Computational Informatics for Brain Electromagnetic Research

Computational Informatics for Brain Electromagnetic Research

High-Performance Computing, Computational Science, and NeuroInformatics Research Allen D. Malony Department of Computer and Information Science NeuroInformatics Center (NIC) Computational Science Institute University of Oregon Outline High-performance computing research Computational science at UO Projects Computational Science Institute Neuroinformatics research Interactions and funding

Project areas TAU parallel performance system NeuroInformatics Center (NIC) ICONIC Grid April 29, 2004 PNNL UO Visit High-Performance Computing Research Strong associations with DOE national laboratories DOE funding Los Alamos National Lab Lawrence Livermore National Lab Sandia National Lab (Livermore) Argonne National Lab National Energy Research Supercomputing Center

Office of Science, Advance Scientific Computing ASCI/NNSA NSF funding Academic Research Infrastructure Major Research Instrumentation April 29, 2004 PNNL UO Visit Project Areas 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 April 29, 2004

PNNL UO Visit TAU Parallel Performance System Allen D. Malony Sameer S. Shende Department of Computer and Information Science Computational Science Institute University of Oregon Parallel Performance Research Tools for performance problem solving Empirical-based performance optimization process Performance Tuning hypotheses Performance Diagnosis properties Performance Experimentation characterization Performance Observation

April 29, 2004 Performance Technology Instrumentation Measurement Analysis Visualization PNNL UO Visit Complexity Challenges for Performance Tools Computing system environment complexity Observation integration and optimization Access, accuracy, and granularity constraints Diverse/specialized observation capabilities/technology Restricted modes limit performance problem solving Sophisticated software development environments Programming paradigms and performance models

Performance data mapping to software abstractions Uniformity of performance abstraction across platforms Rich observation capabilities and flexible configuration Common performance problem solving methods April 29, 2004 PNNL UO Visit General Problems How do we create robust and ubiquitous performance technology for the analysis and tuning of parallel and distributed software and systems in the presence of (evolving) complexity challenges? How do we apply performance technology effectively for the variety and diversity of performance problems that arise in the context of complex parallel and distributed computer systems? April 29, 2004 PNNL UO Visit TAU Performance System Tuning and Analysis Utilities Performance system framework for scalable parallel and distributed high-performance computing Targets a general complex system computation model

Integrated toolkit for performance instrumentation, measurement, analysis, and visualization nodes / contexts / threads Multi-level: system / software / parallelism Measurement and analysis abstraction Portable performance profiling and tracing facility Open software approach with technology integration University of Oregon , Forschungszentrum Jlich, LANL April 29, 2004 PNNL UO Visit TAU Performance System Architecture Paraver April 29, 2004 EPILOG PNNL UO Visit

TAU Performance System Status Computing platforms Programming languages C, C++, Fortran 77/90/95, HPF, Java, OpenMP, Python Communication libraries IBM SP / Power4, SGI Origin 2K/3K, ASCI Red, Cray T3E / SV-1 / X-1, HP (Compaq) SC (Tru64), HP Superdome (HP-UX), Sun, Hitachi SR8000, NEX SX5/6, Linux clusters (IA-32/64, Alpha, PPC, PA-RISC, Power, Opteron), Apple (G4/5, OS X), Windows MPI, PVM, Nexus, shmem, LAMPI, MPIJava Thread libraries pthreads, SGI sproc, Java,Windows, OpenMP April 29, 2004

PNNL UO Visit TAU Performance System Status (continued) Compilers Application libraries (selected) POOMA, MC++, ECMF, Uintah, VTF, UPS, GrACE Performance technology integrated with TAU Blitz++, A++/P++, PETSc, SAMRAI, Overture, PAWS Application frameworks (selected) Intel KAI (KCC, KAP/Pro), PGI, GNU, Fujitsu, Sun, Microsoft, SGI, Cray, IBM (xlc, xlf), Compaq, Hitachi, NEC, Intel

