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NAMIC Core 3.2 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 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 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 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 ?? ?? Fallon’s PFC’s importance Implied Circuitry: visual attention and orienting 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 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 Goals 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 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 + + + + + + + + + +++++++ G72 / 13q DAAO / 12q 3 MDAAO-5 3 M-22 p value=0.01 p value=0.01 2 2 p value=0.05 p value=0.05 1 1 0 0 106.4 Kb Risk Odds Standard Model genotypes Ratio error 0.76 G72-DAAO M-22_AA 1.89 M-22_AG 1.82 0.72 MDAAO-5_TT 1.05 0.75 M-22_AA*/ MDAAO-5_TT 5.02 3.95 M-22_AG*/ MDAAO-5_TT 1.73 1.30 C.I. : confident interval ;O.R. : Odds Odd Ratio Ratio 120.7 Kb z 1.59 1.52 0.06 P>|z| 0.11 0.13 0.95 [95% C.I.forO.R] 0.86 - 4.14 0.84 - 3.95 0.26 - 4.24 2.05 0.04 1.08 - 23.45 0.73 0.47 0.40 - 7.52 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 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 ? Neuroarray WWW: Analyze Image 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. Legacy Dataset 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 Anatomical Accuracy 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 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. 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 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.