AWT Ivo D. Dinov, Ph.D., CCB Chief Operations Officer PI: Arthur W. Toga, Ph.D. Co-PI: Tony F. Chan, Ph.D.

Core 2: Computational Tools Analysis Data Integration Knowledge Management Core 3: Driving Biological Projects Brain Development Aging & Dementia Multiple Sclerosis Schizophrenia Core 1: Computational Science Registration Shape Modeling Surface Modeling Segmentation Core 7: Administration & Management Committees, SIGs Science Advisory Board Meetings & Communication Progress & Monitoring Support Core 6: Dissemination Web Publications Education Database Core 5: Education & Training Courses Fellowships Workshops Training Materials Core 4: Infrastructure/Resources Computing Software Informatics CCB Overall Organization

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. CCB Major Objectives

CCB Grand Challenges Brain Mapping Challenges Software & Hardware Engineering Challenges Infrastructure & Communication Challenges Data Management Multidisciplinary Science Environment

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 )

Non-Affine Volumetric Registration Parametric & Implicit Modeling of Shape & Shape Analysis using Integral Invariants Conformal Mapping (on D 2 or S 2 ) Volumetric Image Segmentation Core 1 Specific Aims

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 Core 2: Computational Tools Research Categories

Data Visualization Additional functionality Is integrated via the extension architecture. Mutation Pathways Of HIV-1 Protease

Data Mediation

Grid Engine Integration

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 CCB – Driving Biological Projects (current) DBP 4: Correlating Neuroimaging, Phenotype and Genotype in Schizophrenia

DBP 5 (Jack van Horn, Dartmouth): Computational Mining Methods on fMRI Datasets of Cognitive Function DBP 7 (James Gee, U Penn): Shape Optimizing Diffeomorphisms for Atlas Creation DBP 6 (Srinka Ghosh & Tom Gingeras, Affymetrix): Maps of Transcription and Regulation of key Brain tissues in the Human Genome CCB – Driving Biological Projects ( pending! ) DBP 8 (Wojciech Makalowski, Penn State U): Alternative Splicing of Minor Classes of Eukaryotic Introns

Modeling - Brain Conformal Mapping 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 Last year’s groundbreaking publication* on conformal mapping as applied to brain surfaces initiated a novel technique for examining neuroscience data.

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. CCB Neuroimaging Applications: Brain Mapping of Disease CCB as featured in US News & World Report, 3/21/05

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. Neuroimaging Applications: Beyond the Brain into the Mind CCB as featured in National Geographic magazine, Mach 2005

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 Infrastructure (Core 4)

Coursework in imaging-based Computational Biology Graduate & undergrad training in Computational Biology Fellowship Program Visiting Scholars Program Workshops, Retreats & Tutorials Educational Materials CCB Education & Training (Core 5)

Timeline for Core 1: Computational Science SA 1-2: Modeling of Shape and Shape Analysis 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) Year 4 (10/07- 3/08) Year 4.5 (4/08- 9/08) Year 5 (10/08- 3/09) Year 5.5 (4/09- 9/09) Develop level set reps. for open curves/surfaces Test Cost functions for 2D Matching Test Cost functions for 3D Matching Ensure deformation mappings are diffeomorphic Test on 2D brain data Test on 3D brain data Add intensity information (Jensen divergence) Add intensity information (Jensen divergence) Formal Validation in 2D and 3D Use by DBPs and the rest of the world 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) Year 4 (10/07- 3/08) Year 4.5 (4/08- 9/08) Year 5 (10/08- 3/09) Year 5.5 (4/09- 9/09) SA: 1-1: Registration using Level Sets 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) Year 4 (10/07- 3/08) Year 4.5 (4/08- 9/08) Year 5 (10/08- 3/09) Year 5.5 (4/09- 9/09) Hippocampal Morphometry Studied with Brain Conformal Mapping Matching Landmarks 3D Paint Foliation and conformal Maps Shape Space Image Manifold Solving PDE on Surfaces with Conformal Structure SA: 1-3: Parametric & Implicit Surface Models SA: 1-4: Volumetric Image Segmentation Experimental evaluation of limitations of local and global shape representations 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 3-D Shape matching Dynamic shape signatures Classification of dynamic shapes Integration with other Cores

SA 1-2: Modeling of Shape and Shape Analysis 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) Year 4 (10/07-3/08) Year 4.5 (4/08-9/08) Year 5 (10/08-3/09) Year 5.5 (4/09-9/09) Combine level-set segmentation methods with atlas-based approaches to label neuro-anatomical structures. Extend tissue classification methods to process multiple modalities and identify pathologic structures Apply level set methods from Core I, Aim 4 to identify structures in MRI Extend methods to other modalities and specimens (mice) Validation – ongoing through duration of the project Image Segmentation Develop methods for parameterizing zero-genus surfaces Surface methods 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. Clinical Applications : Concurrent fMRI / DTI in surgical planning of tumor patients MS Alzheimer's EAE models (mouse) Develop a DTI phantom model for validation of DTI analysis algorithms DTI analysis Bio-sequence atlas tools Develop analysis tools and database technology for analyzing the role of alternative splicing in temporal development of neuronal tissue and disease states. Biosequence analysis 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) Year 4 (10/07-3/08) Year 4.5 (4/08-9/08) Year 5 (10/08-3/09) Year 5.5 (4/09-9/09) Extension architecture implementation Brain Graph BAMS interface Image Processing and Visualization Plugins Dev support, API help, End user documentation Networking API Library Grid engine integration SCIRun integration BIRN SRB integration Provenance integration Tools Ontology Neuroimaging Domain Ontology SQL integration Pipeline V.3 Public release Pipeline V.4 Public release CCB Pipeline Processing Environment Pipeline V.5 Public release Overlay network grid computing Natural language interface Enhanced user interface Connect LONI to ITK component model interface for LONI modules Compile CCB pipeline with SCIRun2 Connect LONI to SCIRun SCIRun Integration Shiva Integration with Pipeline Surface Models LONI viz Redesign LONI_Viz core - small/tight core plus a diverse & expandable plug-in infrastructure 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 Timeline for Core 2 : Computational Tools

Center for Computational Biology