The Effect of Age-specific Brain Template on Morphometric Analysis of Pediatric Brain Data

OHBM

S123

15th Annual Meeting ■ June 18–23, 2009 ■ San Francisco, CA, USA

Schedule of Poster Presentations

386 SA-PM An Investigation of Registration Accuracy for Clinical EPI to T1-weighted MR Images using a T2-weighted Intermediary, M Jenkinson, A J Bartsch, C F Beckmann, FMRIB Centre, University of Oxford,Oxford, United Kingdom

388 SA-PM* Creating Functional Probabilistic Maps Using Structurally and Functionally Driven Multi-Subject(O-SA3) Alignment, M. A Frost, R. Goebel, Department of Cognitive Neuroscience, Faculty of Psychology and

Neuroscience, Maastricht, Netherlands

390 SA-PM The Effect of Age-specific Brain Template on Morphometric Analysis of Pediatric Brain Data, U. Yoon,V.S Fonov, D. Perusse, A.C. Evans, McConnell Brain Imaging Centre, Montreal Neurological Institute,Montreal, QC, Canada

392 SA-PM Potential Differences in Registration Quality Between Schizophrenia Patients and Controls.,N.D. Davenport, K.O. Lim, B.A. Mueller, S.C. Schulz, University of Minnesota, Minneapolis, MN, USA

394 SA-PM An evaluation of volume- and surface-based nonlinear registration of human brain MRI data, A Klein,SS Ghosh, RV Parsey, Columbia University, New York, NY, USA

396 SA-PM Multi-contrast Large Deformation Diffeomorphic Metric Mapping and Diffusion Tensor ImageRegistration, C. Ceritoglu, K. Oishi, S. Mori, M.I. Miller, Center for Imaging Science, The Johns HopkinsUniversity, Baltimore, MD, USA

MODELING AND ANALYSISMultivariate Modeling, PCA and ICA

398 SA-PM Source Based Morphometry: Approaches to Identify Gray and White Matter Group Differences withApplication to Schizophrenia, L Xu, V.D. Calhoun, The MIND Research Network, Albuquerque, NM, USA

400 SA-PM SMART: A statistical framework for optimal design matrix generation with application to fMRI, G.V. Pendse, D. Borsook, L. Becerra, Imaging and Analysis Group (iMAG), P. A. I. N Group, McLeanHospital, Harvard Medical School, Belmont, MA, USA

402 SA-PM* A Novel Test Statistic for Local Canonical Correlation Analysis of fMRI Data, M Jin, R Nandy,(O-SU6) D Cordes, University of Colorado Denver, Aurora, CO, USA

404 SA-PM A Riemannian Framework for Statistical Shape Analysis of the Corpus Callosum., S H Joshi, K L Narr,R P Woods, O R Phillips, K H Nuechterlein, R F Asarnow, A W Toga, Department of Neurology, David GeffenSchool of Medicine, University of California, Los Angeles, CA, USA

406 SA-PM Bias and Heterogeneity in Neuroimaging Meta-analysis, G. Salimi-Khorshidi, S.M. Smith, T.E. Nichols,Centre for functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom

408 SA-PM A Method for Fusion of fMRI Data with MEG Data from the Same Cognitive Task, V J Schmithorst, S K Holland, J Vannest, Children's Hospital Medical Center, Cincinnati, OH, USA

410 SA-PM Validating Independent Component Analysis of Functional Brain Imaging Data by Temporal ClusterAnalysis, Ruxy Mutihac, Radu Mutihac, University of Oxford, Oxford, United Kingdom

412 SA-PM Template free identification of resting state networks based on independent component analysis,V. Schöpf, C.H. Kasess, A. Weissenbacher, R. Lanzenberger, C. Windischberger, E. Moser, MR Center ofExcellence, Medical University Vienna, Vienna, Austria

414 SA-PM Multi-level bootstrap analysis of stable clusters (BASC) in resting-state fMRI, P Bellec, P Rosa-Neto, H Benali, A, C Evans, Montreal Neurological Institute, McGill University, Montreal, QC, Canada

416 SA-PM More independent EEG components tend to be more dipolar, A Delorme, J Palmer, R Oostenveld,J Onton, S Makeig, UCSD, La Jolla, CA, USA

Saturday, June 20, 2009