Specific Aims – Aim 1

1. GENII: Grid-based Environmentfor Neuroimaging Integration and InteroperationHuman Brain Project – Neuroinformatics Proposal

2. Specific Aims – Aim 1 • Develop a grid-based computing environment for dynamic neuroimaging • We will apply state-of-the-art technology from the Open Grid Computing Environment (OGCE) consortium to build GENII • Grid-based domain-specific Environment for dynamic Neuroimaging of human brain activity with support for analysis and modeling tool Integration and Interoperation • Provide access to shared time-series data repositories, and analysis workflow creation and execution on a grid of computational and storage resources • GENII will be publicly-available open-source software designed to maintain both technological and operational consistency with the NIH Biomedical Informatics Research Network (BIRN)

3. Specific Aims – Aim 2 • Enable leading Matlab-based time-series neuroimaging tools to efficiently and conveniently access the GENII environment and benefit from the emerging grid computing infrastructure • We will develop mechanisms for Matlab to interface with GENII using the Java Virtual Machine (JVM) support inherent in current Matlab (R14) installations and the Java CoG and portlet technologies from the OGCE • Utilize this interface to make the widely used Matlab toolsets EEGLAB, BrainStorm, SPM, APECS, and FieldTrip able to access GENII computational, storage, and workflow services • GENII will mediate a high-level interaction between freely available (front-end) Matlab tools and a user’s (back-end) grid computing infrastructure

4. Specific Aims – Aim 3 • Build high-performance analysis and modeling components as GENII neuroimaging services • We will select and implement several essential time-intensive algorithms in signal processing, computational head modeling, image segmentation, and dipole source localization in high-performance sequential and parallel forms • The implementations will support scaling in data size, model resolution, and degree of parallelism • These programs will be wrapped as grid-enabled components to operate as prototypical computational services available within the GENII environment

5. Specific Aims – Aim 4 • Create computational workflows that capture complex, multi-step neuroanalysis processes • We will support processing of neurophysiological time-series data involving a series of analysis steps performed by multiple tools across multiple subjects over multiple experiments • Utilize emerging grid workflow programming technologies (e.g., XCAT and GridAnt) to create multi-component neuroimaging pipelines as high-level GENII services • The workflows can be reused with pluggable analysis components and composed with other workflow processes

6. Specific Aims – Aim 5 • Design and implement a storage schema for managing time-series neuroimaging information • Support storage and management of large, multi-dimensional, and multi-model neurophysiological data (raw, intermediate analyses, final results) • We will develop a robust schema for the storage of neuroimaging data and experimental information compatible with the SRB technology and incorporating a streamlined interface for GENII software access • A range of time-series data formats and types will be supported as well as tools for data conversion and correlation

7. Specific Aims – Neuroinformatics Software • The results of the project will be publicly-available open-source software for managing the storage and analysis of neuroinformatic experiment data and for leveraging the power of grid-accessible Unix-based clusters for high-performance computation • The GENII software will allow users to extend freely available Matlab toolsets with grid-enabled capabilities for workflow processing and shared storage

8. good spatial poor temporal Cortical Activity Knowledge Base Head Analysis Source Analysis Structural / Functional MRI/PET spatial pattern recognition temporal dynamics Cortical Activity Model Experiment subject Constraint Analysis IndividualBrain Analysis Component Response Model neural constraints Dense Array EEG / MEG temporal pattern recognition Signal Analysis Response Analysis Component Response Knowledge Base poor spatial good temporal neuroimaging integration Integrated Dynamic Brain Analysis Problem

9. Experimental Methodology and Tool Integration 16x256bits permillisec (30MB/m) CT / MRI segmentedtissues EEG NetStation BrainVoyager processed EEG mesh generation source localization constrained to cortical surface Interpolator 3D EMSE BESA

10. Experiment Tool Integration (Selected) • Geodesic Sensor Net (EGI) • EEGLAB (Swartz Center) • NetStation (EGI) • BrainStorm, USC/LANL

11. ICONIC Grid at University of Oregon graphics workstations interactive, immersive viz other campus clusters Internet 2 Gbit Campus Backbone CNI NIC NIC CIS CIS 4x8 16 16 2x8 2x16 SMP Server IBM p655 Shared Memory IBM p690 Graphics SMP SGI Prism Distributed Memory IBM JS20 Distributed Memory Dell Pentium Xeon TapeBackup SAN Storage SystemIBM SAN FS 5 Terabytes

12. ICONIC Grid – Hardware p690  16 processors p655  4 nodes  8 processors per node FibreChannel FibreChannel FAStT storage  5 TB SAN FS Dell cluster  16 nodes  2 processors per node JS20 Blade  16 nodes  2 processors per node

13. raw … … virtual services storage resources compute resources Computational Integrated Neuroimaging System

14. Leveraging Internet, HPC, and Grid Computing • Telemedicine imaging and neurology • Distributed EEG and MRI measurement and analysis • Neurological medical services • Shared brain data repositories • Remote and rural imaging capabilities • Enhanced HPC / grid infrastructure for neuroinformatics • Build on emerging web services and grid technology • Establish HPC resources with high-bandwidth networks • Further institutional and industry partnerships

15. Proposed Budget • Direct costs