Post on 16-Jan-2016
NA-MICNational Alliance for Medical Image Computing http://na-mic.org
Big Science in Schizophrenia Research
Ron Kikinis, M.D.
Professor of Radiology, Harvard Medical School, Director, Surgical Planning Laboratory
Brigham and Women’s Hospital
National Alliance for Medical Image Computing http://na-mic.org
Co-authors
• Ron Kikinis, M.D.1, Tina Kapur, Ph.D.2, Martha E. Shenton, Ph.D.1, Jeffrey S. Grethe, Ph.D.3, Mark H. Ellisman, Ph.D.3
• 1Brigham and Women’s Hospital, Boston, MA, 2Epiphaniymedical, Seattle, WA, 3University of California San Diego, La Jolla, CA
• Acknowledgements: NIH roadmap, NCRR, NIBIB, NCI, NLM, NSF, CIMIT
National Alliance for Medical Image Computing http://na-mic.org
Introduction
• Schizophrenia research is still on the search for a diagnosis based on quantitative methods
• Imaging is complementing clinical assessments with subtle findings that are only significant in group comparisons
• Most schizophrenia research groups have small numbers of subjects and compete fiercely with their peers.
National Alliance for Medical Image Computing http://na-mic.org
Big Science
• Potential Advantages:– Improved signal through larger subject
numbers– Reduced noise through standardization – Potential for new, subtle findings
• Disadvantages– Clinical and technical challenges– No history of large scale collaboration in the
field of schizophrenia research
National Alliance for Medical Image Computing http://na-mic.org
Problems to be Addressed
1. Clinical assessment
2. Patient populations
3. Treatment variations
4. Variations in assessment of genetics
5. Imaging
6. NIH review process
National Alliance for Medical Image Computing http://na-mic.org
1. Clinical Assessment
• Diagnostic and clinical symptom measures often vary across sites
• Solution: Standardization of – patient recruiting, – data acquisition protocols – clinical assessments– fBIRN is working on this problem in the
context of schizophrenia
National Alliance for Medical Image Computing http://na-mic.org
2. Patient Populations
• Patient populations may vary across sites (e.g. chronic patients versus first episode subjects)
• Solution: Homogeneous patient groupings based on:– severity of illness, – duration of illness, – chronic versus first episode, etc.
National Alliance for Medical Image Computing http://na-mic.org
3. Treatment Variation
• Medication practices may be different at different sites – this also may impact findings and needs to be evaluated.
• Solution: Algorithms for the administration of medication need to be agreed upon across multiple-centers.
National Alliance for Medical Image Computing http://na-mic.org
4. Assessment of genetics
• Genetic measures may be evaluated differently across sites.
• Solution: Some consensus or plan is needed across sites regarding specific measurements to be used to evaluate genetic contributions to schizophrenia.
National Alliance for Medical Image Computing http://na-mic.org
5. Imaging
• Imaging techniques may vary across sites and thus some effort would need to be made to ensure compatibility and comparability across sites.
• Solution: Standardize a number of imaging protocols that can be used across sites. A great deal of work has already been done here as part of BIRN and related projects.
National Alliance for Medical Image Computing http://na-mic.org
6. NIH Review process
• How should the peer review process be modified to maximize excellence in science along with big science?
• Solution: R01 grant applications and large science enterprises need to be seen as complementary and not competing with one another.
National Alliance for Medical Image Computing http://na-mic.org
Example: Imaging Research
• Schizophrenia-imaging related research topics– DTI– fMRI
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Harvard/MIT: DTI
• Psychiatry Neuroimaging Laboratory
• http://pnl.bwh.harvard.edu
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Fiber Clustering
Left and Right Fornix Uncinate Fasciculus and Inferior Occipito-Frontal Fasciculus
Splenium of the Corpus Callosum
[O’Donnell L, Kubicki M, Shenton, ME, Dreusicke M, Grimson E, Westin, CF: A Method for Clustering White Matter Fiber Tracts. Am J Neuroradiol (In Press)].
Clustering algorithm: Takes traced fibers (left), extracts features from these fibers (middle), and produces a segmentation based on the similarity (right).
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Population Comparison
Automatic generation of white matter fiber bundles based on shape similarity across subjects.
[O’Donnell, et al., MIT]
Cingulum Bundles Uncinate Fasciculi Corpus Callosum
National Alliance for Medical Image Computing http://na-mic.org
Dartmouth: fMRI
• Andrew J. Saykin, Psy.D., ABPP-CN• John D. West, M.S.• Robert M. Roth, Ph.D.
