ICONIC Grid – Improving Diagnosis of Brain Disorders

ICONIC Grid – Improving Diagnosis of Brain Disorders

Allen D. Malony

University of Oregon

ProfessorDepartment of Computerand Information Science

DirectorNeuroInformatics Center

Computational Science Institute

IBM Theatre SC 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


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


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)


Integrated Dynamic Brain Analysis

IndividualBrain Analysis

Structural /FunctionalMRI/PET

DenseArray EEG /

MEG

ConstraintAnalysis

Head Analysis

Source Analysis

Signal Analysis

Response Analysis

Experimentsubject

temporaldynamics

neuralconstraints

CorticalActivity Model

ComponentResponse Model

spatial patternrecognition

temporal patternrecognition

Cortical ActivityKnowledge Base

Component ResponseKnowledge Base

good spatialpoor temporal

poor spatialgood temporal neuroimaging

integration


Experimental Methodology and Tool Integration

source localization constrained to cortical surface

processed EEG

BrainVoyager

BESA

CT / MRI

EEG segmentedtissues

16x256bits permillisec(30MB/m)

mesh generation

EMSEInterpolator 3D

NetStation


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


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”


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


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


Topographic Waveforms – First Full Spike-Wave

350ms interval


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


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

QuickTime™ and aAnimation decompressor

are needed to see this picture.


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


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


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


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


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


Conductivity Modeling

Governing Equations ICS/BCS

Discretization

System of Algebraic Equations

Equation (Matrix) Solver

Approximate Solution

Continuous Solutions

Finite-DifferenceFinite-Element

Boundary-ElementFinite-Volume

Spectral

Discrete Nodal Values

TridiagonalADISOR

Gauss-SeidelGaussian elimination

(x,y,z,t)J (x,y,z,t)B (x,y,z,t)


Alternating Direction Implicit (ADI) Method

Finite difference method C++ and OpenMP on IBM p655 running Linux

ADI speedup

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

0 2 4 6 8 10

number of processors

speedup

64x64x44128x128x88256x256x176ideal speedup

305 seconds


Source Modeling with Standard Brain MRI Model

Source model foranterior negative slowwave (100-200 ms)

Source model forfirst medial positivewave (216-234 ms)

Source model forsecond medial positivewave (256-308 ms)


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


SMPServerIBM p655

GraphicsSMP

SGI Prism

SAN Storage SystemIBM SAN FS

Gbit Campus Backbone

NIC CIS CIS

Internet 2

SharedMemory

IBM p690

DistributedMemory

IBM JS20

CNI

DistributedMemory

Dell Pentium Xeon

NIC4x8 16 16 2x8 2x16

graphics workstations interactive, immersive viz other campus clusters

ICONIC Grid

5 TerabytesTape

Backup


ICONIC Grid Hardwarep690 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 TBSAN FS

FibreChannel

FibreChannel


Computational Integrated Neuroimaging System

… …

raw

storageresources

virtualservices

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

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


Oregon E-Science Grid

Region 4

Region 1Region 2

Region 3

Region 5

Internet 2 /National LambdaRail

Regional networks

HPC serversRegional clients

Documents

ICONIC Grid – Improving Diagnosis of Brain Disorders