Ivo D. Dinov, Ph.D.,
CCB Chief Operations Officer
PI: Arthur W. Toga, Ph.D.
Co-PI: Tony F. Chan, Ph.D.
AWT
CCB Overall Organization
Core 1: Computational Science
Registration
Shape Modeling
Surface Modeling
Segmentation
Core 4: Infrastructure/Resources
Computing
Software
Informatics
Core 2: Computational Tools
Analysis
Data Integration
Knowledge Management
Core 5: Education & Training
Courses
Fellowships
Workshops
Training Materials
Core 7: Administration & Management
Committees, SIGs
Science Advisory Board
Meetings & Communication
Progress & Monitoring
Support
Core 3: Driving Biological Projects
Brain Development
Aging & Dementia
Multiple Sclerosis
Schizophrenia
Core 6: Dissemination
Web
Publications
Education
Database
CCB Major Objectives
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Establish a new integrated multidisciplinary research
center in computational neurobiology.
Develop Atlases – sets of maps on different spheres
of biological information that span many resolutionscales, image-modalities, species, genotypes &
phenotypes.
Introduce new mathematical symbolic representations
of biological information across space & time.
Develop, implement and test computational
tools that are applicable across different
biological systems & atlases.
CCB Grand Challenges
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Brain Mapping Challenges
Software & Hardware Engineering
Challenges
Infrastructure & Communication
Challenges
Data Management
Multidisciplinary Science Environment
CCB Brain Mapping Challenges
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Quantitative analysis of structural & functional data
Merging NeuroImaging and Clinical data (e.g., NPI)
NeuroImaging markers associated with Gender,
Race, Disease, Age, Socioeconomics, Drug effects
NeuroImaging Interactions w/ Genotype-Phenotype
Understanding Temporal Changes in the Brain
Data Management (volume, complexity, sharing, HIPAA)
NeuroImaging Across Species (similarities and diff)
Integrating Multimodal Brain Imaging Data
Efficient and Robust Neurocomputation (Grid)
SW & Tool Development and Management (Pipeline)
Core 1 Specific Aims
• Non-Affine Volumetric Registration
• Parametric & Implicit Modeling of Shape &
Shape Analysis using Integral Invariants
• Conformal Mapping (on D2 or S2)
• Volumetric Image Segmentation
Core 2: Computational Tools
Research Categories
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Data Analysis
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Volumetric segmentation
Surface analyses
DTI Analysis (tractography)
Biosequence analysis
Interaction
– Grid Pipeline Environment
– SCIRun/Pipeline integration
– New tools for integrating, managing,
modeling, and visualizing data
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Knowledge Management
– Analytic strategy validation
Data Visualization
Mutation
Pathways
Of
HIV-1
Protease
Additional functionality
Is integrated via the
extension architecture.
Data Mediation
Grid Engine Integration
CCB – Driving Biological Projects (current)
DBP 1:
Mapping Language Development Longitudinally
DBP 2:
Mapping Structural and Functional Changes in Aging and Dementia
DBP 3:
Multiple Sclerosis and Experimental Autoimmune Encephalomyelitis
DBP 4:
Correlating Neuroimaging, Phenotype and Genotype in Schizophrenia
CCB – Driving Biological Projects (pending!)
DBP 5 (Jack van Horn, Dartmouth):
Computational Mining Methods on fMRI Datasets of Cognitive Function
DBP 6 (Srinka Ghosh & Tom Gingeras, Affymetrix):
Maps of Transcription and Regulation of key Brain tissues in the Human Genome
DBP 7 (James Gee, U Penn):
Shape Optimizing Diffeomorphisms for Atlas Creation
DBP 8 (Wojciech Makalowski, Penn State U):
Alternative Splicing of Minor Classes of Eukaryotic Introns
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Modeling - Brain Conformal Mapping
Last year’s groundbreaking publication* on conformal mapping as applied to
brain surfaces initiated a novel technique for examining neuroscience data.
The continuing CCB developments since publication include:
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New algorithms for brain surface representation, cortical thickness and variation
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Updates on efficiency of conformal mapping techniques
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Better synergy of multi-disciplinary resources
*Genus Zero Surface Conformal Mapping and Its Application to Brain Surface Mapping
Xianfeng Gu, Yalin Wang, Tony F. Chan, Paul M. Thompson and Shing-Tung Yau
IEEE Transactions on Medical Imaging, 2004, Volume 23, Number 8
CCB Neuroimaging Applications:
Brain Mapping of Disease
CCB as featured in US News & World Report, 3/21/05
http://www.usnews.com/usnews/health/articles/050321/21brain.htm
Mapping Schizophrenia
Mapping temporal structural changes
Schizophrenia. The disease causes a mix of
hallucinations and psychotic behavior in
teenagers. Abnormalities in schizophrenics first
cropped up in the parietal lobe.
