1. NA-MIC Experience Familiar with DTI algorithms and datasets: universal recipient –HUVA, Vetsa, Dartmouth, Susumu JHU datasets –Experience with Slicer and DTI Studio; familiar with Gerig tools and FreeSurfer –GE, Siemens and Philips scanner –Various acquisition sequences

2. National Alliance for Medical Image Computing – http://na-mic.org DLPFC 3D models: Manual (Left), Semi-Automatic (Right) Georgia Tech, UC Irvine, Kitware DLPFC Semi-automatic segmentation Expert rules in segmentation framework From 45 to 5 minutes Validation (n=10): >70% DICE overlap with pure manual Module in Slicer R. Al-Hakim, J.Fallon, D. Nain, J. Melonakos, A. Tannenbaum. A DLPFC semi-automatic segmenter. In SPIE Medical Imaging, 2006.

3. National Alliance for Medical Image Computing – http://na-mic.org DTI Tractography Validation Identify 11 major tracts with Slicer Study repeatability and interrator variability Goal: standardization of DTI analysis UC Irvine

4. National Alliance for Medical Image Computing – http://na-mic.org MOG vs Total Brain White Matter Sample: Dr. Honer UBC – 47 schiz, 24 cont Phenotype: automated output from standard structural MRI – total grey and white matter MRI=> C1334T marker genotype associated with white matter volume (P=0.003) Other MOG markers negative All MOG markers negative for total grey matter volume

5. National Alliance for Medical Image Computing – http://na-mic.org Myelin Associated Glycoprotein Associated with White Matter Volume in Psychosis Cases – MAG rs720309 (T/A) p=0.016 P = 0.016 p=0.016

6. National Alliance for Medical Image Computing – http://na-mic.org EXTRACTING DATA FOR ANALYSIS Data are returned in a format suitable for association-type studies (m-link or case- control). Additional formats may be designed as needed (such as vertical haplotypes { } ). Data may be transcribed and converted to document formats supported by the analysis program (tab de-limited text, etc…). 1 2 2 1 1 2 2 2 2 1 1 1 2 2 1 1 With access to source codes, or by invoking special features in downstream applications, the database can include automated running of analyses or transfer of data to other spreadsheets/databases.

7. NA-MIC Experience Lack of standardized system for acquiring and analyzing data: –Time course unrealistic to establish technical expertise at our site –Turnover of masters, grad student & postdoc q 2 years meant the projects were not seen to completion –Different expectations E.g. rules for DLPFC and frontal lobe were proof of concept but not a tool for research use Timeline did not allow continuation of projects and funding Difficult to remain a priority, too dependent of Cores 1 & 2 –Have established collaborative network but need funds –Training geneticists in imaging –Lead to new visualization tools & K01 on using DTI