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      Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI

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          Abstract

          Neurofeedback based on real-time fMRI is an emerging technique that can be used to train voluntary control of brain activity. Such brain training has been shown to lead to behavioral effects that are specific to the functional role of the targeted brain area. However, real-time fMRI-based neurofeedback so far was limited to mainly training localized brain activity within a region of interest. Here, we overcome this limitation by presenting near real-time dynamic causal modeling in order to provide feedback information based on connectivity between brain areas rather than activity within a single brain area. Using a visual–spatial attention paradigm, we show that participants can voluntarily control a feedback signal that is based on the Bayesian model comparison between two predefined model alternatives, i.e. the connectivity between left visual cortex and left parietal cortex vs. the connectivity between right visual cortex and right parietal cortex. Our new approach thus allows for training voluntary control over specific functional brain networks. Because most mental functions and most neurological disorders are associated with network activity rather than with activity in a single brain region, this novel approach is an important methodological innovation in order to more directly target functionally relevant brain networks.

          Highlights

          • We adapt DCM for use in neurofeedback experiments.

          • Participants can control a DCM-based neurofeedback signal.

          • Real-time DCM allows for voluntary control over brain connectivity.

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          Most cited references67

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          Dynamic causal modelling.

          In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.
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            Cramming More Components Onto Integrated Circuits

            G.E. Moore (1998)
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              A spelling device for the paralysed.

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                Author and article information

                Journal
                Neuroimage
                Neuroimage
                Neuroimage
                Academic Press
                1053-8119
                1095-9572
                01 November 2013
                01 November 2013
                : 81
                : 100
                : 422-430
                Affiliations
                Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
                Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
                Computer Science Department, University College London, London, UK
                Department of Neuroscience, CMU, University of Geneva, Geneva, Switzerland
                Geneva Neuroscience Center, Geneva, Switzerland
                Institute of Cognitive Neuroscience, University College London, London, UK
                Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
                Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK
                Author notes
                [* ]Corresponding author at: Department of Radiology and Medical Informatics, CIBM, University of Geneva, Geneva, Switzerland, Rue Gabrielle-Perret-G. 4, CH-1211, Geneva, Switzerland. yury.koush@ 123456unige.ch
                Article
                YNIMG10447
                10.1016/j.neuroimage.2013.05.010
                3734349
                23668967
                8a8a1a39-5eab-4be5-a77b-26c9138ffbd9
                © 2013 Elsevier Inc.

                This document may be redistributed and reused, subject to certain conditions.

                History
                : 1 May 2013
                Categories
                Article

                Neurosciences
                functional magnetic resonance imaging (fmri),real-time fmri,neurofeedback,brain connectivity,dynamic causal modeling (dcm)

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