Drug effects on the Brain
Differences of antipsychotic drug effects.
Alzheimer’s Disease
Mapping
Temporal anatomical alterations in Alzheimer’s
disease. Gray matter loss starts in the
hippocampus, a memory area, and quickly
moves to the limbic system, which is involved
in emotions.
Neuroimaging Applications:
Beyond the Brain into the Mind
CCB as featured in National Geographic magazine, Mach 2005
http://magma.nationalgeographic.com/ngm/0503/feature1/index.html
CCB reaches 5 million readers via
National Geographic and shares
neuroscience research with the public.
The CCB receives many requests from
doctors and teachers interested in
using these models as teaching
devices.
CCB’s 3D models show fMRI activity
in the visual system, fear, meditation,
navigation, musical pitch, object
permanence, plasticity, autism and
hypergraphia.
CCB Infrastructure (Core 4)
SA-1: Computing Infrastructure
Develop, implement and maintain the
computing resources and network services
required for computationally intensive
science performed in the CCB
SA-2: Application Deployment
Integrate the algorithms, techniques and tools developed
in Cores 1 & 2 with the Computing Infrastructure to
enable researchers to remotely access and use the
computing resources of the CCB
SA-3: Computational Research Support
Provide technical support and expertise to enable
collaborators to use the resources of the CCB
CCB Education & Training (Core 5)
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Coursework in imaging-based
Computational Biology
Graduate & undergrad training
in Computational Biology
Fellowship Program
Visiting Scholars Program
Workshops, Retreats & Tutorials
Educational Materials
Timeline for Core 1:
Computational Science
SA 1-2: Modeling of Shape and Shape Analysis
SA: 1-1: Registration using Level Sets
Year 1
(10/043/05)
Year 1.5 Year 2 Year 2.5
(4/05-Develop(10/05level set reps.(4/06for open curves/surfaces
9/05)
3/06)
9/06)
Year 3
(10/063/07)
Year 3.5
(4/079/07)
Test Cost functions for
2D Matching
Ensure deformation mappings
are diffeomorphic
Year 4
(10/073/08)
Year 4.5
(4/089/08)
Year 5
(10/083/09)
Year 1 Year 1.5 Year 2
Year 5.5
(4/099/09)
Test Cost functions for
3D Matching
Test on 2D
brain data
Year 2.5 Year 3 Year 3.5 Year 4 Year 4.5 Year 5 Year 5.5
(10/06(4/07(10/07(4/08(10/08(4/093/07)
9/07)
3/08)
9/08)
3/09)
9/09)
Experimental
(10/04- evaluation
(4/05-of limitations
(10/05-of (4/06local and global shape representations
3/05)
9/05)
3/06)
9/06)
Shape matching based
on local descriptors
Shape matching based
on global deformations
Kernel shape statistics with local priors
Shape representation: hierarchy and compositionality.
Convergence of local/global representations
3-D Shape descriptors
and integral invariants
Test on 3D brain data
3-D Shape matching
Add intensity information (Jensen divergence)
Formal Validation
in 2D and 3D
Dynamic shape signatures
Use by DBPs and
the rest of the world
Classification of dynamic shapes
Integration with
other Cores
SA: 1-3: Parametric & Implicit Surface Models
Year 1
Year 1.5 Year 2
(10/04(4/05(10/05Hippocampal Morphometry Studied
3/05)
9/05)
3/06)
with Brain Conformal Mapping
Year 2.5
(4/069/06)
Year 3
(10/063/07)
Year 3.5
(4/079/07)
Year 4
(10/073/08)
Year 4.5
(4/089/08)
Year 5
(10/083/09)
Year 5.5
(4/099/09)
Matching Landmarks
SA: 1-4: Volumetric Image Segmentation
Year 1 Year 1.5 Year 2 Year 2.5 Year 3 Year 3.5 Year 4 Year 4.5 Year 5 Year 5.5
(10/04(4/05(10/05(4/06(10/07(4/08(10/08(4/09Extend Multi-Layer
Level Sets(10/06to measurement (4/07of
Develop Multi-Layer Extend Multi-Layer Level cortical thickness - Local modification of
Validate on pediatric
to topology preserving multi-layer level sets
Level3/05)
Sets for 2D MRISets
to Volumetric 3/06)
Data (3D)
and adult data sets
9/05)
9/06)
3/07) Extend9/07)
3/08)
9/08)
3/09)
9/09)
