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

The Central Neuroimaging Data Archive Supporting Wash U investigators since 2003 Currently holds 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.

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

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

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

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

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

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

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

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

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

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

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

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.

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.

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