• Brain Imaging Laboratory• Dartmouth Medical School / DHMC
National Alliance for Medical Image Computing http://na-mic.org
Functional MRI – Behavioral PerformanceVerbal Encoding/Retrieval Episodic Memory TaskPerformance on the In-Scanner Recognition Memory Task
New Words WM Old Words LD Old Words
% C
orr
ect
(ad
just
ed
for
gues
sin
g)
60
70
80
90
100
ControlsPts with Schizophrenia
Interim analysis
National Alliance for Medical Image Computing http://na-mic.org
Functional MRI
FMRI Activation During Continuous Auditory-Verbal Memory (new > old word contrast) in Patients with Schizophrenia
PatientsN = 7
p = .01
Interim analysis
Verbal Encoding/Retrieval Episodic Memory Task
National Alliance for Medical Image Computing http://na-mic.org
Standardization
• Clinical measures• Image acquisition protocol
National Alliance for Medical Image Computing http://na-mic.org
Technological Infrastructure
• Data Sharing: BIRN• Clinical Measures: fBIRN• Data Analysis: NA-MIC
National Alliance for Medical Image Computing http://na-mic.org
Example 1: BIRN CC
• Building of shared infrastructure– Distributed file and database system (SRB)– Managed Authentication System with single
login– Access to compute facilities (teragrid)– Access to shared data
National Alliance for Medical Image Computing http://na-mic.org
BIRN• Biomedical Informatics Research Network
– Improve Multi-Site Clinical Research
– Calibration– Informatics
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mBIRN Federated Database
Cortical Summary Data by Region
Subcortical Summary Data by Region
National Alliance for Medical Image Computing http://na-mic.org
BIRN Portal
• Web Based– Single Login to BIRN Resources– Intuitive Interface– Flexible to Add Tools– Launch Local Visualization Tools on
Downloaded Data
National Alliance for Medical Image Computing http://na-mic.org
Example 2: fBIRN “calibration”
• First large scale attempt at standardization for multi-site fMRI acquisitions.
• Acquistion protocol on equipment from multiple vendors
• Calibraiton phantom for technical level calibration
• Traveling subjects for repeat fMRI studies• Large variabilities found• Second run in preparation
National Alliance for Medical Image Computing http://na-mic.org
Function BIRN Overview• Calibration Methods for Multi-Site fMRI
– Study Regional Brain Dysfunction and Correlated Morphological Differences
– Progression and Treatment of Schizophrenia• Human Phantom Trials
– Common Consortium Protocol– 5 Subjects Scanned at All 11 Sites– Additional 15 Controls, 15 Schizophrenics Per Site
Per Year• Develop Interoperable Post-Processing• UC Irvine, UCLA, UC San Diego, MGH, BWH,
Stanford, UMinnesota, UIowa, UNew Mexico, Duke/UNorth Carolina, MIT
National Alliance for Medical Image Computing http://na-mic.org
sub106.2 BH2 sub106.4 BH2Breath-Holding Task
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Comparing Apples and Oranges
Bad News:
Different scanners = different raw images
Good News
The errors are systematic
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Results: Images
Average across 5 individuals at same site, same visit
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STAPLE Results
• Site vs. Subject– Subject Variability Greater than Site Variability
• Field Strength– 3T and 4T Detect More Activation than 1.5T– 3T and 4T have Less Variability in Sensitivity and
Specificity• Visits
– Less Activation, but More Robust and Systematic Activation in Visit 2 vs. Visit 1
National Alliance for Medical Image Computing http://na-mic.org
Example 3: NA-MIC kit
• NA-MIC aims at developing image analysis technology for – software developers and – medical researchers
• Applications: 3D Slicer• Software methodologies and software tools:
– Multiplatform, nightly builds, automated testing– VTK, ITK
National Alliance for Medical Image Computing http://na-mic.org
NA-MIC: a National Alliance
• 10 entities develop BSD style open source technology: – MIT, UNC, UTAH, Georgia Tech, MGH– GE GR, Kitware, Isomics, UCSD, UCLA
• 4 groups act as driving biological projects and use the technology– BWH, Dartmouth, UCI, Toronto
• Significant outreach activities:– Service, dissemination, training– For example google ‘slicer 101 training’
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Structure
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MIT
Inward OutwardAtlas-based SegmentationPrinciple Modes of Variation
Train Deformable Model
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Someone broke the build!
GE
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Isomics
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Dissemination: Events
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Conclusion
• Pros– “Big Science” done right is a force multiplier– Allows development and adoption of best practices in
research– Faster and higher quality dissemination of new
techniques and of new science• Cons
– Re-education of scientist is necessary and painful– Infrastructure is difficult to explain/justify, because of
long lead times between creation and impact