ICONIC Grid – Improving Diagnosis of Brain Disorders Allen D. Malony University of Oregon Professor Department of Computer and Information Science Director NeuroInformatics Center Computational Science Institute

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Outline  Brain, Biology, and Machine Initiative (BBMI) at UO  Neuroinformatics research  Dynamic brain analysis problem  NeuroInformatics Center (NIC) at UO  Neuroinformatics technology and applications  Dense-array EEG and Electrical Geodesics, Inc. (EGI)  Epilepsy and pre-surgical planning (Dr. Frishkoff)  NIC research and development  ICONIC Grid HPC system at UO  IBM HPC solutions  HPC/Grid computing for Oregon’s science industry

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders 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

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Brain Dynamics Analysis Problem  Understand functional activity of the human cortex  Different cognitive research neuroscience contexts  Multiple research, clinical, and medical domains  Multiple experimental paradigms and methods  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)

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Integrated Dynamic 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

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders 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 EMSE Interpolator 3D NetStation

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders 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 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

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Funding Support  BBMI federal appropriation  DoD Telemedicine Advanced Technology Research Center (TATRC)  $40 million research attracted by BBMI  $10 million gift from Robert and Beverly Lewis family  Established Lewis Center for Neuroimaging (LCNI)  NSF Major Research Instrumentation  “Acquisition of the Oregon ICONIC Grid for Integrated COgnitive Neuroscience Informatics and Computation”  New proposal  NIH Human Brain Project Neuroinformatics  “GENI: Grid-Enabled Neuroimaging Integration”

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders 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  Epilepsy diagnosis, pre-surgical planning

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Epilepsy  Epilepsy affects ~5.3 million people in the U.S., Europe, & Japan  EEG in epilepsy diagnosis  childhood and juvenile absence  idiopathic (genetic)  “generalized” or multifocal?  EEG in presurgical planning  fast, safe, inexpensive  128/256 channels permit localization of seizure onset

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders EEG Methodology  Electroencephalogram (EEG)  EEG time series analysis  Event-related potentials (ERP)  Averaging to increase SNR  Linking brain activity to sensory–motor, cognitive functions (e.g., visual processing, response programming)  Signal cleaning (removal of noncephalic signal, “noise”)  Signal decomposition (PCA, ICA, etc.)  Neural Source localization

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders EEG Time Series - Progression of Absence Seizure First full spike–wave

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Topographic Waveforms – First Full Spike-Wave 350ms interval

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Topographic Mapping of Spike-Wave Progression  Palette scaled for wave-and-spike interval (~350ms) -130 uV (dark blue)  75 uV (dark red)  1 millisecond temporal resolution is required  Spatial density (256ch) to capture shifts in topography

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders  Spatial & Temporal Dynamics  Linked Networks  Fronto-thalamic circuit (executive control)  Limbic circuit (episodic memory)  Problem of Superposition  How many sources?  Where are they located? Animated Topography of Spike–Wave Dynamics

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Addressing Superposition: Brain Electrical Fields  Brain electrical fields are dipolar  Volume conduction  depth & location indeterminacy  Highly resistive skull (CSF: skull est. from 1:40 to 1:80)  Left-hemisphere scalp field may be generated by a right-hemisphere source  Multiple sources  superposition  Radial source  Tangential sources  one and two sources  varying depths

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Source Localization  Mapping of scalp potentials to cortical generators  Signal decomposition (addressing superposition)  Anatomical source modeling (localization)  Source modelling  Anatomical Constraints  Accurate head model and physics  Computational head model formulation  Mathematical Constraints  Inverse solutions apply mathematical criteria such as “smoothness” (LORETA) to constrain the solution

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders 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

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Advanced Image Segmentation  Native MR gives high gray-to-white matter contrast  Image analysis techniques  Edge detection, edge merger, region growing  Level set methods and hybrid methods  Knowledge-based  After segmentation, color contrasts tissue type  Registered segmented MRI

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Building Computational 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

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Conductivity Modeling Governing Equations ICS/BCS Discretization System of Algebraic Equations Equation (Matrix) Solver Approximate Solution 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)

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Alternating Direction Implicit (ADI) Method  Finite difference method  C++ and OpenMP on IBM p655 running Linux 305 seconds

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Source Modeling with Standard Brain MRI Model Source model for anterior negative slow wave ( ms) Source model for first medial positive wave ( ms) Source model for second medial positive wave ( ms)

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders 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

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders SMP Server IBM p655 Graphics SMP SGI Prism SAN Storage System IBM SAN FS Gbit Campus Backbone NICCIS Internet 2 Shared Memory IBM p690 Distributed Memory IBM JS20 CNI Distributed Memory Dell Pentium Xeon NIC 4x816 2x82x16 graphics workstationsinteractive, immersive vizother campus clusters ICONIC Grid 5 Terabytes Tape Backup

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders ICONIC Grid Hardware p690  16 processors p655  4 nodes  8 processors per node Dell cluster  16 nodes  2 processors per node JS20 Blade  16 nodes  2 processors per node FAStT storage  5 TB SAN FS Fibre Channel

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Computational Integrated Neuroimaging System

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders 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  Cerebral Data Systems (UO partnership with EGI)  Continue strong relationship with IBM and Life Sciences

IBM TheatreSC 2004ICONIC Grid – Improving Diagnosis of Brain Disorders Oregon E-Science Grid Region 4 Region 1 Region 2 Region 3 Region 5 Internet 2 / National LambdaRail Regional networks HPC servers Regional clients