Distributed Computational Architectures for Integrated Time-Dynamic Neuroimaging

Distributed Computational Architectures forIntegrated Time-Dynamic Neuroimaging

Dr. Allen D. Malony

[email protected]

Computer & Information Science DepartmentComputational Science Institute

CIBER

University of Oregon

April 22, 2023 HBP Neuroinformatics Conference

Outline

Computational science and cognitive neuroscience Brain dynamics analysis problem

integrated electromagnetic analysis system Motivating case studies

observations Computational architectures

models and technology key ideas

Neuroinformatics GRID Final Thoughts


Computational Science & Cognitive Neuroscience

Computational methods applied to scientific research high-performance simulation of complex phenomena large-scale data analysis and visualization

Understand functional activity of the human cortex multiple cognitive domains multiple experimental paradigms and methods

Need for coupled/integrated modeling and analysis electrical and magnetic, cortical and theoretical

Need for robust tools: computational, informatic

Problem solving environment for brain analysis


Brain Dynamics Analysis Problem

Identify functional components in cognitive contexts Interpret with respect to cognitive theoretical 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) potential to map electrical activity to cortex surface


Electromagnetic Analysis Methodology

Multi-trial analysis signal analysis and response analysis averaging across subjects and trials

distortion (smearing) of estimated source response noise artifacts, signal variation (individuals, trials) improvements: artifact removal, selective averaging

create component response models factor analysis: PCA, ICA error in source factors: variability, statistics

Multi-subject and single-subject analysis quantify differences of individual from population


Single-Trial Analysis Capability

Improve fidelity of single-subject response model higher information content than multi-trial/subject reduce analysis error from trial/subject variability knowledge of subject population, stimulus deviations

Diagnosis (identification) of cognitive state known stimulus blind stimulus match response to known component response model

Problems greater noise greater complexity


Single-Trial Analysis Methodology

Integrate methods for analyzing brain dynamics Improve resolution and robustness of techniques

increase measurement density (128 to 256 channels) Coupled modeling: constraints and cross-validation

component response model cortical activity model tuned models for single individual

Build models in experimental paradigm context Match single-trial measurements to models

known stimulus multiple trial models blind stimulus multiple stimulus/trial models

Training and learning


Integrated Electromagnetic Brain Analysis

Single-trialAnalysis

Structural /Functional

MRI

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

EEGMEG


Case Study: Readiness Potential

Self-paced button pressing task slow negative shifts in potential contralateral to hand

Single subject examination multi-trial (150 trials) averaged ERP analysis

Dense-array scalp electrical measurement 129 electrode array (EGI Geodesic Sensor Net)

Modeling of brain electrical activity MRI and CT data analysis with tissue segmentation realistic boundary element meshes (2K ’s for brain) source localization

Can ERP analysis accurately localize cortical activity?


processed EEG

Experimental Methodology

BrainVoyager

EMSE

CT and MRI

Interpolator 3D

NetStationEEG segmented

tissues

mesh generation,source localization


Electrical Activity of Scalp and Brain

Expected brain activity Correlated with fMRI

experimental studies Topographic and cortex

mapped spatial analysis

-404 ms -56 ms 0 ms 160 ms

Lateralize Readiness Potential (LRP)


Case Study: Self-Monitored Motivated Action

Learning task with feedback (Gehring et al. (1993)) left- or right-hand button press response "incorrect" feedback on error "OK" or “late” feedback if correct timed expectancy and motivated response

Error-Related Negativity (ERN) large medial negative response on error self-monitoring when motivated action goes wrong

What is the nature and complexity of the ERN withrespect to dynamic components of brain activity?


Cognitive Experiments and Brain Dynamics

Visualize the dynamic operations of brain Example: fMRI blood flow response to reading a word Dense-array EEG / MEG frontal lobe activity (ERN)

significant changes in milliseconds frontal oscillations and separate time courses

BrainVoyager


ERN Analysis using ICA (Makeig, Salk Institute)

Average analysis smears temporal/spatial dynamics Single-trial analysis may expose greater detail Independent Components Analysis (ICA)

find independent EEG component contributors temporal and spatial components accounting for artifacts components accounting for functional sources (ERN)

analysis over single trials Two components account for averaged ERN

response-locked ERN difference wave dominated show temporal and functional independence


ERP and Component Envelopes (Left/Correct)