PAPI, PCL, DyninstAPI, mpiP, MUSE/Magnet TAU full distribution (Version 2.x, web download) TAU performance system toolkit and users guide Automatic software installation and examples April 29, 2004 PNNL UO Visit Computational Science Integration of computer science in traditional science Math disciplines Third model of Geoscience Computer scientific Science research Application of Paleontology high-performance computation, algorithms

and networking Biology Neuroscience Psychology Parallel computing Grid computing April 29, 2004 PNNL UO Visit Computational Science Projects at UO Geological science Bioinformatics

Zebrafish Information Network (ZFIN) Evolution of gene families Oregon Bioinformatics Tool Neuroinformatics Electronic notebooks Domain-specific problem solving environments Model coupling for hydrology Dinosaur skeleton and motion modeling Computational Science Institute April 29, 2004 PNNL UO Visit Computational Science Cognitive Neuroscience Computational methods applied to scientific research 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 High-performance simulation of complex phenomena Large-scale data analysis and visualization Multi-modal (electromagnetic, MR, optical) Physical brain models and theoretical cognitive models Need for robust tools: computational & informatic April 29, 2004 PNNL UO Visit 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) April 29, 2004 PNNL UO Visit Integrated Electromagnetic Brain Analysis good spatial poor temporal Cortical Activity Knowledge Base Head Analysis Structural / Functional

MRI/PET Experiment subject Dense Array EEG / MEG Source Analysis temporal dynamics Cortical Activity Model Constraint Analysis neural constraints spatial pattern recognition Individual Brain Analysis Component Response Model temporal pattern recognition Signal Analysis

Response Analysis poor spatial good temporal April 29, 2004 Component Response Knowledge Base neuroimaging integration PNNL UO Visit Experimental Methodology and Tool Integration 16x256 bits per millisec (30MB/m) CT / MRI segmented tissues EEG NetStation processed EEG Interpolator 3D April 29, 2004 BESA

BrainVoyager mesh generation source localization constrained to cortical surface EMSE PNNL UO Visit NeuroInformatics Center (NIC) Application of computational science methods to cognitive and clinical neuroscience problems Advance techniques for integrated neuroimaging 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 Coupled modeling (EEG/ERP and MR analysis) Advanced statistical factor analysis FDM/FEM brain models (EEG, CT, MRI) Source localization Problem-solving environment for brain analysis April 29, 2004 PNNL UO Visit NIC Organization Director, Allen D. Malony Associate Director, Don M. Tucker Ph.D., Computer Science; B.S., Physics Computer Scientist, Sameer S. Shende Ph.D., Computer Science; B.S., Physics

Computational Physicist, Sergei Turovets Professor, Psychology; CEO, EGI Computational Scientist, Kevin Glass Associate Professor, Computer and Information Science Ph.D., Computer Science; parallel computing specialist Mathematician, Bob Frank M.S., Mathematics April 29, 2004 PNNL UO Visit Funding Support BBMI federal appropriation

NSF Major Research Instrumentation DoD Telemedicine Advanced Technology Research Command (TATRC) Initial budget of approximately $750K Oct. 1, 2002 through March 31, 2004 ICONIC Grid, awarded New proposal opportunities NIH Human Brain Project Neuroinformatics NSF ITR April 29, 2004 PNNL UO Visit NIC Approaches Optimize spatial resolution

Optimize temporal resolution MRI structural information Measurement of skull conductivity Convergence / co-recording with MEG and fMRI Use EEG/MEG time course for fMRI signal extraction Decomposition of component analysis (ICA, PCA) Single-trial analysis Computational brain models Boundary and finite element brain models Brain information databases and atlases April 29, 2004 PNNL UO Visit EEG/ERP Methodology Electroencephalogram (EEG) Event-Related Potential (ERP)

Stimulus-locked measures of brain dynamics Generated from subject- and trial-based analysis Raw EEG datasets processed and analyzed Segmentation to time series waveforms Blink removal and other cleaning ERP analysis Averaging for increasing signal to noise Characterization with respect to trial conditions Results visualization Source localization April 29, 2004 PNNL UO Visit EGI Geodesics Sensor Net Electrical Geodesics Inc. Dense-array sensor technology

