Test Neuroimaging Workflow Test Neuroimaging Workflow

Optimization TechniquesOptimization Techniques

Stephen Strother, Ph.D., Rotman Research Institute, Baycrest Centre

& Medical Biophysics, University of Toronto

Rotman Research Institute/University of TorontoRotman Research Institute/University of Toronto

Xu Chen, Ph.D., Cheryl Grady, Ph.D., Simon Graham, Ph.D., Wayne Lee, B.Eng., Randy McIntosh, Xu Chen, Ph.D., Cheryl Grady, Ph.D., Simon Graham, Ph.D., Wayne Lee, B.Eng., Randy McIntosh, Ph.D., Anita Oder, B.Sc., Imran Somji, B.Sc., Don Stuss, Ph.D.Ph.D., Anita Oder, B.Sc., Imran Somji, B.Sc., Don Stuss, Ph.D.

KLARU & Brain Health Clinics/University of Toronto, KLARU & Brain Health Clinics/University of Toronto, Jon Ween, Ph.D.

Multiple Collaborators: Multiple Collaborators: University of Minnesota, USA; Danish Technical University, Copenhagen

Principal Funding Sources:Principal Funding Sources: NIH Human Brain Project, P20-EB02013-10 & P20-MH072580-02, James NIH Human Brain Project, P20-EB02013-10 & P20-MH072580-02, James S. McDonnell Foundation, Heart & Stroke Foundation of Ontario, Lundbeck Foundation DenmarkS. McDonnell Foundation, Heart & Stroke Foundation of Ontario, Lundbeck Foundation Denmark

OverviewOverview

Example: Functional MRI (fMRI) workflows = Example: Functional MRI (fMRI) workflows = pipelinespipelines

Why optimise fMRI workflows?Why optimise fMRI workflows?• don’t the domain experts know what they are doing with fixed don’t the domain experts know what they are doing with fixed

pipelines?pipelines?• interactions with ageinteractions with age• lack of replication across major development groupslack of replication across major development groups

A resampling workflow optimisation frameworkA resampling workflow optimisation framework The components we envisage usingThe components we envisage using Some integration challengesSome integration challenges

MRI and fMRI Images

MRI fMRI

one image

many images (e.g., every 2 sec for 5 mins)

high resolution(1 mm) low resolution

(~3 mm but can be better)

MRI (3D)

T1 based on proton precession decay

fMRI (4D) Blood Oxygenation Level Dependent (BOLD) signal

indirect measure of neural activity

…

Source: Jody Culham, fMRI for Newbies, UWO

Statistical Mapsuperimposed on

anatomical MRI image

~2s

Functional images

Time

Condition 1

Condition 2 ...

~ 5 min

Time

fMRISignal

(% change)

ROI Time Course

Condition

Activation StatisticsActivation Statistics

Region of interest (ROI)

Source: Jody Culham, fMRI for Newbies, UWO

fMRI Processing PipelinesfMRI Processing Pipelines

ReconstructedfMRI Data

B0 Correction

Slice TimingAdjustment

MotionCorrection

Intensity Normalization

Spatial & TemporalFiltering

Statistical AnalysisEngine

StatisticalMaps

Some Preprocessing

Steps

ExperimentalDesignMatrix

Rendering of Results on Anatomy

Data Modeling/Analysis

Intensity Normalization with Age Intensity Normalization with Age

# Subjects

The Functional Image Analysis CompetitionThe Functional Image Analysis Competitionz=-12 z=2 z=5

3

1,4

21

3 3 31

3

The main effects of sentence repetition (in red) and of speaker repetition (in blue). 1: Meriaux et al, Madic; 2: Goebel et al, Brain voyager; 3: Beckman et al, FSL; and 4: Dehaene-Lambertz et al, SPM2.