Component #2

Component #7

• Complementary behavior

• Both active at strongest ERN channels


ERPs averaged across response hand

neither C2nor C7 explainthe waveforms

component sumdoes explain the waveforms and showsERN response


Topographic Imaging and Dipole Modeling

Component #2 Component #2

Averaged ERN

Brain ElectricalSource Analysis

(BESA)


ICA Component #2 Dynamics

Stimulus locked


ICA Component #7 Dynamics

Phase reset byresponse, largestafter incorrect


Case Study Observations

Diverse set of tools function and implementation separate and not integrated incompatibilities and limitations for interoperation

Complex analysis processes scientific discovery through integrated techniques heterogeneous, flexible, extensible capabilities increasingly high computational demands high-level process methodology

Multiple, interdisciplinary scientific domains


High-Performance Computational Environments

Integrated database, analysis, and visualization Distributed tool infrastructure

diverse tools across multiple platforms interoperation requirements user interaction requirements support portability, flexibility, extensibility

Scalable, high-performance parallel computing increase data resolution minimize solution time

High-level access to tools web-based access


Computational Systems: Models and Technology

Domain-specific, problem-specific environments (PSE) TIERRA

Scientific “workbench” SCIRun

Programming environments numerical frameworks

POOMA application coupling

PVM / MPI CUMULVS PAWS SILOON / PDT

Metacomputing / GRID Legion Globus

Heterogeneous distributed computing / coupling NetSolve INTERLACE HARNESS

Web-based environments ViNE PUNCH VNC


SCIRun (Johnson, University of Utah)

Scientific programming environment large-scale simulations “computational workbench” visual programming interface dataflow model of computing

modules: operation or algorithm with I/O ports network: set of modules and their interconnections widgets: 3D user interaction

data types: Mesh, Surface, Matrix, Field, Geometry extensible module library computational steering


SCIRun User Interface

Visual programming lets users select, arrange, and connect modules into a desired network

Interactive steering of design, computation, and visualization allows more rapid convergence


ICA for EEG Source Localization with SCIRun

PCA decomposition forEEG signal/noise subspaces

ICA activity map separationon signal subspace

Solution to a single dipolesource forward problem underlying model is shown

in the MRI planes dipole source is indicated by red and blue spheres electric field visualized by cropped scalp potential

map and wire-frame equipotential isosurface


PDT (Malony, University of Oregon)

Program Database Toolkit Program analysis

multi-language(Fortran, C,C++, Java)

commercial-grade parsers

IL to programdatabase (PDB)

API for PDBaccess / query

Tools: instrumentation, code wrapping, documentation


SILOON (Advanced Computing Lab, LANL; UO)

Scripting Interface Language for OO Numerics Toolkit and run-time support for building easy-to-use

external interfaces to existing numerical codes Scripting language to “glue” components together


Metasystems and Metacomputing

Many resources accessible on the internet computers, data, devices, people

Extend single system model to internet domain wide-area (department, campus, region, country) scalable, transparent access to resources hides network complexity (“as if on your machine”)

Extend computing model to internet domain shared persistent space of objects (data, execution) heterogeneous distributed and parallel processing meta-applications (multi-component, hierarchical)

Deal with complex environment / primitive tools


Characteristics of Meta-applications

Multiple components programs, databases, instruments, devices

Different authors Different languages Different applications

legacy, COTS, ... coupled modeling

Parallelism internal: task/data external: components


“The GRID”

New applications based on high-speed coupling of people, computers, databases, instruments, ... computer-enhanced instruments collaborative engineering browsing of remote datasets use of remote software data-intensive computing very large-scale simulation large-scale parameter studies


GRID Architectural Picture


GRID Technical Challenges

Complex application structures, combining aspects of parallel, multimedia, distributed, collaborative computing

Dynamic varying resource characteristics, in time and space

Need for high and guaranteed “end-to-end” performance, despite heterogeneity and lack of global control

Inter-domain issues of security, policy, payment


NetSolve (Dongarra, University of Tennessee)

Client-server systemto access distributedcomputational / DBHW/SW resources

Distributed computing:resources, processes,data, users

Load-balancing policy for efficiency / performance Integration with arbitrary software components

C, Fortran, Java, MatLab, Mathematica, Excel BLAS, (Sca)LAPACK, MINPACK, FFTPACK


NetSolve – 1999 R&D Winner


NetSolve Usage

“Blue collar” GRID-based computing users can set things up (without “su” privileges) no deep network programming knowledge required

