Literature DB >> 17079518

Magnetoencephalography as a research tool in neuroscience: state of the art.

Andreas A Ioannides1.   

Abstract

Magnetoencephalography (MEG) is a noninvasive neuroimaging method for detecting, analyzing, and interpreting the magnetic field generated by the electrical activity in the brain. Modern hardware can capture the MEG signal at hundreds of points around the head in a snapshot lasting only a fraction of a millisecond. The sensitivity of modern hardware is high enough to permit the extraction of a clean signal generated by the brain well above the noise level of the MEG hardware. It is possible to identify signatures of superficial and often deep generators in the raw MEG signal, even in snapshots of data. In a more quantitative way, tomographic images of the electrical current density in the brain can be extracted from each snapshot of MEG signal, providing a direct correlate of coherent collective neuronal activity. A number of recent studies have scrutinized brain function in the new spatiotemporal window that real-time tomographic analysis of MEG signals has opened. The results have allowed the variability in a single area to be seen in the context of activity in other areas and background rhythmic activity. In this view, normal brain function is seen as a cascade of extremely fast events and the unfolding of specialized processes, segregated in space and time and organized into well-defined stages of processing.

Mesh:

Year:  2006        PMID: 17079518     DOI: 10.1177/1073858406293696

Source DB:  PubMed          Journal:  Neuroscientist        ISSN: 1073-8584            Impact factor:   7.519


  21 in total

1.  Functional neuroimaging: a brief overview and feasibility for use in chiropractic research.

Authors:  Reidar P Lystad; Henry Pollard
Journal:  J Can Chiropr Assoc       Date:  2009-03

2.  Neural mechanisms of movement speed and tau as revealed by magnetoencephalography.

Authors:  Heng-Ru May Tan; Arthur C Leuthold; David N Lee; Joshua K Lynch; Apostolos P Georgopoulos
Journal:  Exp Brain Res       Date:  2009-05-08       Impact factor: 1.972

Review 3.  Functional network disruption in the degenerative dementias.

Authors:  Michela Pievani; Willem de Haan; Tao Wu; William W Seeley; Giovanni B Frisoni
Journal:  Lancet Neurol       Date:  2011-07-21       Impact factor: 44.182

Review 4.  Connectivity and complex systems: learning from a multi-disciplinary perspective.

Authors:  Laura Turnbull; Marc-Thorsten Hütt; Andreas A Ioannides; Stuart Kininmonth; Ronald Poeppl; Klement Tockner; Louise J Bracken; Saskia Keesstra; Lichan Liu; Rens Masselink; Anthony J Parsons
Journal:  Appl Netw Sci       Date:  2018-06-18

Review 5.  Neuroenergetics at the brain-mind interface: a conceptual approach.

Authors:  Kuzma Strelnikov
Journal:  Cogn Process       Date:  2014-03-17

6.  BA3b and BA1 activate in a serial fashion after median nerve stimulation: direct evidence from combining source analysis of evoked fields and cytoarchitectonic probabilistic maps.

Authors:  Christos Papadelis; Simon B Eickhoff; Karl Zilles; Andreas A Ioannides
Journal:  Neuroimage       Date:  2010-08-04       Impact factor: 6.556

7.  Emotion separation is completed early and it depends on visual field presentation.

Authors:  Lichan Liu; Andreas A Ioannides
Journal:  PLoS One       Date:  2010-03-22       Impact factor: 3.240

8.  A spatiotemporal framework for estimating trial-to-trial amplitude variation in event-related MEG/EEG.

Authors:  Tulaya Limpiti; Barry D Van Veen; Hagai T Attias; Srikantan S Nagarajan
Journal:  IEEE Trans Biomed Eng       Date:  2008-10-31       Impact factor: 4.538

9.  A spatiotemporal framework for MEG/EEG evoked response amplitude and latency variability estimation.

Authors:  Tulaya Limpiti; Barry D Van Veen; Ronald T Wakai
Journal:  IEEE Trans Biomed Eng       Date:  2009-09-29       Impact factor: 4.538

Review 10.  Dynamic imaging of brain function.

Authors:  Fahmeed Hyder
Journal:  Methods Mol Biol       Date:  2009
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