Neuroinformatics, the ICONIC Grid,
and Oregon’s Science Industry
Allen D. Malony
University of Oregon
Oregon’s 2004 Biosci...
Outline
 Computational science
 High-performance computing research at UO
 Brain, Biology, and Machine Initiative
 Neu...
Computational Science
 Integration of computer
science in traditional
scientific disciplines
 Increasingly accepted
mode...
Computational Science Projects at UO
 Geological science
 Model coupling for hydrology
 Bioinformatics
 Zebrafish Info...
HPC Research Project Areas at UO
 Parallel performance evaluation and tools
 Parallel language systems
 Tools for paral...
HPC Research Affiliations at UO
 Strong associations with DOE national laboratories
 Los Alamos National Lab, Lawrence L...
Brain, Biology, and Machine Initiative
 UO interdisciplinary research in cognitive
neuroscience, biology, computer scienc...
BBMI Sponsored Research
 $40 million research attracted by BBMI
 DoD TATRC funding
 Telemedicine Advanced Technology Re...
BBMI Research and Development Plan
 Imaging technology and integration
 Dense-array EEG and MRI
 Coil development
 Sim...
Computational Science and Human Neuroscience
 Computational methods applied to scientific research
 High-performance sim...
Brain Dynamics Analysis Problem
 Identify functional components
 Different cognitive neuroscience research contexts
 Cl...
Integrated Electromagnetic Brain Analysis
Individual
Brain Analysis
Structural /
Functional
MRI/PET
Dense
Array EEG /
MEG
...
Experimental Methodology and Tool Integration
source localization
constrained to
cortical surface
processed
EEG
BrainVoyag...
NeuroInformatics Center (NIC)
 Application of computational science methods to
human neuroscience problems (cognitive, cl...
Electrical Geodesics Inc. (EGI)
 EGI Geodesics Sensor Net
 Dense-array sensor technology
 64/128/256 channels
 256-cha...
UO MRI Facility (Lewis Center for Neuroimaging)
 Siemens Head-Only 3T MRI System
 Tailored to performing
functional imag...
Source Localization Problem
 Mapping of scalp potentials to cortical generators
 Single time sample and time series
 Re...
Advanced Image Segmentation
 Native MR gives high
gray-to-white matter
contrast
 Edge detection finds region
boundaries
...
Building Finite Element Brain Models
 MRI segmentation of brain tissues
 Conductivity model
 Measure head tissue conduc...
Computational Integrated Neuroimaging System
… …
raw
storage
resources
virtual
services
compute resources
UO ICONIC Grid
 NSF Major Research Instrumentation (MRI) proposal
 “Acquisition of the Oregon ICONIC Grid for Integrated...
SMP
Server
IBM p655
Graphics
SMP
SGI MARS
SAN Storage System
Gbit Campus Backbone
NIC CIS CIS
Internet 2
Shared
Memory
IBM...
Human Neuroscience and ICONIC Grid
 Common questions to be explored
 Identifying brain networks
 Critical periods durin...
Computer Science and ICONIC Grid
 Scheduling and resource management
 Assign hardware resources to computation tasks
 S...
NIC Relationships
Biology
CIS
CSI
OHSUUtah
UCSD
USC
Academic Labs / Centers
LANL Argonne
SDSC
Internet2
EGI
Industry
Intel...
Technology Transfer in Human Neuroscience
 UO’s BBMI is conducting pioneering research and
development in human neuroscie...
Leveraging Internet, HPC, and Grid Computing
 Telemedicine imaging and neurology
 Distributed EEG and MRI measurement an...
Region 4
Region 1Region 2
Region 3
Region 5
Oregon E-Science Grid
Internet 2 /
National LambdaRail
Regional networks
HPC s...
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  • In cognitive neuroscience studies, it is has been shown that EEG and fMRI studies provide complimentary information that informs researchers about brain functions. We believe that the EEG can be used to compliment other brain imaging methods.
  • Constraining the source solutions to the cortical surface is a major advantage for analyzing EEG and ERP effects. A working assumption is that sources are likely to be oriented normal to the cortical surface.
