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      Optimising beamformer regions of interest analysis

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Beamforming is a spatial filtering based source reconstruction method for EEG and MEG that allows the estimation of neuronal activity at a particular location within the brain. The computation of the location specific filter depends solely on an estimate of the data covariance matrix and on the forward model. Increasing the number of M/EEG sensors, increases the quantity of data required for accurate covariance matrix estimation. Often however we have a prior hypothesis about the site of, or the signal of interest. Here we show how this prior specification, in combination with optimal estimations of data dimensionality, can give enhanced beamformer performance for relatively short data segments. Specifically we show how temporal (Bayesian Principal Component Analysis) and spatial (lead field projection) methods can be combined to produce improvements in source estimation over and above employing the approaches individually.

          Highlights

          • This paper concerns optimising beamformer analysis for anatomical ROIs.

          • Channel reduction is performed using an ROI projection and Bayesian PCA.

          • This improves covariance matrix estimation for a given data length.

          • The proposed approach results in improvements in source estimation.

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          Most cited references18

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          Identifying true brain interaction from EEG data using the imaginary part of coherency.

          The main obstacle in interpreting EEG/MEG data in terms of brain connectivity is the fact that because of volume conduction, the activity of a single brain source can be observed in many channels. Here, we present an approach which is insensitive to false connectivity arising from volume conduction. We show that the (complex) coherency of non-interacting sources is necessarily real and, hence, the imaginary part of coherency provides an excellent candidate to study brain interactions. Although the usual magnitude and phase of coherency contain the same information as the real and imaginary parts, we argue that the Cartesian representation is far superior for studying brain interactions. The method is demonstrated for EEG measurements of voluntary finger movement. We found: (a) from 5 s before to movement onset a relatively weak interaction around 20 Hz between left and right motor areas where the contralateral side leads the ipsilateral side; and (b) approximately 2-4 s after movement, a stronger interaction also at 20 Hz in the opposite direction. It is possible to reliably detect brain interaction during movement from EEG data. The method allows unambiguous detection of brain interaction from rhythmic EEG/MEG data.
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            Dynamic imaging of coherent sources: Studying neural interactions in the human brain.

            Functional connectivity between cortical areas may appear as correlated time behavior of neural activity. It has been suggested that merging of separate features into a single percept ("binding") is associated with coherent gamma band activity across the cortical areas involved. Therefore, it would be of utmost interest to image cortico-cortical coherence in the working human brain. The frequency specificity and transient nature of these interactions requires time-sensitive tools such as magneto- or electroencephalography (MEG/EEG). Coherence between signals of sensors covering different scalp areas is commonly taken as a measure of functional coupling. However, this approach provides vague information on the actual cortical areas involved, owing to the complex relation between the active brain areas and the sensor recordings. We propose a solution to the crucial issue of proceeding beyond the MEG sensor level to estimate coherences between cortical areas. Dynamic imaging of coherent sources (DICS) uses a spatial filter to localize coherent brain regions and provides the time courses of their activity. Reference points for the computation of neural coupling may be based on brain areas of maximum power or other physiologically meaningful information, or they may be estimated starting from sensor coherences. The performance of DICS is evaluated with simulated data and illustrated with recordings of spontaneous activity in a healthy subject and a parkinsonian patient. Methods for estimating functional connectivities between brain areas will facilitate characterization of cortical networks involved in sensory, motor, or cognitive tasks and will allow investigation of pathological connectivities in neurological disorders.
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              A new approach to neuroimaging with magnetoencephalography.

              We discuss the application of beamforming techniques to the field of magnetoencephalography (MEG). We argue that beamformers have given us an insight into the dynamics of oscillatory changes across the cortex not explored previously with traditional analysis techniques that rely on averaged evoked responses. We review several experiments that have used beamformers, with special emphasis on those in which the results have been compared to those observed in functional magnetic resonance imaging (fMRI) and on those studying induced phenomena. We suggest that the success of the beamformer technique, despite the assumption that there are no linear interactions between the mesoscopic local field potentials across distinct cortical areas, may tell us something of the balance between functional integration and segregation in the human brain. What is more, MEG beamformer analysis facilitates the study of these complex interactions within cortical networks that are involved in both sensory-motor and cognitive processes.
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                Author and article information

                Contributors
                Journal
                Neuroimage
                Neuroimage
                Neuroimage
                Academic Press
                1053-8119
                1095-9572
                15 November 2014
                15 November 2014
                : 102
                : 945-954
                Affiliations
                [a ]Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London, UK
                [b ]Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK
                [c ]Oxford Centre for Human Brain Activity (OHBA), Oxford, UK
                Author notes
                [* ]Corresponding author at: The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL, Queen Square, London, WC1N 3BG, UK. ashwinioswal@ 123456gmail.com
                Article
                S1053-8119(14)00680-6
                10.1016/j.neuroimage.2014.08.019
                4229504
                25134978
                c26bf1f9-b9f6-4780-a17c-5bcb1a91a05a
                © 2014 The Authors
                History
                : 9 August 2014
                Categories
                Technical Note

                Neurosciences
                beamforming,regions of interest,bayesian pca
                Neurosciences
                beamforming, regions of interest, bayesian pca

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