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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. Outline. Brain, Biology, and Machine Initiative (BBMI) at UO

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iconic grid improving diagnosis of brain disorders

ICONIC Grid – Improving Diagnosis of Brain Disorders

Allen D. Malony

University of Oregon

Professor

Department of Computerand Information Science

Director

NeuroInformatics Center

Computational Science Institute

outline
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
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
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

good spatial

poor temporal

Cortical Activity

Knowledge Base

Head Analysis

Source Analysis

Structural /

Functional

MRI/PET

spatial pattern

recognition

temporal

dynamics

Cortical

Activity Model

Experiment

subject

Constraint

Analysis

IndividualBrain Analysis

Component

Response Model

neural

constraints

Dense

Array EEG / MEG

temporal pattern

recognition

Signal Analysis

Response Analysis

Component Response

Knowledge Base

poor spatial

good temporal

neuroimaging

integration

Integrated Dynamic Brain Analysis
experimental methodology and tool integration
Experimental Methodology and Tool Integration

16x256bits permillisec

(30MB/m)

CT / MRI

segmentedtissues

EEG

NetStation

BrainVoyager

processed EEG

mesh generation

source localization constrained to cortical surface

Interpolator 3D

EMSE

BESA

neuroinformatics center nic at uo
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
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
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
  • 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
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
topographic mapping of spike wave progression
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
animated topography of spike wave dynamics
Animated Topography of Spike–Wave Dynamics
  • Spatial & Temporal Dynamics
  • Linked Networks
    • Fronto-thalamic circuit

(executive control)

    • Limbic circuit

(episodic memory)

  • Problem of Superposition
    • How many sources?
    • Where are they located?
addressing superposition brain electrical fields
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
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
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
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
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
Conductivity Modeling

Governing Equations ICS/BCS

Continuous Solutions

Finite-DifferenceFinite-ElementBoundary-ElementFinite-VolumeSpectral

Discretization

System of Algebraic Equations

Discrete Nodal Values

TridiagonalADISORGauss-SeidelGaussian elimination

Equation (Matrix) Solver

 (x,y,z,t)J (x,y,z,t)B (x,y,z,t)

Approximate Solution

alternating direction implicit adi method
Alternating Direction Implicit (ADI) Method
  • Finite difference method
    • C++ and OpenMP on IBM p655 running Linux

305 seconds

source modeling with standard brain mri model
Source Modeling with Standard Brain MRI Model

Source model foranterior negative slow

wave (100-200 ms)

Source model forfirst medial positivewave (216-234 ms)

Source model forsecond medial positive

wave (256-308 ms)

uo iconic grid
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
iconic grid
ICONIC Grid

graphics workstations

interactive, immersive viz

other campus clusters

Internet 2

Gbit Campus Backbone

CNI

NIC

NIC

CIS

CIS

4x8

16

16

2x8

2x16

SMP

Server

IBM p655

Shared

Memory

IBM p690

Graphics

SMP

SGI Prism

Distributed

Memory

IBM JS20

Distributed

Memory

Dell Pentium Xeon

TapeBackup

SAN Storage SystemIBM SAN FS

5 Terabytes

iconic grid hardware
ICONIC Grid Hardware

p690

 16 processors

p655

 4 nodes

 8 processors per node

FibreChannel

FibreChannel

FAStT storage

 5 TB

SAN FS

Dell cluster

 16 nodes

 2 processors per node

JS20 Blade

 16 nodes

 2 processors per node

computational integrated neuroimaging system

raw

virtual

services

storage

resources

compute resources

Computational Integrated Neuroimaging System
leveraging internet hpc and grid computing
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 2

Internet 2 /National LambdaRail

Region 1

Regional networks

Region 5

Region 4

HPC servers

Regional clients

Oregon E-Science Grid

Region 3