  • [Power Point Presentation]

    1. 1. Neuroinformatics, the ICONIC Grid, and Oregon’s Science Industry Allen D. Malony University of Oregon Oregon’s 2004 Bioscience ConferenceMay 10, 2004 Professor Department of Computer and Information Science Director NeuroInformatics Center Computational Science Institute
    2. 2. Outline  Computational science  High-performance computing research at UO  Brain, Biology, and Machine Initiative  Neuroinformatics and the ICONIC Grid  NeuroInformatics Center (NIC)  Electrical Geodesics, Inc. (EGI)  ICONIC Grid system and application  HPC / Grid computing for Oregon’s science industry  Services delivery (research, clinical, medical, …)  HPC resource centers  High-bandwidth state-wide networking
    3. 3. Computational Science  Integration of computer science in traditional scientific disciplines  Increasingly accepted model of scientific research  Application of high-performance computation, algorithms, networking, database, and visualization  Parallel and grid computing  Integrated problem-solving environments  Computer science research at the core Computer Science Biology Neuroscience PsychologyPaleontology Geoscience Math
    4. 4. Computational Science Projects at UO  Geological science  Model coupling for hydrology  Bioinformatics  Zebrafish Information Network (ZFIN)  Evolution of gene families  Oregon Bioinformatics Toolkit  Neuroinformatics  Paleontology  Dinosaur skeleton and motion modeling  Artificial intelligence  Computational Intelligence Research Lab (CIRL)  Oregon Computational Science Institute
    5. 5. HPC Research Project Areas at UO  Parallel performance evaluation and tools  Parallel language systems  Tools for parallel system and software interaction  Source code analysis  Parallel component software  Computational services  Grid computing  Parallel modeling and simulation  Scientific problem solving environments
    6. 6. HPC Research Affiliations at UO  Strong associations with DOE national laboratories  Los Alamos National Lab, Lawrence Livermore National Lab, Sandia National Lab, Argonne National Lab, Pacific Northwest National Lab  DOE funding  Office of Science, Advance Scientific Computing Research  Accelerated Strategic Computing Initiative (ASCI/NNSA)  NSF funding  Academic Research Infrastructure  Major Research Instrumentation
    7. 7. Brain, Biology, and Machine Initiative  UO 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 of neuroimaging resources  Magnetic Resonance Imaging (MRI) systems  Dense-array EEG systems  Computation clusters for high-end analysis  Establish and support institutional centers
    8. 8. BBMI Sponsored Research  $40 million research attracted by BBMI  DoD TATRC funding  Telemedicine Advanced Technology Research Center  $10 million gift from Robert and Beverly Lewis family  Established Lewis Center for Neuroimaging (LCNI)  Dr. Ray Nunnally, Director  NIH  NSF  Oregon bond funds  UO foundation funds
    9. 9. BBMI Research and Development Plan  Imaging technology and integration  Dense-array EEG and MRI  Coil development  Simultaneous measurement  Computational analysis problems  Image segmentation, analysis, identification  EEG signal decomposition, component analysis, source localization  Internet-based capabilities for analysis services, data archiving, and data mining  Computation and data grid for bio and neuro sciences
    10. 10. Computational Science and Human Neuroscience  Computational methods applied to scientific research  High-performance simulation of complex phenomena  Large-scale data analysis and visualization  Understand functional activity of the human cortex  Multiple cognitive, clinical, and medical domains  Multiple experimental paradigms and methods  Need for coupled/integrated modeling and analysis  Multi-modal (electromagnetic, MR, optical)  Physical brain models and theoretical cognitive models  Need for robust tools  Computational, informatic, and collaborative
    11. 11. Brain Dynamics Analysis Problem  Identify functional components  Different cognitive neuroscience research contexts  Clinical and medical applications  Interpret with respect to physical and cognitive models  Requirements: spatial (structure), temporal (activity)  Imaging techniques for analyzing brain dynamics  Blood flow neuroimaging (PET, fMRI)  good spatial resolution  functional brain mapping  temporal limitations to tracking of dynamic activities  Electromagnetic measures (EEG/ERP, MEG)  msec temporal resolution to distinguish components  spatial resolution sub-optimal (source localization)
    12. 12. Integrated Electromagnetic Brain Analysis Individual Brain Analysis Structural / Functional MRI/PET Dense Array EEG / MEG Constraint Analysis Head Analysis Source Analysis Signal Analysis Response Analysis Experiment subject temporal dynamics neural constraints Cortical Activity Model Component Response Model spatial pattern recognition temporal pattern recognition Cortical Activity Knowledge Base Component Response Knowledge Base good spatial poor temporal poor spatial good temporal neuroimaging integration
    13. 13. Experimental Methodology and Tool Integration source localization constrained to cortical surface processed EEG BrainVoyager BESA CT / MRI EEG segmented tissues 16x256 bits per millisec (30MB/m) mesh generation EMSEInterpolator 3D NetStation
