1. Informatics for the Neuroimaging Research Enterprise Dan Marcus Washington University NITRC Enhancement Grantee Meeting Monday, June 30, 2008

2. The Central Neuroimaging Data Archive Supporting Wash U investigators since 2003 Currently holds 25000 MR, PET and CT scans from over 5000 individual studies ~100 active users from two dozen labs Supports all of the Univ.’s imaging facilities and many of its research centers.

3. Defining the enterprise Lab Stakeholders: Principal investigator, students, postdocs, research techs.

4. Defining the enterprise Center Lab Stakeholders: Director, scanner facility, IT department, human studies

5. Defining the enterprise Center Lab Center Lab Center Lab Multisite collaboration Stakeholders: study PI, individual PIs, research cores, coordinating center

6. Defining the enterprise Labs: Focused on data & analysis Centers: Focused on operations & oversight Multisite studies: Focused on technical & scientific coordination and logistics

7. Defining informatics: Data Capture NEUROIMAGING GENETICS OTHER SOURCES Integrity: Do I have the data? Quality Control: Are the data any good?

8. Defining informatics: Local Use NEUROIMAGING GENETICS OTHER SOURCES Application: Can I do things with the data? Automation: Am I optimizing throughput?

9. Defining informatics: Collaboration NEUROIMAGING GENETICS OTHER SOURCES Access: Are colleagues getting the data they need? Security: Are colleagues getting data they shouldn’t?

10. Defining informatics: Public access NEUROIMAGING GENETICS OTHER SOURCES Privacy: Am I respecting the rights of the study participants? Convenience: How usable are the data?

11. QUARANTINELOCAL USECOLLABORATIONPUBLIC ACCESS CAPTURE NEUROIMAGING GENETICS OTHER SOURCES The XNAT workflow Quality control Data archiving Data access Security Visualization Automation Integration Data sharing

12. Lessons learned: stakeholders Identify the stakeholders and their personalities – The Micromanager – The Empire builder – The Outsourcer – The Benefactor N investigators ≠N databases

13. Lessons learned: budgets Hardware costs will be over budgeted. Personnel costs will be under budgeted.

14. Lessons learned: personnel Hire software engineers. Good Java programmers are rare. Good Java programmers who will work for what you want to pay them? Forget about it. There are no rules. Except: your software engineering team is your most important asset.

15. Lessons learned: software engineering Use the least possible technology. Half the features. Twice the usability. Compliance issues (HIPAA, IRBs, IT security) becomes increasingly burdensome Open source is your friend.

16. Lessons learned: data Remain agnostic to formats Except DICOM. Drink the Kool-Aid.