This article describes the use of Bayes factors for comparing Dynamic Causal Models (DCMs). DCMs are used to make inferences about effective connectivity from functional Magnetic Resonance Imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, the connectivity pattern between the regions included in the model. Given the current lack of detailed knowledge on anatomical connectivity in the human brain, there are often considerable degrees of freedom when defining the connectional structure of DCMs. In addition, many plausible scientific hypotheses may exist about which connections are changed by experimental manipulation, and a formal procedure for directly comparing these competing hypotheses is highly desirable. In this article, we show how Bayes factors can be used to guide choices about model structure, both with regard to the intrinsic connectivity pattern and the contextual modulation of individual connections. The combined use of Bayes factors and DCM thus allows one to evaluate competing scientific theories about the architecture of large-scale neural networks and the neuronal interactions that mediate perception and cognition.

& What happens in the brain when you conjure up a mental image in your mind’s eye? We tested whether the particular regions of extrastriate cortex activated during mental imagery depend on the content of the image. Using functional magnetic resonance imaging (fMRI), we demonstrated selective activation within a region of cortex specialized for face perception during mental imagery of faces, and selective activation within a place-selective cortical region during imagery of places. In a further study, we compared the activation for imagery and perception in these regions, and found greater response magnitudes for perception than for imagery of the same items. Finally, we found that it is possible to determine the content of single cognitive events from an inspection of the fMRI data from individual imagery trials. These findings strengthen evidence that imagery and perception share common processing mechanisms, and demonstrate that the specific brain regions activated during mental imagery depend on the content of the visual image. &

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The anatomical organization of the brain is such that incoming signals from different sensory modalities are initially processed in anatomically separate regions of the cortex. When these signals originate from a single event or object in the external world, it is essential that the inputs are integrated to form a coherent representation of the multisensory event. This review discusses recent data indicating that the integration of multisensory signals relies not only on anatomical convergence from sensory-specific cortices to multisensory brain areas but also on reciprocal influences between cortical regions that are traditionally considered as sensory-specific. These findings highlight integration mechanisms that go beyond traditional models based on a hierarchical convergence of sensory processing. NEUROSCIENTIST 12(4):327–338, 2006. DOI: 10.1177/1073858406287908 KEY WORDS Multisensory, Sensory-specific, fMRI, ERPs, Attention, Space One of the central themes dominating research in neuroscience is the notion that sensory, motor, and cognitive functions are localized in specific areas of the brain. The development of noninvasive brain imaging techniques, such as functional magnetic resonance imaging (fMRI),

This study investigated the cortical mechanisms of visual-spatial attention in a task where subjects discriminated patterned targets in one visual field at a time. Functional magnetic imaging (fMRI) was used to localize attention-related changes in neural activity within specific retinotopic visual areas, while recordings of event-related brain potentials (ERPs) traced the time course of these changes. The earliest ERP components enhanced by attention occurred in the time range 70–130 ms post-stimulus onset, and their neural generators were estimated to lie in the dorsal and ventral extrastriate visual cortex. The anatomical areas activated by attention corresponded closely to those showing increased neural activity during passive visual stimulation. Enhanced neural activity was also observed in the primary visual cortex (area V1) with fMRI, but ERP recordings indicated that the initial sensory response at 50–90 ms that was localized to V1 was not modulated by attention. Modeling of ERP sources over an extended time range showed that attended stimuli elicited a long-latency (160–260 ms) negativity that was attributed to the dipolar source in area V1. This finding is in line with hypotheses that V1 activity may be modulated by delayed, reentrant

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journal homepage: www.elsevier.com/locate/actpsy

Attention has been found to have a wide variety of effects on the responses of neurons in visual cortex. We describe a model of attention that exhibits each of these different forms of attentional modulation, depending on the stimulus conditions and the spread (or selectivity) of the attention field in the model. The model helps reconcile proposals that have been taken to represent alternative theories of attention. We argue that the variety and complexity of the results reported in the literature emerge from the variety of empirical protocols that were used, such that the results observed in any one experiment depended on the stimulus conditions and the subject’s attentional strategy, a notion that we define precisely in terms of the attention field in the model, but that has not typically been completely under experimental control.

doi: 10.3389/fnsys.2010.00020 The relation of ongoing brain activity, evoked neural responses, and cognition

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