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NAMIC Core 3.2

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

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NAMIC Core 3.2

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  1. NAMIC Core 3.2 Steven Potkin - UCI James Kennedy – U of Toronto

  2. 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

  3. 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

  4. 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

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

  6. Average canonical “anatomical” DLPFC in the group “Physiological” DLPFC In normal subject for one “DLPFC Task” “Physiological” DLPFC In sz subject for one “DLPFC Task” Actual site of “anatomical” DLPFC in this subject COMT effects Non-COMT effects

  7. Paracingulate/ precuneus 18d,19d-V2-3,V6 SMA pulvinar tectum 17/V1 precuneus tectum 18d,19d-V2-3,V6 pulvinar mesopontine reticular formation

  8. Implied circuitry- retinal/meso-tectal-pulvinar-prestriate-precuneus-SMA Potentially an arousal related visual posterior attention/orienting pathway

  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. Transdisciplinary Imaging Genetics Center Synergies With NAMIC DNA DRD1 5’ 3’ -48 A - 5’ 3’ -48 G - Inherited genotype Clinicalandcognitive measures To identify useable endophenotypes & targeted therapeutics Combine neuroimaging With behavioral and clinical measures and genetics Neuroimaging

  20. 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

  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. 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

  23. 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 ?

  24. 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.

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

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

  27. 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

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

  29. Genetics

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

  31. Cognitive Scores

  32. 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

  33. 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

  34. 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.

  35. Identified 80 ROIs Relevant to DBP of Schizophrenia

  36. 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.

  37. 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.

  38. 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.

  39. 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

  40. How do genes determine brain characteristics?

  41. Gene Variants Molecular Genetic Approach Pharmacogenetics Gene Expression Pharmacology Neurobiology Phenotype Endophenotype -Psychophysiology; Neuroimaging Sub-pheno

  42. Cytoarchitectural abnormalities Control 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 Schizophrenia

  43. Will the Brain Derived Neurotrophic Factor (BDNF) Gene Predict Grey Matter Volume? BDNF-1 SNP BDNF-2 BDNF-3 BDNF-4 Exon 11 Val-66-met (GT)n repeat(function? mRNA stability)

  44. 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.

  45. Haplotype TDT: BDNF (GT)n repeat & val66met in schizophrenia * * HTDT for 170-val66 c2 = 7.11; 1 df; p = 0.007 Muglia et al, (2002)

  46. 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).

  47. 3) TaqIB 1) -241 A/G 4) C957T 6 8 2 7 1 3 4 5 5) TaqIA 2) –141 Ins/Del Dopamine D2 Receptor: 5 Genetic Markers Studied

  48. Dopamine D2 Gene LD: Potkin new SCZ sample (N=28) • Linkage Disequilibrium map (Haploview) • 5 markers across the DRD2 gene

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