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      MEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEG

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          Abstract

          NUTMEG is a source analysis toolbox geared towards cognitive neuroscience researchers using MEG and EEG, including intracranial recordings. Evoked and unaveraged data can be imported to the toolbox for source analysis in either the time or time-frequency domains. NUTMEG offers several variants of adaptive beamformers, probabilistic reconstruction algorithms, as well as minimum-norm techniques to generate functional maps of spatiotemporal neural source activity. Lead fields can be calculated from single and overlapping sphere head models or imported from other software. Group averages and statistics can be calculated as well. In addition to data analysis tools, NUTMEG provides a unique and intuitive graphical interface for visualization of results. Source analyses can be superimposed onto a structural MRI or headshape to provide a convenient visual correspondence to anatomy. These results can also be navigated interactively, with the spatial maps and source time series or spectrogram linked accordingly. Animations can be generated to view the evolution of neural activity over time. NUTMEG can also display brain renderings and perform spatial normalization of functional maps using SPM's engine. As a MATLAB package, the end user may easily link with other toolboxes or add customized functions.

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

          Guido Nolte, Ou Bai, Lewis A. Wheaton … (2004)
          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.

            J. Gross, J. Kujala, M. Hämäläinen … (2001)
            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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              Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity.

              A M Dale, A. K. Liu, B Fischl … (2000)
              Functional magnetic resonance imaging (fMRI) can provide maps of brain activation with millimeter spatial resolution but is limited in its temporal resolution to the order of seconds. Here, we describe a technique that combines structural and functional MRI with magnetoencephalography (MEG) to obtain spatiotemporal maps of human brain activity with millisecond temporal resolution. This new technique was used to obtain dynamic statistical parametric maps of cortical activity during semantic processing of visually presented words. An initial wave of activity was found to spread rapidly from occipital visual cortex to temporal, parietal, and frontal areas within 185 ms, with a high degree of temporal overlap between different areas. Repetition effects were observed in many of the same areas following this initial wave of activation, providing evidence for the involvement of feedback mechanisms in repetition priming.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Comput Intell Neurosci
                Journal ID (publisher-id): CIN
                Title: Computational Intelligence and Neuroscience
                Publisher: Hindawi Publishing Corporation
                ISSN (Print): 1687-5265
                ISSN (Electronic): 1687-5273
                Publication date (Print): 2011
                Publication date (Electronic): 15 March 2011
                Volume: 2011
                Electronic Location Identifier: 758973
                Affiliations
                1Department of Psychology, Zukunftskolleg, University of Konstanz, 78457 Konstanz, Germany
                2MEG Department, CERMEP, 69500 Lyon, France
                3INSERM U1028, CNRS UMR5292, Lyon Neuroscience Research Center, Brain Dynamics and Cognition Team, 69500 Lyon, France
                4Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University Nijmegen, 6500 HB Nijmegen, The Netherlands
                5Sir Peter Mansfield Magnetic Resonance Centre, University of Nottingham, Nottingham NG7 2RD, UK
                6Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospital of Geneva, 1211 Geneva, Switzerland
                7Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143, USA
                8Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada M5S 3G9
                9Department of Systems Design and Engineering, Tokyo Metropolitan University, Tokyo 191-0065, Japan
                Author notes

                Academic Editor: Sylvain Baillet

                Article
                DOI: 10.1155/2011/758973
                PMC ID: 3061455
                PubMed ID: 21437174
                SO-VID: 13024088-bc8b-4a3e-95b7-96512b95e19b
                Copyright © Copyright © 2011 Sarang S. Dalal et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 4 October 2010
                Date revision received : 30 November 2010
                Date accepted : 17 January 2011
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Neurosciences
                Data availability:
                ScienceOpen disciplines: Neurosciences

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