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Neuroinformatics for Telemedicine and Medical Services

Neuroinformatics for Telemedicine and Medical Services. Neuroinformatics Center University of Oregon. Allen D. Malony Department of Computer and Information Science. Don Tucker Electrical Geodesics, Inc. NeuroInformatics Center (NIC) at UO.

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Neuroinformatics for Telemedicine and Medical Services

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  1. Neuroinformatics for Telemedicine and Medical Services Neuroinformatics Center University of Oregon Allen D. Malony Department of Computerand Information Science Don Tucker Electrical Geodesics, Inc.

  2. NeuroInformatics Center (NIC) at UO • Computational science applied to human neuroscience • Tools to help understand dynamic brain function • Tools to help diagnosis brain-related disorders • HPC simulation, complex data analysis, medical services • Integration of neuroimaging methods and technology • Coupled measures and evaluation (EEG/ERP, MR) • Advanced statistical signal analysis (PCA, ICA) • Advanced image analysis (segmentation, anatomy) • Computational head modeling (electromagnetics, FDM) • Source localization modeling (dipole, linear inverse) • Internet-based capabilities for brain analysis services, data archiving, and data mining

  3. NIC Organization • Allen D. Malony, Director • Don M. Tucker, Associate Director • Sergei Turovets, Computational Physicist • Bob Frank, Senior Data Analyst • Kai Li, Computer Scientist • Chris Hoge, Computational Software Engineer • Matt Sottile, Computer Scientist, CIS Department • Dejing Dou, Computer Scientist, CIS Department • Gwen Frishkoff, Neuro Scientist, Wisconsin Medical C. • Brad Davidson, Systems administrator • Adnan Salman, Ph.D. student, Computer Science • Jason Sydes, Ph.D. student, Computer Science

  4. NIC Scientists/Students and Projects • Sergei Turovets, Ph.D., PhysicsPhysics-based computational models of human head tissues for developing electrical and optical probes of brain activity • Kai Li, Ph.D., Computer ScienceTissue segmentation of neurological images, cortical surface extraction of the individual brain, and brain visualization • Bob Frank, M.S., MathematicsStatistical analysis of neurophysiological recordings to separate brain from non-brain signals in scalp and source space • Chris Hoge, M.S., Computer ScienceComputational software development, high-performance signal analysis tools, application server environments, EEG and MRI databases, neuroinformatics workflow • Matt Sottile, Ph.D., Computer ScienceEEG data modeling methods for detecting spike and seizure • Dejing Dou, Ph.D., Computer ScienceData mining and ontologies • Gwen Frishkoff, Ph.D., Psychology, Wisconsin Medical CollegeAutomated component separation, ERP pattern classification, and neuro electromagnetic ontologies • Adnan Salman, Ph.D. student, Computer ScienceOptimization solutions for finding human head tissues conductivity • Jason Sydes, M.S. student, Computer ScienceEEG and MRI database development

  5. Neuroinformatic Challenges • Dense-array EEG signal analysis and decomposition • Artifact cleaning and component analysis • Automatic brain image segmentation • Brain tissue identification • Cortex extraction • Computational head modeling • Tissue conductivity estimation • Source localization • Statistical analysis to detect brain states • Discriminant analysis • Pattern recognition • Electromagnetic databases and ontologies • HPC, data management, workflow, services delivery

  6. Observing Dynamic Brain Function • Brain activity occurs in cortex • Observing brain activity requires • high temporal and spatial resolution • Cortex activity generates scalp EEG • EEG data (dense-array, 256 channels) • High temporal (1msec) / poor spatial resolution (2D) • MR imaging (fMRI, PET) • Good spatial (3D) / poor temporal resolution (~1.0 sec) • Want both high temporal and spatial resolution • Need to solve source localization problem!!! • Find cortical sources for measured EEG signals

  7. Electromagnetics Modeling / Source Localization

  8. Computational Head Models Source localization requires modeling Full physics modeling of human head electromagnetics Step 1: Head tissue segmentation Obtain accurate tissue geometries Step 2: Numerical forward solution 3D numerical head model Map current sources to scalp potential Step 3: Conductivity modeling Inject currents and measure response Find accurate tissue conductivities Step 4: Source optimization Create cortex dipoles / lead field matrix

