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

National Alliance for Medical Image Computing – 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.

National Alliance for Medical Image Computing – DTI Tractography Validation Identify 11 major tracts with Slicer Study repeatability and interrator variability Goal: standardization of DTI analysis UC Irvine

National Alliance for Medical Image Computing – 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

National Alliance for Medical Image Computing – Myelin Associated Glycoprotein Associated with White Matter Volume in Psychosis Cases – MAG rs (T/A) p=0.016 P = p=0.016

National Alliance for Medical Image Computing – 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…) 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.

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