1. BIRN Advantages in Morphometry  Standards for Data Management / Curation File Formats, Database Interfaces, User Interfaces  Uniform Acquisition and Calibration Human and Non-Human Phantoms, Distortion Correction  Powerful Analysis Registration, Segmentation, Shape Analysis Grid Computing  Multi-Site Statistics Federated Queries, Drill Down  Integrated Visualization and Queries Outlier Review, Developing Scientific Explanations

2. Data Management / Curation  DICOM Standard Image Format Patient Information Fields are Known Standardized Geometry  SQL-based Relational Database Infrastructure Oracle and Postgres  Cross-Platform User Interfaces (Linux, Windows, Sun, Mac) Web-Based, Java, Tcl/Tk, OpenGL

3. Acquisition and Calibration  Consortium Standard Protocol Multi-flip Angle T1 SPGR 1.5T Plans to Extend  T2, DTI, Multiple Field Strengths  Calibration Geometric Calibration Traveling Humans Vendor Gradient Information Distortion Correction  BIRN Advantage: Morphometric Measure Comparable Across Sites Corrected Uncorrected

4. Analysis  Registration (Intensity Based, Automatic) Rigid Registration for Longitudinal Studies Non-Rigid Registration for Populations  Segmentation Assigning Anatomical Labels Based on Registration to Atlas and Image Properties  Morphometrics: Volume and Shape Analysis Compare Individuals Subdivide Populations  Grid Computing Task Level Parallelism – “One Brain Per Processor” Large Memory Models and Fine Grained Parallelism

5. AD Analysis Example  Alzheimer’s Disease Patients, Control Subjects, and At-Risk Individuals Scanned at BWH/MGH and UCSD with Common Protocol  Raw and Derived Data Uploaded BIRN Federated Data Grid  Mediated Queries Across Sites through BIRN Portal Identify Subject Populations  Univariate and Bivariate Statistics Web-Based Tools  Drill-Down to Individual Subjects Download for Visualization and Query Normal Elderly Control Alzheimer’s Individual

6. MIRIAD Analysis Example  Deidentified Data from Duke Retrospective Archive Loaded in BIRN Data Grid  UCLA LONI Pipeline Register Probabilistic Anatomy Atlas to Subjects Lobar Analysis  BWH/MIT 3D Slicer Image Analysis and Segmentation  UCSD Supercomputers Cluster Processing  Statistical Analysis Detailed Clinical Database UCSD Supercomputing Duke Archives UCLA AIR Registration and Lobar Analysis BWH Intensity Normalization and EM Segmentation Duke Clinical Analysis MIRIAD Data Flow 1) Retrospective data upload from Duke 2) Lobar analysis and Registration of Atlas to Subjects 3) Anatomical Segmentation 4) Comparison to Clinical History 1 2 3 4 BWH Probabilistic Atlas (one time transfer)

7. MIRIAD Segmentation Comparison Original images: PD, T2 weighted Duke semi-automated MIRIAD BWH, UCLA Automated Pipeline

8. MIRIAD Analysis Comparison Duke AloneBIRN Advantage Tissue Classes323 Operator InteractionSupervise Each BrainFully Automated Time for 200 Brains400 Hours1 Hour (Parallel Computing) Time for Lobe Analysis (e.g. temporal) 200 Hours / LobeIncluded Time for Regional Analysis (e.g. orbital- frontal cortex) 50 Hours / RegionIncluded Statistical SignificanceGross tissueDetailed Anatomy

9. MIRIAD Initial Results--Lobar  Analysis carried out by normalizing regions by total brain volume  50 depressed, 50 controls, imaged at baseline and 2 years  Parietal lobe smaller in depressed (p < 0.02)  In subjects responding to therapy: Temporal lobe smaller (p < 0.08); Frontal lobe was not smaller (p < 0.6)  This is the first study to show brain structural change over time in response to treatment in unipolar depression

10. SASHA Analysis Example MGH Freesurfer Cortical & Subcortical segmentations BIRN SRB Database WUSTL AD Subject Acquisition BWH 3D Slicer Visualization JHU Large Deformation Diffeomorphic Metric Mapping Shape Analysis Method: Integrate Computationally Demanding Morphometric Processing and Visualization Tool BIRN Advantage: Grid Computing Infrastructure Allowed Migration to NSF TeraGrid Resources for Large Scale Studies Goal: Study Subtle Morphometric Changes in Large Clinical Populations SASHA: Semi- Automated Shape Analysis

11. Federated Statistics  Identify Subject Populations Portal Interface  Multi-Site Studies Univariate and Bivariate Analyses Clinical Measures, Demographics, Raw Image Characteristics, Derived Data from Analyses  Drill Down to Subgroups and Individuals Review Supporting Data  BIRN Advantage Integrated with Multi-Site Acquisition and Processing

12. Visualization  Reviewing Raw and Derived Data Possible Technical Errors or Other Reasons for Outliers  Performing Interactive Image Analysis Operations Defining Regions of Interest Review and Guidance of Semi-Automated Procedures  Integrated View of Analysis Data View Regional Analysis Results in the Anatomical Context  Support Development of Scientific Explanations Query Atlas to Use Anatomical Labeling of Statistically Identified Regions as the Link to Wider Data Searches

13. Visualization / Query Atlas  Search Terms Automatically Determined From Analysis Results and User Interaction  Search Literature and Other Databases Support Development of Scientific Explanations  BIRN Advantage: Well-Curated BIRN Data Allows Large-Scale Cross- Database Integration Taxonomy/Homology Gene Expression Web Literature, etc Protein Localization