  9. Brain Tissue Segmentation (K. Li) • Exploit various prior knowledge • Structural, geometrical, morphological, radiological • Segmentation workflow • Classifying voxel types of entire image then extract brain • Two core segmentation techniques • Relative thresholding for voxel classification • Morphological image analysis for brain extraction

  10. Cortical Surface Reconstruction • Performed after brain tissue segmentation • Use the marching cube isosurface algorithm • Guarantee topology correctness • Application to surface tessellation and dipole creation

  11. Skull Warping • Skull atlas and T1 MRI (inputs) • Performed after brain extraction • Transformation sequences • Scaling the brain volume • Rigid transformation(translation, rotation) • Affine transformation(translation, rotation,scaling, and shearing) • Deformation field based transformation • For head model construction when individual skull unavailable Skull warping

  12. BrainK Segmentation and Cortex Extraction • GUI-based application or command-line programs • Fully automatic processing in both modes • Segmentation tuning with global parameters • Allows segmentation editing segmentation result cortical surface visualization workflow parameters

  13. Modeling Head Electromagnetics (S. Turovets) • Head volume conduction (isotropic Poisson equation) • Need to model tissue and skull anisotropy • With existing principal axes, the tensor is symmetrical with 6 independent terms: ij = ji • Numerical implementation so far dealt with the orthotropic case: ii are different, allother components of ij = 0, ij. • Complete anisotropic forward solver with arbitrary ij implemented  ()=S in  ,  - scalar function of (x,y.z) -()  n = 0 on   (ij)=Sin  , ij - tensor function of (x,y.z) -ij()  n = 0 on 

  14. Conductivity Optimization (A. Salman) • Correct source inverse solutions depend on accurate estimates of head tissue conductivities • Scalp, skull, brain, CSF, … • Design as a conductivity search problem • Estimate conductivity values • Computer forward solution and compare to measured • Iterate until error threshold is obtained (global minimum) • Use electrical impedance tomography methods • Multiple current injection pairs(source, sink) • Parallelized conductivity search currentsource currentsink measurementelectrodes

  15. Conductivity Scanning and Registration (EGI) • EGI geodesic sensor net integration • Scanning current injection hardware • EEG and bounded EIT data acquisition • EGI photogrammetry system • Machine vision for sensor position registration • EGI tool for validation, correction, and visualization

  16. Directed Components Analysis (R. Frank) • EEG artifacts lead to errors in EEG analysis • EEG artifact identification and removal • Apply signal analysis algorithms (PCA, ICA) • Directed components analysis (DCA) • DCA dynamically models the artifactual and non-artifactual (cortical) activity in the recorded EEG • Artifactual and cortical models facilitate extraction of artifactual activity while preserving cortical activity • Target application to eye blink removal • Computationally efficient implementation permits real-time artifact extraction

  17. EEG Data Window Spatial Filter … … K*EB Topographies (time slices) Blink Intensity … EEG1 EEG2 EEG3 EEGn or Channels (time series) DCA Extraction of Eye Blink Intensity • Inner product of DCA spatial filter and EEG scalp topography at each time sample extracts temporal stream of eye blink intensity … …

  18. BT “Blinky” EEG Blink Intensity = - * Blink - Free EEG DCA Extraction of Eye Blink Artifacts • Extraction is applied to overlapping EEG data windows permitting updating of artifactual and cortical models

  19. Neural ElectroMagnetic Ontologies (NEMO) • How can EEG data be compared across laboratories? • Need a system for representation, storage, mining, and dissemination of electromagnetic information • Need standardization of methods for measure generation and classification of information • Identification and labeling of components • Patterns of interest • NEMO will address issue by providing • Spatial and temporal ontology database • Use for data representation, mining, and meta-analysis • Components in average EEG and MEG (ERPs) • D. Dou, G. Frishkoff, R. Frank

