1. 2005 All Hands Meeting Multi-Site Alzheimer’s Disease Project a.k.a. “MAD” Project Leaders: C. Fennema-Notestine, R. Gollub, B. Dickerson

2. MAD Project Goals  Pooling datasets to increase statistical power for studying relatively rare populations or subtle neuroanatomic changes  Replicate single site results using pooled data to validate methods (Project #1)  Extend morphometric capabilities to novel observations requiring pooled samples (Projects #2&3)  Provide benchmark data sets for the use of BIRN developers for testing and validation  e.g., Freesurfer subcortical segmentation, 3D Slicer, HID, XNAT

3. Achievements since 10/04  Data sharing agreement among collaborating sites: UCSD, MGH/BWH, Wash U, and UCI.  Minimal dataset requirements for sharing Diagnosis (& how generated) Inclusion/exclusion criteria Age, education, sex Mini-mental state exam score (MMSE)  All data processed with most recent version of FreeSurfer subcortical segmentation and common atlas (MGH/Fischl)

4. Achievements cont.  Coordinated image and clinical data sharing UCSD and MGH data combined HID storage Added Wash U site data UCI data currently being processed  Data employed in development and testing of HID, defacing, BIRN-DUP, SRB, mediated query tools  Collaborating with BWH/3DSlicer development of QA tool for review of final datasets  Three research projects underway resulting in conference abstracts and ultimate publication 

5. Project #1: Morphometric Measurements in Healthy Elderly Controls (C. Fennema-Notestine, R. Gollub, B. Dickerson) Normal Elderly Control Society for Neuroscience 2005

6. Project #2: Clinico-anatomic Relationships  Replicate previous work in aging and AD  2 sites with similar memory measures (UCSD and MGH)  Relationship between visual and verbal memory and right and left hippocampal volume (B. Dickerson, C. Fennema-Notestine, R. Gollub)

7. Project #3: Diagnostic Classification Healthy Elderly vs. AD  Determine participant classification using hippocampus, amygdala, ventricular volumes, & MMSE  2 sites with control and AD patients (UCSD and WashU)  Linear and quadratic discriminant analysis applied.  Classification success rate on test data can approach 90%. (C. Roddey, A. Dale, C. Fennema-Notestine, R. Gollub)

8. AHM 2005 Discussion Items - Morphing MAD  Should we extend existing project? E.g., examine diagnostic specificity of classification using morphometric results from depressed elderly with pseudodementia If so, what other datasets are available for study?  Should we embark on an entirely new clinical investigation (brain or beyond)? Examine the outcomes of fully automated segmentation or other image processing outputs?  Continue dual purpose to advance clinical knowledge and to provide substrate for testing newly developed BIRN tools

9. Goals for 10/06  Complete and publish quantitative study describing methodology and feasibility of pooling legacy MR data across sites (Neuroinformatics) – 3m  Complete explorations of clinico-anatomic relationships between UCSD and MGH data – 6m  Complete and publish diagnostic classification work – 6m  Descriptive exploration of MCI and AD patient data – 6m  Repository for legacy data to be used for in-house BIRN infrastructure and tool development – 6m  MAD morphed into new goals to be examined and refined for mBIRN AHM – 6m