Naumer, M. J.1, van den Bosch, J. F.1, Wibral, M.2, Kohler, A.3, Singer, W.3, Kaiser, J.1, van de Ven, V.4, and Muckli, L.5
1Institut für Medizinische Psychologie, Goethe Universität, Frankfurt am Main; 2Brain Imaging Center, Goethe Universität, Frankfurt am Main; 3Max Planck Institut für Hirnforschung, Frankfurt am Main; 4Faculty of Psychology, Maastricht University, The Netherlands; 5Department of Psychology, University of Glasgow, United Kingdom
In this study, we aimed at mapping the network of brain regions that were functionally connected during object-related audio-visual (AV) integration. To this end, we used spatial independent component analysis (sICA), a multivariate, data-driven analysis technique that decomposes an fMRI dataset into spatially independent components. The resulting components were classified by the functional profile of their time course in a general linear model (GLM) of the stimulation time course, selecting three components of interest: visual, auditory, and audio-visual. Regions-of-interest (ROIs) were defined as clusters of voxels which weighted significantly to at least two of these, in other words, these were regions of overlap. ROI-restricted GLM analyses on the independent data of a second experiment showed robust AV integration effects according to the max-criterion (i.e., 0<A<AV>V>0) in this ICA-defined bilateral cortical network (consisting of pSTS, VOT, PPC, and PFC regions), thus demonstrating the sensitivity and value of the proposed analysis approach.
Poster 20
Postergruppe 2