Poline JB, et al. (2006) Hum Brain Mapp 27(5):351-9

Optimisation Metrics Literature ROC p-values AIC, BIC

Statistical Resampling- Prediction- Reproducibility

fMRI Processing PipelinesfMRI Processing Pipelines

ReconstructedfMRI Data

B0 Correction

Slice TimingAdjustment

MotionCorrection

Intensity Normalisation

Spatial & TemporalFiltering

Statistical AnalysisEngine

StatisticalMaps

Some Preprocessing

Steps

ExperimentalDesignMatrix

Rendering of Results on Anatomy

Data Modeling/Analysis

Consensus-Model ROC ResultsSimple Signal Complex Signal

Hansen LK, Nielsen FA, Strother SC, Lange N. Consensus Inference in Neuroimaging. Neuroimage 13:1212-1218, 2001

Prediction and Reproducibility Prediction and Reproducibility (Split-Half Cross-Validation Resampling)

Prediction Metric

Standard SPM Estimation

LaConte et al., (2003) Neuroimage 18(1):10-27

ROC-Like: Prediction vs. Reproducibility ROC-Like: Prediction vs. Reproducibility

LaConte et al., (2003) Neuroimage 18(1):10-27

Subject-Specific Pipeline OptimizationSubject-Specific Pipeline Optimization

Shaw ME, et. al., Neuroimage 19:988-1001, 2003

Optimisation Metrics Literature ROC p-values AIC, BIC

Statistical Resampling- Prediction- Reproducibility

fMRI Processing PipelinesfMRI Processing Pipelines

ReconstructedfMRI Data

B0 Correction

Slice TimingAdjustment

MotionCorrection

Intensity Normalization

Spatial & TemporalFiltering

Statistical AnalysisEngine

StatisticalMaps

Some Preprocessing

Steps

ExperimentalDesignMatrix

Rendering of Results on Anatomy

Data Modeling/Analysis

Automated Software Frameworks

BIRN / NeuBase / FisWidgets

fBIRN:Federated DB FrameworkfBIRN:Federated DB Framework

D. Keator et al., (2007) A National Human Neuroimaging Collaboratory Enabled By The Biomedical Informatics Research Network (BIRN). IEEE Information & Technology in Biomedicine - BioGrid special edition (in press)

Function BIRN DB ToolsFunction BIRN DB Tools

Particular focus on XCEDE XML Schema for fMRI

BackupSystem

Data AnalysisPipeline

MRI

StudyWork

Station

BVL

DCC

INTE

RN

ET

MRI

BVLBVL

MRI

PSC

BehavioralPC

(laptop)

MRI Console

MRIScanne

r

ScientificCommunity

Mass Storage System

Internet &

DBMSServer(s)

Data Marts

DataWarehouse

NEUBASE BIC/MNI Network Architecture

Courtesy: A. Evans et al., Brain Imaging Centre, MNI

Candidate Profile candidatefor eachPSCID

DCC-ID

identified by

bio

exclus

brief int

full int

figs

disc

dps4

cbcl

apib

carey

hand

nepsy

das

neuro

pls3

pregn

tanner

wasi

wj3

bayley

cantab

cvltc

cvlt2

jtci

psi

purdue

saliva

urine

wisc

waisr

behavioral battery of instruments

DICOM

MINC

header

T2W3D

MRS

MRSI

PDT1W3D

MRI procedures

areidentified

by

Objective ObjectiveID

Type Screening

containsdata on multiple

visits

visit

stores data for a battery of administered MRI procedures

& behavioral instruments

ethnic

member of

EthnicID

SessionID

VisitNo

Objective

Age

Test ID ScoreID

CommentID

TestID

recruited by

psc

CenterID

DoB

Gender

personal

Weight

Height

Courtesy: A. Evans et al., Brain Imaging Centre, MNI

Storage Resource Broker/BIRNStorage Resource Broker/BIRN

Welcome to the BIRN/SRB Space!

The Storage Resource Broker (SRB), developed at SDSC, is a client-server middleware designed for managing file collections in a heterogeneous, distributed environment.

All files within the environment are part of a single data grid file system where a file’s logical location within the file system is represented independently of its physical location.

The SRB middleware is capable of managing large data sets and is currently managing the BIRN data grid.