Scenarios clients, servers, and agents anywhere on Internet clients, servers, and agents on an Intranet clients, servers, and agent on the same machine

Focus on MATLAB users OO-style language (objects are matrices) one of most popular desktop systems for numerical

computing (> 400K users)


NetSolve – The Client

NetSolve API hides complexity of numerical software Computation is location transparent Provides access to virtual libraries:

Component GRID-based framework Central management of library resources User not concerned with most up-to-date versions Automatic tie to Netlib repository

Synchronous or asynchronous calls User-level parallelism


Agent gateway to computational services performs load balancing and resource management

Server various software installed on various hardware configurable and extendable framework to easily add software many numerical libraries being integrated supports parallel computing

NetSolve – The Agent and Server


Using MCell with NetSolve


MCell (Bartol, Salk Institute; Salpeter, Cornell)

Monte Carlo simulator of cellular microphysiology Study how neurotransmitters diffuse and activate

receptors in synapses between different cells NetSolve distributes

processing workloadand allows access tocomputational resources

Simultaneous evaluationof large number ofdifferent parametercombinations


ViNE (Malony, University of Oregon)

Virtual Notebook Environment High-level, shared

notebooks, data, andtools in distributed,heterogenous system

Architecture leaves: notebook

functions and data stems: notebook

communication Web-based access


ViNE Experiment Builder

List of available, named data, tools, and experiments Visual dataflow model of experiment process Wrapped tools and databases

wrappedMATLAB

“tool”


Brain Electrophysiology Lab Notebook

Dense array EEG datasets

Commercial of the shelf statistical and numerical packages

Multiple machines types

Notebook content automatically generated from experiment results


PUNCH

Purdue University Network-Computing Hubs Educational and research computing “portals”

across the Purdue “enterprise” with affiliated institutions

Resource sharing by Purdue users computers, software, laboratory equipment educational materials

Distance education allows sharing of courses and instructors

Collaborative research


PUNCH – User’s and Developer’s View

Set of network-based laboratories that provide software tools for various fields

Specialized WWW-server interfaces WWW-browsers access software and download data run tools and view results

Tool specification Virtual laboratory

developmentenvironment


PUNCH Web Page

Hubs


PUNCH Software Components


PUNCH Across the Internet


PUNCH Tool Display Support via VNC

MATLABcommandwindow

X Windowsdisplay

MATLABinteractive

window

MATLABgraphicswindow


Remote access to graphical user interfaces VNC “thin client” protocol

based on concept of remote frame buffer server updates a frame buffer displayed on a viewer

OS independent: Unix, Linux, MacOS, Windows, PDA Communications independent – reliable transport

Virtual Network Computing (VNC)


VNC Clients

X (Windows)

Mac-IE (Windows) Mac-IE (X)

PDA (X)

Mac (X) X-NS (Windows)


KEY IDEAS Problem-solving environments

domain specific support for the entire process

Programming environments numerical programming framework encapsulated parallelism application / tool coupling data exchange / interaction support high-level API’s / data support support for application interaction control support for application code wrapping


KEY IDEAS

Scientific “workbench” integrated application development environment “component-based” application programming high-level data objects

Metacomputing / GRID metasystems infrastructure / services metacomputing applications programming GRID resources


KEY IDEAS

Heterogeneous distributed computing high-level numeric computational services access to metasystem resources wrapping/linking of computational engines dynamic, adaptable, extensible high-level metasystems programming support

Web-based environments web-based access to tools web-based applications development web-based data, results, process management


Neuroinformatics GRID

Distributed network of labs and researchers share experimental data in a timely manner access research results as it becomes available establish and maintain cooperative relationships

Informatics hub of neuroimaging community on-line archive of “raw” electrophysiological data extension for experimental and reference meta-data tools for database query and dataset generation tools for data analysis, visualization, experimentation interactive research discussion forums

WWW, distributed computing, database technology


Final Thoughts

Enable high-level problem solving environments Tools to enable scientists to compose solutions from

a set of building blocks Seamless access to local and remote resources Enabling infrastructure

framework standards and interfaces implementations of reusable components

Collaboration environments Opportunity to create Neuroinformatics GRID

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Distributed Computational Architectures for Integrated Time-Dynamic Neuroimaging

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