Title

Volumetric imaging of brain activity with spatial-frequency decoding of neuromagnetic signals

Document Type

Article

Publication Date

1-2015

Publication Source

Journal of Neuroscience Methods

Volume

239

Inclusive pages

114-128

DOI

http://dx.doi.org/10.1016/j.jneumeth.2014.10.007

Publisher

Elsevier

Place of Publication

Netherlands

ISBN/ISSN

0165-0270

Peer Reviewed

yes

Abstract

Background: The brain generates signals in a wide frequency range (∼2840 Hz). Existing magnetoen-cephalography (MEG) methods typically detect brain activity in a median-frequency range (1–70 Hz).The objective of the present study was to develop a new method to utilize the frequency signatures forsource imaging.New method: Morlet wavelet transform and two-step beamforming were integrated into a systematicapproach to estimate magnetic sources in time–frequency domains. A grid-frequency kernel (GFK) wasdeveloped to decode the correlation between each time–frequency representation and grid voxel. Brainactivity was reconstructed by accumulating spatial- and frequency-locked signals in the full spectraldata for all grid voxels. To test the new method, MEG data were recorded from 20 healthy subjects and3 patients with verified epileptic foci.Results: The experimental results showed that the new method could accurately localize brain activationin auditory cortices. The epileptic foci localized with the new method were spatially concordant withinvasive recordings.Comparison with existing methods: Compared with well-known existing methods, the new method isobjective because it scans the entire brain without making any assumption about the number of sources.The novel feature of the new method is its ability to localize high-frequency sources.Conclusions: The new method could accurately localize both low- and high-frequency brain activities.The detection of high-frequency MEG signals can open a new avenue in the study of the human brainfunction as well as a variety of brain disorders.

Keywords

Brain; High-frequency oscillation; Magnetoencephalography; Neuroimaging; Spatial filter; Wavelet

Disciplines

Bioelectrical and Neuroengineering | Engineering

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