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Audition and vision both convey spatial information about the environment, but much less is known about mechanisms of auditory spatial cognition than visual spatial cognition. Human cortex contains >20 visuospatial map representations but no reported auditory spatial maps. The intraparietal sulcus (IPS) contains several of these visuospatial maps, which support visuospatial attention and short-term memory (STM). Neuroimaging studies also demonstrate that parietal cortex is activated during auditory spatial attention and working memory tasks, but prior work has not demonstrated that auditory activation occurs within visual spatial maps in parietal cortex. Here, we report both cognitive and anatomical distinctions in the auditory recruitment of visuotopically mapped regions within the superior parietal lobule. An auditory spatial STM task recruited anterior visuotopic maps (IPS2-4, SPL1), but an auditory temporal STM task with equivalent stimuli failed to drive these regions significantly. Behavioral and eye-tracking measures rule out task difficulty and eye movement explanations. Neither auditory task recruited posterior regions IPS0 or IPS1, which appear to be exclusively visual. These findings support the hypothesis of multisensory spatial processing in the anterior, but not posterior, superior parietal lobule and demonstrate that recruitment of these maps depends on auditory task demands.
Pubmed ID: 26656996 RIS Download
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Open source software suite for processing and analyzing human brain MRI images. Used for reconstruction of brain cortical surface from structural MRI data, and overlay of functional MRI data onto reconstructed surface. Contains automatic structural imaging stream for processing cross sectional and longitudinal data. Provides anatomical analysis tools, including: representation of cortical surface between white and gray matter, representation of the pial surface, segmentation of white matter from rest of brain, skull stripping, B1 bias field correction, nonlinear registration of cortical surface of individual with stereotaxic atlas, labeling of regions of cortical surface, statistical analysis of group morphometry differences, and labeling of subcortical brain structures.Operating System: Linux, macOS.
View all literature mentionsNetwork of ftp and web servers around world that store identical, up to date, versions of code and documentation for R. Package archive network for R programming language.
View all literature mentionsTHIS RESOURCE IS NO LONGER AVAILABLE,documented on February 1st, 2022. Instrument supplier providing eye tracking capabilities for behavioral labs as well as for MRI, MEG, and EEG research environments.
View all literature mentionsNEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. NEURON has benefited from judicious revision and selective enhancement, guided by feedback from the growing number of neuroscientists who have used it to incorporate empirically-based modeling into their research strategies. NEURON's computational engine employs special algorithms that achieve high efficiency by exploiting the structure of the equations that describe neuronal properties. It has functions that are tailored for conveniently controlling simulations, and presenting the results of real neurophysiological problems graphically in ways that are quickly and intuitively grasped. Instead of forcing users to reformulate their conceptual models to fit the requirements of a general purpose simulator, NEURON is designed to let them deal directly with familiar neuroscience concepts. Consequently, users can think in terms of the biophysical properties of membrane and cytoplasm, the branched architecture of neurons, and the effects of synaptic communication between cells. * helps users focus on important biological issues rather than purely computational concerns * has a convenient user interface * has a user-extendable library of biophysical mechanisms * has many enhancements for efficient network modeling * offers customizable initialization and simulation flow control * is widely used in neuroscience research by experimentalists and theoreticians * is well-documented and actively supported * is free, open source, and runs on (almost) everything
View all literature mentionsA free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer.
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
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