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Neuroinformatics, the ICONIC Grid, and GEMINI

Neuroinformatics, the ICONIC Grid, and GEMINI. Allen D. Malony University of Oregon. Professor Department of Computer and Information Science. Director NeuroInformatics Center Computational Science Institute. UO Brain, Biology, and Machine Initiative.

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Neuroinformatics, the ICONIC Grid, and GEMINI

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  1. Neuroinformatics, the ICONIC Grid, and GEMINI Allen D. Malony University of Oregon Professor Department of Computerand Information Science Director NeuroInformatics Center Computational Science Institute

  2. UO Brain, Biology, and Machine Initiative • University of Oregon interdisciplinary research in cognitive neuroscience, biology, computer science • Human neuroscience focus • Understanding of cognition and behavior • Relation to anatomy and neural mechanisms • Linking with molecular analysis and genetics • Enhancement and integration of neuroimaging facilities • Magnetic Resonance Imaging (MRI) systems • Dense-array EEG system • Computation clusters for high-end analysis • Establish and support UO institutional centers

  3. NeuroInformatics Center (NIC) at UO • Application of computational science methods to human neuroscience problems • Tools to help understand dynamic brain function • Tools to help diagnosis brain-related disorders • HPC simulation, large-scale data analysis, visualization • Integration of neuroimaging methods and technology • Need for coupled modeling (EEG/ERP, MR analysis) • Apply advanced statistical signal analysis (PCA, ICA) • Develop computational brain models (FDM, FEM) • Build source localization models (dipole, linear inverse) • Optimize temporal and spatial resolution • Internet-based capabilities for brain analysis services, data archiving, and data mining

  4. Neuroinformatics for Brainwave Research • Electroencephalogram (EEG) • EEG time series analysis • Event-related potentials (ERP) • average to increase SNR • link brain activity to sensory–motor, cognitive functions • Signal cleaning and decomposition • Neural source localization

  5. EEG Dense-Array Methodology • Electrical Geodesics, Inc. 256 channels

  6. Dipole Sources in the Cortex • Scalp EEG is generated in the cortex • Interested in dipole location, orientation, and magnitude • Cortical sheet gives possible dipole locations • Orientation is normal to cortical surface • Need to capture convoluted geometry in 3D mesh • From segmented MRI/CT • Linear superposition

  7. Building Computational Brain Models • MRI segmentation of brain tissues • Conductivity model • Measure head tissue conductivity • Electrical impedance tomography • small currents are injectedbetween electrode pair • resulting potential measuredat remaining electrodes • Finite element forward solution • Source inverse modeling • Explicit and implicit methods • Bayesian methodology

  8. Computational Implementation on ICONIC Grid • Designed as a conductivity search problem • Master launches new inverse problems with guesses • Inverse solvers run iterativeforward calculations • Parallelization • Search • Inverse solve • Hybrid A. Salman, S. Turovets, A. Malony, J. Eriksen, D. Tucker, “Computational Modeling of Human Head Conductivity,” ICCS 2005, May 2005. (awarded best paper)

  9. UO ICONIC Grid • NSF Major Research Instrumentation (MRI) proposal • “Acquisition of the Oregon ICONIC Grid for Integrated COgnitive Neuroscience Informatics and Computation” • PIs • Computer Science: A. Malony, J. Conery • Psychology: D. Tucker, M. Posner, R. Nunnally • Senior personnel • Computer Science: S. Douglas, J. Cuny • Psychology: H. Neville, E. Awh, P. White • Computational, storage, and visualization infrastructure

  10. 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 112 total processors TapeBackup Shared Storage System 40 Terabytes ICONIC Grid

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

  12. GEMINI Project • “GEMINI: Grid-based Electromagnetic Integrated Neuroimaging” • NIH NIBIB proposal • PIs and institutions • A. Malony and D. Tucker, University of Oregon • P. Papadopoulos, San Diego Supercomputing Center • C. Johnson and S. Parker, University of Utah • Under review by NIH Biomedical Imaging panel • Dynamic neuroimaging algorithms and visualization • Grid-based integration (processing and data sharing) • High-end tool integration and environments • Neuroinformatics data ontologies

  13. GEMINI Architecture

  14. Electromagnetic Data Spaces and Representation

  15. 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 • Build on emerging web services and grid technology • Leverage HPC compute and data centers • Create institutional and industry partnerships • Electrical Geodesics, Inc. • Cerebral Data Systems (UO partnership with EGI) • Looking for other industrial partnerships

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