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NA-MIC Experience

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.

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NA-MIC Experience

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

  3. DTI Tractography Validation • Identify 11 major tracts with Slicer • Study repeatability and interrator variability • Goal: standardization of DTI analysis UC Irvine

  4. 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. Myelin Associated Glycoprotein Associated with White Matter Volume in Psychosis Cases – MAG rs720309 (T/A) p=0.016 P = 0.016 P = 0.016 p=0.016

  6. 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

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