  20. NEMO: Neuro ElectroMagnetic Ontologies

  21. ERP Pattern Analysis and Classification • Extract ERP patterns onto PCA / ICA components 100ms P100 170ms N100 200ms fP2 P1r /N3 280ms scalp data 400ms P1r /MFN 600ms P300

  22. Statistical Metric Generation • Supports Knowledge Discovery through Data (KDD) component for NEMO EEG ontology development • Quantifies attributes of PCA / ICA components • Spatial, temporal and functional attributes • Metrics may combine with expert-defined rules to automatically match components to ERP patterns Measures Truth table expert rules

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

  24. CDS Medical Services Software Layers (C. Hoge)

  25. CDS Work Flow System Architecture

  26. Application Server User Package

  27. Application Server Job Management Package

  28. Support experiment information hierarchy Uses XCEDE Support for workflows MR image analysis Head model building NEMO Supports workflow provenance MySQL DB engine MR/EEG Database Schema (J. Sydes)

  29. Companion Slides

  30. Skull and Brain Anisotropy Parameterization Total number of unknowns for skull conductivity N = 1 + 2N t r • Linear relation betweenconductivity and diffusiontensor eigenvalues • Parameterization:  = K (d - d0) MRI DT brain map (Tuch et al, 2001)

  31. bEIT Procedure 3D subject geometry CT-registered with MRI Individual subject CT is not required Sensor positions are acquired by photogrammetry system and registered with head model Finite difference method solution for particular set of tissue conductivities Search for conductivity solutions

  32. Tissue Conductivity Estimation (Results)

  33. ODESSI (A. Salman) • Open Domain-enabled Environment for Simulation-based Scientific Investigations

  34. Domain Problems in Neuroscience for ODESSI • Scientific investigations • Verification • forward and inverse solvers (4-shell sphere) • grid convergence, time step, special step • Validation • phantoms, animal experiments, data from surgery • Uncertainly and sensitivity analysis • sensor position, injected current, tissue conductivities, … • Parameter sweep • Optimization, extract optimal parameters to fit the data • Comparative analysis • Data management • Geometry MRI/CT, current injection, results

  35. Investigations in Conductivity Modeling • Forward solver parameter tuning • Time-step: controls the speed of reaching the steady state • Convergence tolerance: sets the level of convergence • Geometry resolution error assessment • High resolution (1mm), accurate but inefficient • Low resolution (2mm), efficient but less accurate

  36. Investigations in Conductivity Modeling • Sensitivity Analysis • How the uncertainty in each electrode’s potential can be apportioned to uncertainties to inputs • Potentials at the electrodes is: • Insensitive to variation in CSF tissues conductivity • Highly sensitive to scalp and skull conductivities • Sensitive to brain conductivity

  37. DCA Eye Blink Removal Sequence Cortical topography EEG Eye blink topography EEG Data Model Matrix of eye blink topography (KEB) and cortical eigenvectors (K1 .. KCT) Cortical topography Source model Eye blink topography Source model Spatial Filter Generation Matrix pseudo inverse Extract temporal evolution of eye blink intensity Refine Extracted Eye Blink Intensity Nullspace filtering Frequency filtering Extract Eye Blinks

  38. DCA Derived Spatial Filter DCA spatial filter extracts intensity of activity correlated to the blink topography that is not described by the principal eigenvectors of the cortical EEG covariance matrix KEB: Blink topography (Artifactual Model) KC1, KC2, …, KCT: Cortical eigenvectors (Cortical Model) K*EB, K*C1, …, K*CT: Pseudo-inverse of blink and cortical eigenvectors (Spatial filter: K*EB) Matrix Pseudo - Inverse K*EB Blink Topography + Cortical Eigenvectors K*C1 K*C2 KEB KC1 KC2 … KCT … K*CT

  39. NIC Computational Services Architecture interfaceadaptors

  40. Sub-schema: Experiment Hierarchy / Provenance Experiment Hierarchy Provenance

  41. Sub-schema: Experiment Hierarchy (condensed) Based upon XCEDE

  42. XCEDE • XML-based Clinical and Experimental Data Exchange • Developed by BIRN

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