NA-MIC National Alliance for Medical Image Computing Big Science in Schizophrenia Research Ron...

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


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.


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


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


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


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.


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.


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.


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.


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.


Example: Imaging Research

• Schizophrenia-imaging related research topics– DTI– fMRI


Harvard/MIT: DTI

• Psychiatry Neuroimaging Laboratory

• http://pnl.bwh.harvard.edu


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).


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


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


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


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


Standardization

• Clinical measures• Image acquisition protocol


Technological Infrastructure

• Data Sharing: BIRN• Clinical Measures: fBIRN• Data Analysis: NA-MIC


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


BIRN• Biomedical Informatics Research Network

– Improve Multi-Site Clinical Research

– Calibration– Informatics


mBIRN Federated Database

Cortical Summary Data by Region

Subcortical Summary Data by Region


BIRN Portal

• Web Based– Single Login to BIRN Resources– Intuitive Interface– Flexible to Add Tools– Launch Local Visualization Tools on

Downloaded Data


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


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


sub106.2 BH2 sub106.4 BH2Breath-Holding Task


Comparing Apples and Oranges

Bad News:

Different scanners = different raw images

Good News

The errors are systematic


Results: Images

Average across 5 individuals at same site, same visit


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


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


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’


Structure


MIT

Inward OutwardAtlas-based SegmentationPrinciple Modes of Variation

Train Deformable Model


Someone broke the build!

GE


Isomics


Dissemination: Events


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

NA-MIC National Alliance for Medical Image Computing Big Science in Schizophrenia Research Ron...

Documents

Transcript of NA-MIC National Alliance for Medical Image Computing Big Science in Schizophrenia Research Ron...