NeuroInformatics Center Neuroinformatics and High-Performance Grid Computing for Rural Telemedicine Allen D. Malony Computational Science Institute Department of Computer and Information Science Don Tucker Electrical Geodesics, Inc. Department of Psychology NeuroInformatics Center University of Oregon

Outline 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 NIC research and development High-performance and grid computing ICONIC Grid HPC system at UO HPC/Grid computing for Oregon’s health care industry E-Health grid for rural telemedicine

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 Cortical Activity Knowledge Base Component Response good spatial poor temporal poor spatial good temporal neuroimaging integration 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.

Experimental Methodology and Tool Integration 16x256 bits per millisec (30MB/m) CT / MRI EEG segmented tissues NetStation BrainVoyager processed EEG mesh generation source localization constrained to cortical surface 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. Interpolator 3D EMSE BESA

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

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

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 Requires massive storage and data analysis capabilities Unifying Features of Idiopathic Generalized Epilepsies •Normal neurologically before onset of seizures •EEG changes appear generally well organized with otherwise normal background •Long term outlook for cognitive function is good Many of the epilepsies previously classified as “generalized” are not truly generalized. •Idiopathic Generalized Seizures occur in neurologically normal children •Most seizures or epilepsy syndromes previously classified as “generalized” or “undetermined” should be thought of as Epileptic Encephalopathies •Epileptic Encephalopathies may have an identifiable mechanism or respond to specific therapies

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

EEG Time Series - Progression of Absence Seizure First full spike–wave Figure 1. Topographic waveform plot for the first full spike-wave in one patient’s seizure. Following the clinical EEG convention, negative is up. The 256 channels are arrayed in two dimensions as they would be seen looking down on the top of the head, with the nose at the top of the page, and with the lower channels unwrapped to the sides of the page. This 350 ms epoch captures the first wave and spike of this seizure, plus the initial onset of the second wave. Note that the prominent spike-wave pattern over medial, superior frontal sites inverts over lateral, inferior frontal sites, indicating neural sources in medial frontal cortex.

Topographic Mapping of Spike-Wave Dynamics Spatial and temporal dynamics need to be observed 1 millisecond temporal resolution is required Spatial density to capture shifts in topography Linked networks contribute to measured potentials Problem of superposition (source number and location) Figure 2. Selected topographic maps for the spike-wave pattern shown in Figure 3. Palette is scaled for this wave and spike interval, from –130 uV (dark blue) to 75 uV (dark red). The interval characterized by each map is 1 ms, with the selections made to illustrate the major topographic transitions of this subject’s spike-wave pattern.

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

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

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

ICONIC Grid Hardware IBM p690 IBM p655 IBM FAStT Dell cluster IBM JS20  16 processors IBM p655  4 nodes  8 processors per node Fibre Channel Fibre Channel IBM FAStT  5 TB  SAN FS Dell cluster  16 nodes  2 processors per node IBM JS20  16 blades  2 processors per node

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

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 Link HPC servers with high-bandwidth networks Create institutional and industry partnerships Cerebral Data Systems (UO partnership with EGI) Continue strong relationship with IBM and Life Sciences

Oregon E-Health Grid for Rural Telemedicine Region 2 Internet 2 / National LambdaRail Region 1 Regional networks Region 5 Region 4 HPC servers Rural sites Region 3 Regional clients