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In humans, the extent to which body-based cues, such as vestibular, somatosensory, and motoric cues, are necessary for normal expression of spatial representations remains unclear. Recent breakthroughs in immersive virtual reality technology allowed us to test how body-based cues influence spatial representations of large-scale environments in humans. Specifically, we manipulated the availability of body-based cues during navigation using an omnidirectional treadmill and a head-mounted display, investigating brain differences in levels of activation (i.e., univariate analysis), patterns of activity (i.e., multivariate pattern analysis), and putative network interactions between spatial retrieval tasks using fMRI. Our behavioral and neuroimaging results support the idea that there is a core, modality-independent network supporting spatial memory retrieval in the human brain. Thus, for well-learned spatial environments, at least in humans, primarily visual input may be sufficient for expression of complex representations of spatial environments. VIDEO ABSTRACT.
Pubmed ID: 31540825
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A Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run. Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. This Python-based, cross-platform, open-source software toolbox software toolbox for the application of classifier-based analysis techniques to fMRI datasets makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages.
View all literature mentionsFit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue."
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