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      Decoding the direction of imagined visual motion using 7 T ultra-high field fMRI

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          Abstract

          There is a long-standing debate about the neurocognitive implementation of mental imagery. One form of mental imagery is the imagery of visual motion, which is of interest due to its naturalistic and dynamic character. However, so far only the mere occurrence rather than the specific content of motion imagery was shown to be detectable. In the current study, the application of multi-voxel pattern analysis to high-resolution functional data of 12 subjects acquired with ultra-high field 7 T functional magnetic resonance imaging allowed us to show that imagery of visual motion can indeed activate the earliest levels of the visual hierarchy, but the extent thereof varies highly between subjects. Our approach enabled classification not only of complex imagery, but also of its actual contents, in that the direction of imagined motion out of four options was successfully identified in two thirds of the subjects and with accuracies of up to 91.3% in individual subjects. A searchlight analysis confirmed the local origin of decodable information in striate and extra-striate cortex. These high-accuracy findings not only shed new light on a central question in vision science on the constituents of mental imagery, but also show for the first time that the specific sub-categorical content of visual motion imagery is reliably decodable from brain imaging data on a single-subject level.

          Highlights

          • Four different directions of visual motion can be decoded during imagery at 7 T.

          • We found very high classification accuracies in single subjects.

          • High variability between activation patterns and accuracies of different subjects

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          Most cited references76

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          Information-based functional brain mapping.

          The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a "searchlight," whose contents are analyzed multivariately at each location in the brain.
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            Brain Computer Interfaces, a Review

            A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.
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              Decoding reveals the contents of visual working memory in early visual areas

              Visual working memory provides an essential link between perception and higher cognitive functions, allowing for the active maintenance of information regarding stimuli no longer in view1,2. Research suggests that sustained activity in higher-order prefrontal, parietal, inferotemporal and lateral occipital areas supports visual maintenance3-11, and may account for working memory’s limited capacity to hold up to 3-4 items9-11. Because higher-order areas lack the visual selectivity of early sensory areas, it has remained unclear how observers can remember specific visual features, such as the precise orientation of a grating, with minimal decay in performance over delays of many seconds12. One proposal is that sensory areas serve to maintain fine-tuned feature information13, but early visual areas show little to no sustained activity over prolonged delays14-16. Using fMRI decoding methods17, here we show that orientations held in working memory can be decoded from activity patterns in the human visual cortex, even when overall levels of activity are low. Activity patterns in areas V1-V4 could predict which of two oriented gratings was held in memory with mean accuracy levels upwards of 80%, even in participants exhibiting activity that fell to baseline levels after a prolonged delay. These orientation-selective activity patterns were sustained throughout the delay period, evident in individual visual areas, and similar to the responses evoked by unattended, task-irrelevant gratings. Our results demonstrate that early visual areas can retain specific information about visual features held in working memory, over periods of many seconds when no physical stimulus is present.
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                Author and article information

                Contributors
                Journal
                Neuroimage
                Neuroimage
                Neuroimage
                Academic Press
                1053-8119
                1095-9572
                15 January 2016
                15 January 2016
                : 125
                : 61-73
                Affiliations
                [a ]Department of Cognitive Neuroscience, Maastricht University, 6229 EV Maastricht, The Netherlands
                [b ]Maastricht Brain Imaging Center, 6229 ER Maastricht, The Netherlands
                [c ]Netherlands Institute for Neuroscience (NIN), 1105 BA Amsterdam, The Netherlands
                Author notes
                [* ]Corresponding author at: Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV, P.O. Box 616, 6200 MD Maastricht, The Netherlands. Fax: + 31 433884125. thomas.emmerling@ 123456maastrichtuniversity.nl
                Article
                S1053-8119(15)00923-4
                10.1016/j.neuroimage.2015.10.022
                4692515
                26481673
                4661f796-c86c-4447-814a-6811ec7a9bfd
                © 2015 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 24 July 2015
                : 8 October 2015
                Categories
                Article

                Neurosciences
                decoding,functional magnetic resonance imaging,multi-voxel pattern analysis,ultra-high field mri,visual mental imagery

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