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?
