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NAMIC Core 3. 2 NAMIC Core 3. 2

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

Opportunity & Challenges u u Schizophrenia as the exemplar: Heterogeneous symptoms and course; Heritable; Opportunity & Challenges u u 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 u Time course of the disease u The challenges Standard but subjective diagnostic assessments u Time course of the disease u – Unclear relationship between clinical profiles, genotype, and disease progression – Multiple genes involved – Multiple internal/external influences u Multiple levels of study, from molecular to behavioral

A Collaborative Approach to Research To understand the time course of the disease – 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 Improving Progression 30 40 50 60 70 Age (Years) Sheitman BB, Lieberman JA. J Psychiatr Res. 1998(May-Aug); 32(3 -4): 143 -150

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

u Fallon’s PFC’s importance u Fallon’s PFC’s importance

SMA precuneus 18 d, 19 d-V 2 -3, V 6 pulvinar tectum 17/V 1 SMA precuneus 18 d, 19 d-V 2 -3, V 6 pulvinar tectum 17/V 1 precuneus tectum 18 d, 19 d-V 2 -3, V 6 mesopontine reticular formation pulvinar

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

Clozapine: The First Atypical Antipsychotic u Efficacy 1980 s – Reduction of positive and Clozapine: The First Atypical Antipsychotic u Efficacy 1980 s – Reduction of positive and negative symptoms – Improvements treatment refractory patient – Reduction of suicidality in SA & schizo. patients u Side effects – – – u low EPS, TD risk of agranulocytosis risk of respiratory/cardiac arrest & myopathy moderate-to-high weight gain potential for seizures Receptor binding – Lowest D 2 affinity – Highest D 1 affinity

Potkin et al , 2003 Potkin et al , 2003

Clozapine Challenges Dogma u The EPS associated with conventional antipsychotics led to the misconception Clozapine Challenges Dogma u The EPS associated with conventional antipsychotics led to the misconception that EPS were required for an antipsychotic u Clozapine’s lack of EPS established that EPS are not a necessary for a therapeutic response

19 AIMS Scores for DRD 3 Msc I Polymorphism after Typical Neuroleptic Treatment Corrected 19 AIMS Scores for DRD 3 Msc I Polymorphism after Typical Neuroleptic Treatment Corrected Mean AIMS score 1, 1 1, 2 2, 2 n=34 n=53 n=25 DRD 3 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 D 3 Alleles Gly-Gly UCI Brain Imaging Center FDG Metabolic Changes With Haloperidol By D 3 Alleles Gly-Gly Other Alleles

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

The COMT Gene CHROMOSOME 22 22 q 11. 23 1 22 q 11. 22 The COMT Gene CHROMOSOME 22 22 q 11. 23 1 22 q 11. 22 27 kb PROMOTER 5´ COMT-MB START CODON TRANSMEMBRANE SEGMENT COMT-S START CODON 210 BP 5´-CTCATCACCATCGAGATCAA Nla. III 5´-GATGACCCTGGTGATAGTGG Nla. III G 1947 A 1947 …CGTG… . . AGVKD. . STOP CODON PCR COMT-MB/S: Nla. III …CATG… Val 158/108 Met 158/108 . . AGMKD. . . high-activity (3 -4 X) low-activity (1 X) thermo-stable Low Dopamine Available thermo-labile More Dopamine Available SOURCE: NCBI, GEN-BANK, ACCESSION # Z 26491 Nla. III

Dopamine terminals in striatum and in prefrontal cortex are not the same Striatum DA 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 1998 199 Weinberger, ICOSR, 2003

COMT Genotype Effects Executive Function n = 218 n = 181 n = 58 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

COMT Genotype and Cortical Efficiency During f. MRI Working Memory Task Val-val>val-met>met-met use more COMT Genotype and Cortical Efficiency During f. MRI 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 u Combinations of – Imaging measures (s. MRI, FMRI, PET, EEG) – Genotypes Proto-endophenotypes u Combinations of – Imaging measures (s. MRI, FMRI, PET, EEG) – Genotypes – Clinical profiles – Treatment response – Cognitive behavior Iterative refinements to develop endophenotypes u Studies like these represent a wealth of potential information ---if they can be combined u

