NeuroImage
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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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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.
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In our daily life we look at many scenes. Some are rapidly forgotten, but others we recognize later. We accurately predicted recognition success with natural scene photographs using single trial magnetoencephalography (MEG) measures of brain activation. ⋯ A permutation test confirmed that all lSVM based prediction rates were significantly better than "guessing". More generally, we present four approaches to analyzing brain function using lSVMs. (1) We show that lSVMs can be used to extract spatio-temporal patterns of brain activation from MEG-data. (2) We show lSVM classification can demonstrate significant correlations between comparatively early and late processes predictive of scene recognition, indicating dependencies between these processes over time. (3) We use lSVM classification to compare the information content of oscillatory and event-related MEG-activations and show they contain a similar amount of and largely overlapping information. (4) A more detailed analysis of single-trial predictiveness of different frequency bands revealed that theta band activity around 5 Hz allowed for highest prediction rates, and these rates are indistinguishable from those obtained with a full dataset. In sum our results clearly demonstrate that lSVMs can reliably predict natural scene recognition from single trial MEG-activation measures and can be a useful tool for analyzing predictive brain function.
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An important step in perceptual processing is the integration of information from different sensory modalities into a coherent percept. It has been suggested that such crossmodal binding might be achieved by transient synchronization of neurons from different modalities in the gamma-frequency range (>30 Hz). Here we employed a crossmodal priming paradigm, modulating the semantic congruency between visual-auditory natural object stimulus pairs, during the recording of the high density electroencephalogram (EEG). ⋯ Early gamma-band activity (40-50 Hz) was increased between 120 ms and 180 ms following auditory stimulus onset for semantically congruent stimulus pairs. Source reconstruction for this gamma-band response revealed a maximal increase in left middle temporal gyrus (BA 21), an area known to be related to the processing of both complex auditory stimuli and multisensory processing. The data support the hypothesis that oscillatory activity in the gamma-band reflects crossmodal semantic-matching processes in multisensory convergence sites.
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Recent research has shown that intrinsic brain activity as observed by functional magnetic resonance imaging (fMRI) manifest itself as coherent signal changes in networks encompassing brain regions that span long-range neuronal pathways. One of these networks, the so called default mode network, has become the primary target in recent investigations to link intrinsic activity to cognition and how intrinsic signal changes may be altered in disease. ⋯ Additionally, we found support for strong interactions between the precuneus/posterior cingulate cortex and the left inferior parietal lobe as well as between the dorsal and ventral sections of the medial prefrontal cortex. The suggested pivotal role of the precuneus/posterior cingulate cortex in the default mode network is discussed.