Image forces to improve overall segmentation
Apply to multi-channel segmentation
Develop Logic Models using Level Sets
Extend to un-registered images
of different modalities of MRI data
3D Paint
Improve auto- Extend algorithm
matic initializationfrom 2D to true 3D
Extend 2-D Apply 3-D Charged
Charged Fluid Fluid to volumetric
simulation to 3-D
3-D image segmentation
Foliation and conformal Maps
segmentation MR, CT and 3DRA)
Shape Space
Detect pathology in Collect appropriate multi-sequence
one of the modalities data sets and validate algorithms
Perform a detailed
Local modification of the
Integrate with multi-layer
analysis of the
image forces to improve
Level Sets to enable
sensitivity of the
segmentation accuracy
segmentation of cortical
algorithm
to 3-D
initial
Apply the
Charged Fluid to
Adapt the particle
size
thickness
parameter
selection
volumetric
3-D vascular image
to object and boundary
characteristics
Validate the
algorithm across
diverse data sets
Validate the algorithm
across diverse data sets
(MR, CT and 3DRA)
5-1
Image Manifold
6-1
Solving PDE on Surfaces
with Conformal Structure
6-2
6-3
6-4
Timeline for Core 2 :
Computational Tools
Year 1
Year 1.5
Extend tissue classification
methods to
(10/04-3/05)
(4/05-9/05)
Year 2
(10/05-3/06)
process multiple modalities and
identify
structures
Applypathologic
level set methods
from Core I, Aim 4
to identify structures in MRI
Combine level-set segmentation methods
with atlas-based approaches to label neuro-anatomical structures.
Year 2.5
(4/06-9/06)
Year 3
(10/06-3/07)
Year 3.5
(4/07-9/07)
Year 4
Year 4.5
Year 5
Year 5.5
(4/09-9/09)
SA 1-2: (10/07-3/08)
Modeling of(4/08-9/08)
Shape and(10/08-3/09)
Shape Analysis
Image Segmentation
Extend methods to other modalities and specimens (mice)
Validation – ongoing through duration of the project
Develop methods
for parameterizing
zero-genus
surfaces
P-harmonic
method
validation
Application of
parameterization
from Core I
Develop novel approaches
for labeling cortical landmarks
Develop tools for computing
various measures
from DTI
data.
Develop
a fluid-model
approach to fiber tract
segmentation in DTI.
Surface methods
Develop a DTI phantom
model for validation of
DTI analysis algorithms
Clinical Applications : Concurrent fMRI / DTI
DTI analysis
Biosequence analysis
in surgical planning of tumor patients
MS Alzheimer's EAE models (mouse)
Develop analysis tools and database technology
for analyzing the role of alternative splicing in temporal
development of neuronal tissue and disease states.
Bio-sequence
atlas tools
Year 1
(10/04-3/05)
Year 1.5
(4/05-9/05)
Year 2
(10/05-3/06)
Year 2.5
(4/06-9/06)
Year 3
(10/06-3/07)
Year 3.5
(4/07-9/07)
CCB Pipeline Processing Environment
Extension
architecture
implementation
Compile CCB
pipeline with
SCIRun2
Brain Graph
BAMS interface
Grid engine
integration
BIRN SRB
integration
component model
interface for
LONI modules
Pipeline V.3
Public release
SQL
integration
Pipeline V.4
Public release
Networking
API Library
Connect
LONI to SCIRun
SCIRun
integration
Connect
LONI to ITK
Integration with
Surface Models
Pipeline
Image Processing and Visualization Plugins
Dev support, API help, End user documentation
Year 4
(10/07-3/08)
Provenance
integration
Enhanced
user interface
Year 4.5
(4/08-9/08)
Year 5
(10/08-3/09)
Tools Ontology
Neuroimaging Domain Ontology
Natural language
interface
Overlay network
grid computing
SCIRun Integration
Shiva
Redesign LONI_Viz core - small/tight core plus a diverse & expandable plug-in infrastructure
LONI viz
Tool integration - LONI_Viz, SHIVA, Pipeline, SCIRun, Slicer
Biospeak for Comp. Biology
BLASTgres extension
BioPostgres 1D/2D/3D atlas
ConDuit Useful container lib
CompAtlas
Computational Atlas Kernel
Database Tools
Year 5.5
(4/09-9/09)
Pipeline V.5
Public release
Center for Computational Biology