine) Treatment Models (e.g, neuroleptics) Knowledge of Pathophysiology of Neuronal Circuits Candidates From Neurotransmitter Systems Pharmacology of Disease Candidate Genes Candidates From Microarray Screens (30,000 Genes) Plus validation with In situ hybridization Microsatellite Surveys Identifying “Hotspots” & and Genes in ROI Candidates From Replicated Genome Wide WE Bunney

24. Computer analysis Neuroarray WWW: Analyze Image Probabilities of medication response and development of side-effects Efficacy Negative Cognitive DM Weight Suicide Clozapine 90 80 25 50 85 2 Asenapine90 80 50 10 15 ? Olanzapine80 70 20 70 90 4 Ziprasidone85 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. fMRI: Working Memory  Sternberg task:  Example Results 5 6 2 8 1 + 8 + 3

28. 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

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

30. Genetics 5HT2A (T102 C) DRD1(D deI) DRD2(B stNI) _141 DRD2(T aq1A ) DRD2_r s179 9978 DRD2_r s180 0498 DRD2_r s464 8317 50582 1 2 1 21 50591 21 2 1 2 1 50611 21 2 1 21 1 21 50641 2 2 1 50242 1 1 2 1 1 2 50282 1 1 21 50301 22 1 1 22 50341 2 1 1 2 50351 21 2 1 50371 2 2 1 1 21

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

32. 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)

33. Example Query of Federated Database PET & fMRI How can you predict which prodromal subject will develop first episode schizophrenia ? Integrated View Receptor Density ERP Web PubMed, Expasy Wrapper Structure Wrapper Clinical Wrapper Mediator

34. Aspects  Collaborations with Conte Centers: microarray  Circuitry  Visualization  Data mining and Bayesian approaches

35. Anatomical Accuracy

37.  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

38. 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.

39. Identified 80 ROIs Relevant to DBP of Schizophrenia

40. Anatomical Accuracy  Specified Operational Plan (cont.) –Step 6. Validation of the methods by Core 3-2 on new set of subjects. –Step 7. Core 3-2 data analyses to test hypotheses using methods developed in Steps 1-6. A similar set of steps will be implemented for the anterior cingulate cortex and the sub commissural areas.

41. 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.

42. 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.

43. 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.

44. Genetic Analysis in Combination with Imaging Data  Specified Operational Plan (cont.) –Step 7. The resulting models will be implemented by Core 2. –Step 8. Predictive models will be validated by Core 3-2 on a new set of subjects from Core 3- 1. –Step 9. Core 3-2 will perform data analyses to test hypotheses using algorithms and methods developed in Steps 8-8. –Step 10. Additional genes may be added to iteratively improve the models.