256-channel geodesics sensor net 64/128/256 channels AgCl plastic electrodes Carbon fiber leads Future optical sensors EGI + LANL April 29, 2004 PNNL UO Visit EEG/ERP Experiment Management System Support EEG-based cognitive neuroscience research Based on experiment model Management of experiment data

Experiment type Subjects measured for trial types Raw and processed datasets and derived statistics Per experiment/subject/trial database Secure protection and storage with selective access Analysis tools and workflows Generation of results (across experimental variables) Analysis processes with multi-tool workflows April 29, 2004 PNNL UO Visit EEG/ERP Experiment Analysis Environment raw analysis workflow processed datasets / derived results

virtual services storage resources April 29, 2004 compute resources PNNL UO Visit Source Localization 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 April 29, 2004 PNNL UO Visit 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 advance level set methods and hybrid methods

April 29, 2004 PNNL UO Visit Building Finite Element Brain Models MRI segmentation of brain tissues Conductivity model Measure head tissue conductivity Electrical impedance tomography currents are injected between electrode pair resulting potential measured at remaining electrodes scalp CSF small Finite element forward solution Source inverse modeling

skull Explicit and implicit methods Bayesian methodology April 29, 2004 cortex PNNL UO Visit Conductivity Modeling Continuous Solutions Finite-Difference Finite-Element Boundary-Element Finite-Volume Spectral Discrete Nodal Values Tridiagonal ADI SOR Gauss-Seidel Gaussian elimination (x,y,z,t) J (x,y,z,t) B (x,y,z,t) April 29, 2004

Governing Equations ICS/BCS Discretization System of Algebraic Equations Equation (Matrix) Solver Approximate Solution PNNL UO Visit Source Localization Analysis Environment raw virtual services storage resources April 29, 2004 compute resources PNNL UO Visit NIC Computational Cluster (Neuronic Cluster) Dell computational cluster

16 dual-processor nodes 2.8 MHz Pentium Xeon 4 Gbyte memory 36 Gbyte disk Dual Gigabit ethernet adaptors 2U form factor Master node (same specs) 2 Gigabit ethernet switches Brain modeling Component analysis April 29, 2004 PNNL UO Visit NIC Relationships Utah UCSD USC OHSU / OGI

Internet2 Academic CIS April 29, 2004 Physics Argonne Sandia NCSA Labs / Centers Intel UO Departments Psychology BDL BEL LANL NIC IBM EGI Industry UO Centers/Institutes CSI

BBMI CNI CDSI NSI PNNL UO Visit NSF MRI Proposal Major Research Instrumentation (MRI) Acquisition of the Oregon ICONIC Grid for Integrated COgnitive Neuroscience Informatics and Computation PIs Senior personnel Computer Science: Malony, Conery Psychology: Tucker, Posner, Nunnally Computer Science: Douglas, Cuny Psychology: Neville, Awh, White

Approximately $1.2M over three years April 29, 2004 PNNL UO Visit ICONIC Grid graphics workstations interactive, immersive viz other campus clusters Internet 2 Gbit Campus Backbone NIC 4x8 CIS 16 CIS 16 SMP Server Shared Memory Graphics

SMP IBM p655 IBM p690 SGI MARS SAN Storage System April 29, 2004 CNI 2x8 NIC 2x16 Distributed Distributed Memory Memory IBM JS20 Dell Pentium Xeon 5 Terabytes PNNL UO Visit Cognitive 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 development and deprived Attention and emotion regulation Spatial working memory and selective attention Attention and psychopathology April 29, 2004 PNNL UO Visit Computer Science and ICONIC Grid Scheduling and resource management

PSEs for computational science Provide scientists an entre to the computational and data management power of the infrastructure without requiring specialized knowledge of parallel execution Marine seismic tomograph, molecular evolution Interactive / immersive three-dimensional visualization Assign hardware resources to computation tasks Scheduling of workloads for Explore multi-sensory visualization Merge 3D graphics with force-feedback haptics Parallel performance evaluation April 29, 2004 PNNL UO Visit

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