Slide 1: Ivo D. Dinov, Ph.D., CCB Chief Operations Officer PI: Arthur W. Toga, Ph.D. Co-PI: Tony F. Chan, Ph.D. AWT
Slide 2: CCB Overall Organization Core 1: Computational Science Registration Shape Modeling Surface Modeling Segmentation Core 2: Computational Tools Analysis Data Integration Knowledge Management Core 3: Driving Biological Projects Brain Development Aging & Dementia Multiple Sclerosis Schizophrenia Core 4: Infrastructure/Resources Computing Software Informatics Core 5: Education & Training Courses Fellowships Workshops Training Materials Core 6: Dissemination Web Publications Education Database Core 7: Administration & Management Committees, SIGs Science Advisory Board Meetings & Communication Progress & Monitoring Support
Slide 3: CCB Major Objectives • • Establish a new integrated multidisciplinary research center in computational neurobiology. Develop Atlases – sets of maps on different spheres of biological information that span many resolution-scales, 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. • •
Slide 4: CCB Grand Challenges • • • • • Brain Mapping Challenges Software & Hardware Engineering Challenges Infrastructure & Communication Challenges Data Management Multidisciplinary Science Environment
Slide 5: CCB Brain Mapping Challenges • • • • • • • • • • 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)
Slide 6: 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
Slide 7: Core 2: Computational Tools Research Categories • Data Analysis – – – – 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 • Knowledge Management – Analytic strategy validation
Slide 8: Data Visualization Mutation Pathways Of HIV-1 Protease Additional functionality Is integrated via the extension architecture.
Slide 9: Data Mediation
Slide 10: Grid Engine Integration
Slide 11: 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
Slide 12: 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 • • •
Slide 13: 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: • • • New algorithms for brain surface representation, cortical thickness and variation Updates on efficiency of conformal mapping techniques 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
Slide 14: 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.
Slide 15: 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.
Slide 16: 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 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-2: Application Deployment SA-3: Computational Research Support Provide technical support and expertise to enable collaborators to use the resources of the CCB
Slide 17: CCB Education & Training (Core 5) • • • • • • Coursework in imaging-based Computational Biology Graduate & undergrad training in Computational Biology Fellowship Program Visiting Scholars Program Workshops, Retreats & Tutorials Educational Materials
Slide 18: Timeline for Core 1: Computational Science SA: 1-1: Registration using Level Sets Year 1 (10/043/05) Year 1.5 Year 2 Year 2.5 (4/05(10/05(4/06Develop level set reps. for 9/05) open curves/surfaces 3/06) 9/06) 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) SA 1-2: Modeling of Shape and Shape Analysis Year 1 Year 1.5 Year 2 Experimental evaluation of limitations of (10/04(4/05(10/05(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 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) Test Cost functions for 2D Matching Ensure deformation mappings are diffeomorphic Test on 2D brain data Test Cost functions for 3D Matching Kernel shape statistics with local priors Shape representation: hierarchy and compositionality. Convergence of local/global representations 3-D Shape descriptors and integral invariants 3-D Shape matching Test on 3D brain data Add intensity information (Jensen divergence) Formal Validation in 2D and 3D Use by DBPs and the rest of the world Dynamic shape signatures 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 Matching Landmarks 3D Paint Foliation and conformal Maps Shape Space Image Manifold Solving PDE on Surfaces with Conformal Structure 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 (10/04(4/05(10/05- Multi-Layer Level Sets to measurement of (4/06(10/06(4/07(10/07(4/08Extend Develop Multi-Layer Extend Multi-Layer Level cortical thickness - Local modification of Level Sets for 2D MRISets to Volumetric 3/06) Data (3D) 3/05) 9/05) 9/06) 3/07) Extend to topology preserving multi-layer level sets 9/07) 3/08) 9/08) 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 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 image segmentation 3-D 5-1 6-1 6-2 6-3 6-4 Detect pathology in one of the modalities Perform a detailed Integrate with multi-layer Local modification of the analysis of the Level Sets to enable image forces to improve sensitivity of the segmentation of cortical segmentation accuracy algorithmthe initial thickness Apply to 3-D Charged Fluid to Adapt the particle size parameter selection volumetric 3-D vascular image to object and boundary segmentation MR, CT and 3DRA) characteristics 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) Year 5 Year 5.5 (10/08- pediatric (4/09Validate on and adult 3/09) data sets 9/09) Collect appropriate multi-sequence data sets and validate algorithms Validate the algorithm across diverse data sets Validate the algorithm across diverse data sets (MR, CT and 3DRA)
Slide 19: Timeline for Core 2 : Computational Tools Year 1 Year 1.5 Extend tissue (10/04-3/05) classification methods to (4/05-9/05) process multiple modalities and identify pathologic structures Apply 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. 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 a fluid-model Develop DTI data. approach to fiber tract segmentation in DTI. 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) (4/09-9/09) SA 1-2: (10/07-3/08) Modeling of(4/08-9/08) and(10/08-3/09) Analysis Shape Shape Year 4 Year 4.5 Year 5 Year 5.5 Image Segmentation Surface methods DTI analysis Develop analysis tools and database technology for analyzing the role of alternative splicing in temporal development of neuronal tissue and disease states. Develop a DTI phantom model for validation of DTI Clinical Applications : Concurrent fMRI / DTI analysis algorithms in surgical planning of tumor patients MS Alzheimer's EAE models (mouse) Bio-sequence atlas tools Biosequence analysis Year 4 (10/07-3/08) SQL integration Provenance integration Year 1 (10/04-3/05) Extension architecture implementation Compile CCB pipeline with SCIRun2 Year 1.5 (4/05-9/05) Grid engine integration CCB Pipeline Processing Environment Pipeline V.3 Public release Pipeline V.4 Public release Networking API Library Connect LONI to SCIRun SCIRun integration Connect LONI to ITK Enhanced user interface 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) Year 4.5 (4/08-9/08) Year 5 (10/08-3/09) Year 5.5 (4/09-9/09) Pipeline V.5 Public release Tools Ontology Neuroimaging Domain Ontology Natural language interface Overlay network grid computing Brain Graph BAMS interface Integration with Surface Models Pipeline Image Processing and Visualization Plugins Dev support, API help, End user documentation BIRN SRB integration component model interface for LONI modules Shiva CompAtlas Computational Atlas Kernel SCIRun Integration 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 Database Tools
Slide 20: Center for Computational Biology