    14. 14. NeuroInformatics Center (NIC)  Application of computational science methods to human neuroscience problems (cognitive, clinical)  Understand functional activity of the brain  Help to diagnosis brain-related disorders  Utilize high-performance computing and simulation  Support large-scale data analysis and visualization  Advanced techniques for integrated neuroimaging  Coupled modeling (EEG/ERP and MR analysis)  Advanced statistical analysis (PCA, ICA)  FDM/FEM brain models (EEG, CT, MRI)  Source localization (dipole, linear inverse models)  Problem-solving environment for brain analysis
    15. 15. Electrical Geodesics Inc. (EGI)  EGI Geodesics Sensor Net  Dense-array sensor technology  64/128/256 channels  256-channel geodesics sensor net  AgCl plastic electrodes  Carbon fiber leads  Net Station  Advanced EEG/ERP data analysis  Stereotactic EEG sensor registration  Research and medical services  Stroke monitoring and localization
    16. 16. UO MRI Facility (Lewis Center for Neuroimaging)  Siemens Head-Only 3T MRI System  Tailored to performing functional imaging  Human subjects  Monitor common physiologic parameters  heart rate, respiration  peripheral pulse oxygenation  eye location and eye movement  Audio and visual stimulus  Special RF screening room  MRI coil development
    17. 17. Source Localization Problem  Mapping of scalp potentials to cortical generators  Single time sample and time series  Requirements  Accurate head model and physics  High-resolution 3D structural geometry  Precise tissue identification and segmentation  Correct tissue conductivity assessment  Computational head model formulation  Finite element model (FEM)  Finite difference model (FDM)  Forward problem calculation  Dipole search strategy
    18. 18. Advanced Image Segmentation  Native MR gives high gray-to-white matter contrast  Edge detection finds region boundaries  Segments formed by edge merger  Color depicts tissue type  Investigate more advanced level set methods and hybrid methods
    19. 19. Building Finite Element Brain Models  MRI segmentation of brain tissues  Conductivity model  Measure head tissue conductivity  Electrical impedance tomography  small currents are injected between electrode pair  resulting potential measured at remaining electrodes  Finite element forward solution  Source inverse modeling  Explicit and implicit methods  Bayesian methodology
    20. 20. Computational Integrated Neuroimaging System … … raw storage resources virtual services compute resources
    21. 21. 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: Malony, Conery  Psychology: Tucker, Posner, Nunnally  Senior personnel  Computer Science: Douglas, Cuny  Psychology: Neville, Awh, White  Computational, storage, and visualization infrastructure
    22. 22. SMP Server IBM p655 Graphics SMP SGI MARS SAN Storage System Gbit Campus Backbone NIC CIS CIS Internet 2 Shared Memory IBM p690 Distributed Memory IBM JS20 CNI Distributed Memory Dell Pentium Xeon NIC4x8 16 16 2x8 2x16 graphics workstations interactive, immersive viz other campus clusters ICONIC Grid 5 TerabytesTape Backup
    23. 23. Human Neuroscience and ICONIC Grid  Common questions to be explored  Identifying brain networks  Critical periods during normal development  Network involvement in psychopathologies  Training interventions in network development  Research areas  Development of attentional networks  Brain plasticity in normal & altered development  Attention and emotion regulation  Spatial working memory and selective attention  Psychopathology
    24. 24. Computer Science and ICONIC Grid  Scheduling and resource management  Assign hardware resources to computation tasks  Scheduling of workloads for quality of service  Problem-solving computational science environments  Provide scientists an entrée to the ICONIC Grid without requiring specialized knowledge of parallel execution  Interactive / immersive three-dimensional visualization  Explore multi-sensory visualization  Merge 3D graphics with force-feedback haptics  Parallel performance evaluation
    25. 25. NIC Relationships Biology CIS CSI OHSUUtah UCSD USC Academic Labs / Centers LANL Argonne SDSC Internet2 EGI Industry Intel IBM NIC UO Departments UO Centers/Institutes BBMI CDSI LCNI Physics NSI LLNL SGI OSU PSU Psychology Math
    26. 26. Technology Transfer in Human Neuroscience  UO’s BBMI is conducting pioneering research and development in human neuroscience, genetics and proteomics, and computational science for future neurological medicine and health care  Greater precision and speed in brain imaging has high research and medical relevance  Integrated medical imaging (EEG/MEG, MRI, radiology)  Automatic image assessment (detection and diagnosis)  Neurological evaluation and surgical planning  Linking of genetics factors with complex cognitive traits (personality, learning, attention) has potential for therapies and pharmaceutical clinical drug development
    27. 27. 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  Neet to enhance HPC and grid infrastructure in Oregon  Build on emerging web services and grid technology  Establish HPC resources with high-bandwidth networks  Create institutional and industry partnerships  UO is working closely with EGI to develop high-end EEG analysis services framework  Pilot neuroimaging services model on ICONIC Grid
    28. 28. Region 4 Region 1Region 2 Region 3 Region 5 Oregon E-Science Grid Internet 2 / National LambdaRail Regional networks HPC servers Regional clients
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