NeuroImage
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In humans, it is generally not possible to use invasive techniques in order to identify brain activity corresponding to activity of individual muscles. Further, it is believed that the spatial resolution of non-invasive brain imaging modalities is not sufficient to isolate neural activity related to individual muscles. However, this study shows that it is possible to reconstruct muscle activity from functional magnetic resonance imaging (fMRI). ⋯ The two voxel sets corresponding to the activity of the antagonist muscles were intermingled but disjoint. They were distributed over a wide area of pre-motor cortex and M1 and not limited to regions generally associated with wrist control. These results show that brain activity measured by fMRI in humans can be used to predict individual muscle activity through Bayesian linear models, and that our algorithm provides a novel and non-invasive tool to investigate the brain mechanisms involved in motor control and learning in humans.
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Comparative Study
A tractography comparison between turboprop and spin-echo echo-planar diffusion tensor imaging.
The development of accurate, non-invasive methods for mapping white matter fiber-tracts is of critical importance. However, fiber-tracking is typically performed on diffusion tensor imaging (DTI) data obtained with echo-planar-based imaging techniques (EPI), which suffer from susceptibility-related image artifacts, and image warping due to eddy-currents. Thus, a number of white matter fiber-bundles mapped using EPI-based DTI data are distorted and/or terminated early. ⋯ Thus, Turboprop may be a more appropriate DTI data acquisition technique for tracing white matter fibers near regions with significant magnetic susceptibility differences, as well as in longitudinal studies of such fibers. However, the intra-session reproducibility of tractography results was higher for EPI-based than Turboprop DTI data. Thus, EPI-based DTI may be more advantageous for tracing fibers minimally affected by field inhomogeneities.
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We present an algorithm for multilinear decomposition that allows for arbitrary shifts along one modality. The method is applied to neural activity arranged in the three modalities space, time, and trial. ⋯ We show how shift-invariant multilinear decompositions of multiway data can successfully cope with variable latencies in data derived from neural activity--a problem that has caused degenerate solutions especially in modeling neuroimaging data with instantaneous multilinear decompositions. Our algorithm is available for download at www.erpwavelab.org.
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A hierarchical Bayesian method estimated current sources from MEG data, incorporating an fMRI constraint as a hierarchical prior whose strength is controlled by hyperparameters. A previous study [Sato, M., Yoshioka, T., Kajihara, S., Toyama, K., Goda, N., Doya, K., Kawato, M., 2004. Hierarchical Bayesian estimation for MEG inverse problem. ⋯ The false-positive effects of the noisy priors were suppressed by using appropriate hyperparameter values. The hierarchical Bayesian method also was capable of reconstructing retinotopic sequential activation in V1 with fine spatiotemporal resolution, from MEG data elicited by sequential stimulation of the four visual quadrants with the fan-shaped checker board pattern at much shorter intervals (150 and 400 ms) than the temporal resolution of fMRI. These results indicate the potential capability for the hierarchical Bayesian method combining MEG with fMRI to improve the spatiotemporal resolution of noninvasive brain activity measurement.
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Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. ⋯ In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups' patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects.