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We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches(More)
Echoplanar functional magnetic resonance imaging was used to monitor activation changes of brain areas while subjects viewed apparent motion stimuli and while they were engaged in motion imagery. Human cortical areas MT (V5) and MST were the first areas of the 'dorsal' processing stream which responded with a clear increase in signal intensity to apparent(More)
Independent component analysis (ICA) is a valuable technique for the multivariate data-driven analysis of functional magnetic resonance imaging (fMRI) data sets. Applications of ICA have been developed mainly for single subject studies, although different solutions for group studies have been proposed. These approaches combine data sets from multiple(More)
We present a general method for the classification of independent components (ICs) extracted from functional MRI (fMRI) data sets. The method consists of two steps. In the first step, each fMRI-IC is associated with an IC-fingerprint, i.e., a representation of the component in a multidimensional space of parameters. These parameters are post hoc estimates(More)
Most people acquire literacy skills with remarkable ease, even though the human brain is not evolutionarily adapted to this relatively new cultural phenomenon. Associations between letters and speech sounds form the basis of reading in alphabetic scripts. We investigated the functional neuroanatomy of the integration of letters and speech sounds using(More)
Understanding the functional organization of the human primary auditory cortex (PAC) is an essential step in elucidating the neural mechanisms underlying the perception of sound, including speech and music. Based on invasive research in animals, it is believed that neurons in human PAC that respond selectively with respect to the spectral content of a sound(More)
It is a commonly held view that numbers are represented in an abstract way in both parietal lobes. This view is based on failures to find differences between various notational representations. Here we show that by using relatively smaller voxels together with an adaptation paradigm and analyzing subjects on an individual basis it is possible to detect(More)
We present a framework aimed to reveal directed interactions of activated brain areas using time-resolved fMRI and vector autoregressive (VAR) modeling in the context of Granger causality. After describing the underlying mathematical concepts, we present simulations helping to characterize the conditions under which VAR modeling and Granger causality can(More)
Visual face identification requires distinguishing between thousands of faces we know. This computational feat involves a network of brain regions including the fusiform face area (FFA) and anterior inferotemporal cortex (aIT), whose roles in the process are not well understood. Here, we provide the first demonstration that it is possible to discriminate(More)
Constraints from functional magnetic resonance imaging (fMRI) were used to identify the sources of the visual P300 event-related potential (ERP). Healthy subjects performed a visual three-stimulus oddball paradigm with a difficult discrimination task while fMRI and high-density ERP data were acquired in separate sessions. This paradigm allowed us to(More)