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Abstract #1665

Template Free Identification of Resting State Networks Based Independent Component Analysis

Veronika Schoepf1, Christian H. Kasess1,2, Andreas Weissenbacher1, Rupert Lanzenberger2, Christian Windischberger1,3, Ewald Moser1,3

1MR Center of Excellence, Medical University Vienna, Vienna, Austria; 2Division of Biological Psychiatry, Department of Psychiatry and Psychotherapy, Medical University Vienna, Vienna, Austria; 3Center of Biomedical Engeneering and Physics, Medical University Vienna, Vienna, Austria

The ''default mode of brain function'' has gained considerable interest in human neuroimaging studies. Standard evaluation of spatially consistent resting-state components over all subjects leads to problems using a predefined spatial template for correlation with the single subjects components as other default mode networks might be disregarded due to template definition. In this study we introduce a novel evaluation approach for identifying spatially consistent default mode networks across a group of subjects based on ICA which requires neither component templates nor manual inspection/selection of single subject components allowing for a truly explorative way of assessing resting state networks.