Jing Xiang1, Abraham Korman2, Kasun M Samarasinghe3, Xiaopei Wang4, Fawen Zhang5, Hui Qiao6, Bo Sun6, Fengbin Wang6, Howard H Fan3, Elizabeth A Thompson7. 1. MEG Center, Department of Neurology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, USA. Electronic address: Jing.xiang@cchmc.org. 2. MEG Center, Department of Neurology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, USA. 3. Department of Electrical Engineering, University of Cincinnati, Cincinnati, OH, USA. 4. Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, USA. 5. Department of Communication Sciences and Disorders, University of Cincinnati, OH, USA. 6. MEG Laboratory, Beijing Tiantan Hospital, Beijing, People's Republic of China. 7. Department of Electrical Engineering, Purdue University, Fort Wayne, IN, USA.
Abstract
BACKGROUND: The brain generates signals in a wide frequency range (∼2840 Hz). Existing magnetoencephalography (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 for source imaging. NEW METHOD: Morlet wavelet transform and two-step beamforming were integrated into a systematic approach to estimate magnetic sources in time-frequency domains. A grid-frequency kernel (GFK) was developed to decode the correlation between each time-frequency representation and grid voxel. Brain activity was reconstructed by accumulating spatial- and frequency-locked signals in the full spectral data for all grid voxels. To test the new method, MEG data were recorded from 20 healthy subjects and 3 patients with verified epileptic foci. RESULTS: The experimental results showed that the new method could accurately localize brain activation in auditory cortices. The epileptic foci localized with the new method were spatially concordant with invasive recordings. COMPARISON WITH EXISTING METHODS: Compared with well-known existing methods, the new method is objective 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 brain function as well as a variety of brain disorders.
BACKGROUND: The brain generates signals in a wide frequency range (∼2840 Hz). Existing magnetoencephalography (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 for source imaging. NEW METHOD: Morlet wavelet transform and two-step beamforming were integrated into a systematic approach to estimate magnetic sources in time-frequency domains. A grid-frequency kernel (GFK) was developed to decode the correlation between each time-frequency representation and grid voxel. Brain activity was reconstructed by accumulating spatial- and frequency-locked signals in the full spectral data for all grid voxels. To test the new method, MEG data were recorded from 20 healthy subjects and 3 patients with verified epileptic foci. RESULTS: The experimental results showed that the new method could accurately localize brain activation in auditory cortices. The epileptic foci localized with the new method were spatially concordant with invasive recordings. COMPARISON WITH EXISTING METHODS: Compared with well-known existing methods, the new method is objective 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 brain function as well as a variety of brain disorders.
Authors: Jing Xiang; Kimberly Leiken; Xinyao Degrauw; Benjamin Kay; Hisako Fujiwara; Douglas F Rose; Janelle R Allen; Joanne E Kacperski; Hope L O'Brien; Marielle A Kabbouche; Scott W Powers; Andrew D Hershey Journal: J Pain Date: 2016-03-10 Impact factor: 5.820
Authors: Kimberly A Leiken; Jing Xiang; Emily Curry; Hisako Fujiwara; Douglas F Rose; Janelle R Allen; Joanne E Kacperski; Hope L O'Brien; Marielle A Kabbouche; Scott W Powers; Andrew D Hershey Journal: J Headache Pain Date: 2016-04-26 Impact factor: 7.277