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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
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)
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
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