Accumulated source imaging of brain activity with both low and high-frequency neuromagnetic signals

Abstract

Recent studies have revealed the importance of high-frequency brain signals (>70 Hz). One challenge of high-frequency signal analysis is that the size of time-frequency representation of high-frequency brain signals could be larger than 1 terabytes (TB), which is beyond the upper limits of a typical computer workstation's memory (<196 GB). The aim of the present study is to develop a new method to provide greater sensitivity in detecting high-frequency magnetoencephalography (MEG) signals in a single automated and versatile interface, rather than the more traditional, time-intensive visual inspection methods, which may take up to several days. To address the aim, we developed a new method, accumulated source imaging, defined as the volumetric summation of source activity over a period of time. This method analyzes signals in both low-(1~70Hz) and high-frequency (70~200Hz) ranges at source levels. To extract meaningful information from MEG signals at sensor space, the signals were decomposed to channel-cross-channel matrix (CxC) representing the spatiotemporal patterns of every possible sensor-pair. A new algorithm was developed and tested by calculating the optimal CxC and source location-orientation weights for volumetric source imaging, thereby minimizing multi-source interference and reducing computational cost. The new method was implemented in C/C++ and tested with MEG data recorded from clinical epilepsy patients. The results of experimental data demonstrated that accumulated source imaging could effectively summarize and visualize MEG recordings within 12.7 h by using approximately 10 GB of computer memory. In contrast to the conventional method of visually identifying multi-frequency epileptic activities that traditionally took 2-3 days and used 1-2 TB storage, the new approach can quantify epileptic abnormalities in both low-and high-frequency ranges at source levels, using much less time and computer memory. © 2014 Xiang, Luo, Kotecha, Korman, Zhang, Luo, Fujiwara, Hemasilpin and Rose.