OpenSource Workflow ManagerOpenSource Workflow Manager

Other Neuroimaging SolutionsNEUBASE – MNI (Opening, linked to BIRN)XNAT – Wash. Uni. St. Louis (Opensource, linked to BIRN)FIPS – MGH/MIT (Opensource, part of fBIRN)LONI – UCLA (proprietary → opening, linked to BIRN?)

General Workflow Solutions, e.g., KEPLER

Java-based Code DevelopmentJava-based Code Development

ChallengesChallenges are we trying to integrate a viable set of components?are we trying to integrate a viable set of components?

• relying on DB, grid & web-service computing expertise of collaborators relying on DB, grid & web-service computing expertise of collaborators (NEUBASE:MNI, fBIRN & BIRN-CC/SRB) (NEUBASE:MNI, fBIRN & BIRN-CC/SRB)

knowledgeable people to integrate & manage resourcesknowledgeable people to integrate & manage resources• partially resolved by remote web management but hiring remains difficultpartially resolved by remote web management but hiring remains difficult

time to test and debug resource in research environmenttime to test and debug resource in research environment• getting neuroimaging researcher buy ingetting neuroimaging researcher buy in• must work somewhat reliably when rolled outmust work somewhat reliably when rolled out

remember that the first attempt to build an archive of raw remember that the first attempt to build an archive of raw and processed published fMRI data sets in the US failedand processed published fMRI data sets in the US failed• fMRI Data Center (Dartmouth College)fMRI Data Center (Dartmouth College)• sociologic and funding reasons for failuresociologic and funding reasons for failure

image and meta-data formatsimage and meta-data formats• MINC2.0, (multiframe)DICOM, NIfTI1.1(2?)MINC2.0, (multiframe)DICOM, NIfTI1.1(2?)• what about MedX3D?what about MedX3D?

AcknowledgementsAcknowledgements

Rotman Research Institute/University of TorontoRotman Research Institute/University of Toronto

Xu Chen, Ph.D., Cheryl Grady, Ph.D., Simon Graham, Ph.D., Wayne Lee, B.Eng., Xu Chen, Ph.D., Cheryl Grady, Ph.D., Simon Graham, Ph.D., Wayne Lee, B.Eng., Randy McIntosh, Ph.D., Anita Oder, B.Sc., Imran Somji, B.Sc., Don Stuss, Ph.D.Randy McIntosh, Ph.D., Anita Oder, B.Sc., Imran Somji, B.Sc., Don Stuss, Ph.D.

KLARU & Brain Health Clinics/University of TorontoKLARU & Brain Health Clinics/University of TorontoJon Ween, Ph.D.

Multiple CollaboratorsMultiple Collaborators

University of Minnesota, USAUniversity of Minnesota, USA

Danish Technical University, CopenhagenDanish Technical University, Copenhagen

Principal Funding Sources:Principal Funding Sources: NIH Human Brain Project, P20-EB02013-10 & P20- NIH Human Brain Project, P20-EB02013-10 & P20-MH072580-01, James S. McDonnell Foundation, Heart & Stroke Foundation of MH072580-01, James S. McDonnell Foundation, Heart & Stroke Foundation of Ontario, Lundbeck Foundation DenmarkOntario, Lundbeck Foundation Denmark

Simple Motor-Task Replication at 4.0TSimple Motor-Task Replication at 4.0T

t-test Fisher Linear Discriminant = 2-class CVA

L R

C. Tegeler, S. C. Strother, J. R. Anderson, and S. G. Kim, "Reproducibility of BOLD-based functional MRI obtained at 4 T," Hum Brain Mapp, vol. 7, no. 4, pp. 267-83, 1999.

Physiological Correction InteractionsPhysiological Correction Interactions

Tegeler C, et al., Hum Brain Mapp 7(4):267-83, 1999

NIfTI-DFWG-NIfTI-1.1NIfTI-DFWG-NIfTI-1.1

NIfTI-DFWG-NIfTI-1.1NIfTI-DFWG-NIfTI-1.1