Namic core 2 Steven Potkin uci

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

• 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

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

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

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 after Typical Neuroleptic Treatment

Negative Symptom Schizophrenia

Dopamine terminals in striatum and in prefrontal cortex are not the same

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

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

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-UCI 28

• fMRI

• PET

• Structural MRI

• Genetic - SNP

• Clinical measures

• Cognitive measures

• EEG

• 28 subjects, chronic Sz

fMRI: Working Memory

• Sternberg task:

PET: Continuous Peformance Task

• Continuous Performance Task (CPT)

• Sustained attention
• Selective attention
• Motor control task

Structural MRI

• Cortical thickness measures in mm

• By defined region

Genetics

Clinical Scores

• PANSS

• Thirteen subscales/factors
• Positive, negative, and global summary scores
• Lindenmayer 5-factors summary
• Marder 5-factors summary

Cognitive Scores

Example Query of Federated Database

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

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.

Identified 80 ROIs Relevant to DBP of Schizophrenia

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.

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.

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.

Molecular Genetic Approach

Cytoarchitectural abnormalities

Will the Brain Derived Neurotrophic Factor (BDNF) Gene Predict Grey Matter Volume?

BDNF val66met: MRI functional brain imaging (Egan et al, Cell 2003)

Dopamine D2 Receptor: 5 Genetic Markers Studied

Dopamine D2 Gene LD: Potkin new SCZ sample (N=28)

DRD2 Schiz Responder/Non-Resp. (chi2) Potkin N=48

DRD2 Quantitative Data: Total BPRS (ANCOVA) Potkin N-48

D2 TaqIA Genotypes vs. total BPRS response score (p = 0.035) Potkin N=48

D2 TaqIA vs. Positive Symptoms (ANCOVA; p = 0.07) Potkin N=48

Migrating Window DRD2 Haplotype Analysis (COCAPhase) Potkin N=48

Individual D2 Haplotype Tests Within Window 1-2-3 (global p = 0.019; COCAPhase; Potkin N=48)

SNAP-25 Gene Marker LD Potkin new sample N=28

SNAP-25 Gene vs Schizophrenia Potkin N=28 Cases versus controls (chi-sq)

Gene-Gene Interactions in Schizophrenia: First Steps

• M Lanktree, J Grigull, D Mueller, P Muglia, FM Macciardi, JL Kennedy

TDT

C-TDT Results D4 & D1

Will MOG gene variants predict white matter abnormalities?

UNC

Complexities in Genetics & Neuroimaging

• Genetic variants express themselves in many ways – singularly, or combined (haplotypes, epistasis, partial penetrance…)

• What are the appropriate phenotypes to use from brain imaging data?

• How to control massive multiple testing of genome scan x brain voxels (millions x millions)?

Summary

• D2 role in schizophrenia and clozapine response?

• SNAP-25 gene involved in Schizophrenia and neurodevelopment?

• BDNF gene candidate for grey matter measures?

• MOG gene candidate for white matter?

• Vast expanses of quality data await us: we only need to develop our informatics sophistication…

• National Alliance for Medical Imaging and Computing:

• NAMIC

• www.na-mic.org