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- (1~70 Hz) and high-frequency (70~200 Hz) 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 hours 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.

Purpose. Invasive intracranial recordings have suggested that high‐frequency oscillation is involved in epileptogenesis and is highly localized to epileptogenic zones. The aim of the present study is to characterize the frequency and spatial patterns of high‐frequency brain signals in childhood epilepsy using a non‐invasive technology. Methods. Thirty children with clinically diagnosed epilepsy were studied using a whole head magnetoen‐cephalography (MEG) system. MEG data were digitized at 4 000 Hz. The frequency and spatial characteristics of high‐frequency neuromagnetic signals were analyzed using continuous wavelet transform and beamformer. Three‐dimensional magnetic resonance imaging (MRI) was obtained for each patient to localize magnetic sources. Results. Twenty‐six patients showed high‐frequency (100‐1 000 Hz) components (26/30, 86%). Nineteen patients showed more than one high‐frequency component (19/30, 63%). The frequency range of high‐frequency components varied across patients. The highest frequency band was identified around 910 Hz. The loci of high‐frequency epileptic activities were concordant with the lesions identified by magnetic resonance imaging for 21 patients (21/30, 70%). The MEG source localizations of high‐frequency components were found to be concordant with intracranial recordings for nine of the eleven patients who underwent epilepsy surgery (9/11, 82%). Conclusion. The results have demonstrated that childhood epilepsy was associated with high‐frequency epileptic activity in a wide frequency range. The concordance of MEG source localization, MRI and intracranial recordings suggests that measurement of high‐frequency neuromagnetic signals might provide a novel approach for clinical management of childhood epilepsy.

Neuromagnetic spectral distributions when silently viewing words and non-words were investigated using a 151-channel whole head MEG system. The data were analyzed with synthetic aperture magnetometry (SAM). The dominant changes of spectral power were identified in the parietal and occipital cortices in 15–30 Hz, 30–60 Hz and 60–125 Hz bands following both word and non-word stimulations. The changes in Broca's and Wernicke's areas were consistently observed in 60–125 Hz bands, mainly following word stimulation. The results indicated that the occipital and parietal cortices were most probably involved in visuospatial processing of words and non-words. The language areas, particularly Broca's and Wernickes's areas, might be involved in implicit word processing such as verbal searching and/or internal speech.