Goals Combine neuroimaging DNA With behavioral and clinical measures and genetics To identify useable Goals Combine neuroimaging DNA With behavioral and clinical measures and genetics To identify useable endophenotypes & targete therapeutics DRD 1 5’ - 3’ -48 A 3’ -48 G Inherited genotype Neuroimaging Clinical and cognitive measures

How many genes are needed for one disease ? u In complex traits, genes How many genes are needed for one disease ? u 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 + + + + +++++++

G 72 / 13 q DAAO / 12 q MDAAO-5 M-22 p value=0. 01 G 72 / 13 q DAAO / 12 q MDAAO-5 M-22 p value=0. 01 p value=0. 05 106. 4 Kb p value=0. 05 120. 7 Kb

Strategies for Discovering Novel Candidate Genes & Drug Targets in Schizophrenia Candidates From Replicated Strategies for Discovering Novel Candidate Genes & Drug Targets in Schizophrenia Candidates From Replicated Genome Wide Microsatellite Surveys Identifying “Hotspots” & and Genes in ROI Candidates From Microarray Screens (30, 000 Genes) Plus validation with In situ hybridization WE Bunney Candidate Genes 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 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 u u The First International Imaging Genetics Conference was Aim 1: Imaging Genetics Conference u u 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 u f. MRI u PET u Structural MRI u Genetic - SNP Legacy Dataset u f. MRI u PET u Structural MRI u Genetic - SNP u Clinical measures u Cognitive measures u EEG – 28 subjects, chronic Sz

f. MRI: Working Memory u Sternberg task: 5 6 2 8 1 + 8 f. MRI: Working Memory u Sternberg task: 5 6 2 8 1 + 8 + 3 u Example Results

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

Structural MRI u Cortical thickness measures in mm u By defined region Structural MRI u Cortical thickness measures in mm u By defined region

Genetics 5 HT 2 A (T 102 C) DRD 2(B DRD 2(T DRD 2_r Genetics 5 HT 2 A (T 102 C) DRD 2(B DRD 2(T DRD 2_r DRD 1(D st. NI) aq 1 A s 179 _141 ) 9978 de. I) DRD 2_r s 180 0498 DRD 2_r s 464 8317 5058 2 2 1 1 1 1 5059 1 2 1 1 2 2 1 1 5061 1 2 2 1 1 1 2 1 1 5064 1 2 2 2 1 1 1 1 5024 2 2 1 1 1 2 1 2 5028 2 2 2 1 1 1 2 1 1 5030 1 2 2 2 1 1 1 2 2 2 5034 1 2 1 2 1 1 1 2 5035 1 2 1 1 2 2 1 1 1 5037 1 2 2 2 1 1 1 2 1 1

Clinical Scores u PANSS – Thirteen subscales/factors – Positive, negative, and global summary scores Clinical Scores u 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) 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 Example Query of Federated Database How can you predict which prodromal subject will develop first episode schizophrenia ? Integrated View Mediator Wrapper Wrapper PET & f. MRI Structure Receptor Density ERP Clinical Web Pub. Med, Expasy

Anatomical Accuracy Anatomical Accuracy

Anatomical Accuracy Anatomical Accuracy

Anatomical Accuracy u Operational Plan (Fallon led effort) – Step 1. Core 3 -2 Anatomical Accuracy u 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 u Gliches in Freesurfer, Slicer must be overcome and features added eg subcortical white matter segmentation for tractography u Extend to visualiztion (Falco Kuester) u Supplement Slicer with multiple segmentation programs in addition to Freesurfer u

Anatomical Accuracy u Specified Operational Plan – Step 3. Core 1 will develop algorithms Anatomical Accuracy u 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 Identified 80 ROIs Relevant to DBP of Schizophrenia

Circuitry Analysis u Specified Operational Plan – Step 1. Core 3 -2 will collaborate Circuitry Analysis u 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. u 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 u Specified Operational Plan – Step 1. Genetic Analysis in Combination with Imaging Data u 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 v 2. 0 software for measuring two 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 2 LD and construct haplotypes.

Genetic Analysis in Combinatin with Imaging Data u Specified Operational Plan (cont. ) – Genetic Analysis in Combinatin with Imaging Data u 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 interaction using refinement of Toronto Phase v 2. 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.