Frequency and spatial characteristics of high-frequency neuromagnetic signals in childhood epilepsy

MEG Center, Division of Neurology, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45220, USA.
Epileptic disorders: international epilepsy journal with videotape (Impact Factor: 0.95). 07/2009; 11(2):113-25. DOI: 10.1684/epd.2009.0253
Source: PubMed


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.
Thirty children with clinically diagnosed epilepsy were studied using a whole head magnetoencephalography (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.
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%).
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.

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    • "Furthermore, the overall quantity in a certain time range mirrors disease activity (Zijlmans et al., 2009). Recent studies suggest that HFO may even be detectable with surface EEG (Andrade-Valenca et al., 2011; Zelmann et al., 2013) and MEG (Xiang et al., 2009; Miao et al., 2014) under ideal circumstances. At the other end of the frequency spectrum, another entity related to the epileptic network had been identified much earlier (Cohn, 1954; Chatrian et al., 1968; Ikeda et al., 1996, 1999). "

    Clinical Neurophysiology 06/2014; 126(1). DOI:10.1016/j.clinph.2014.06.001 · 3.10 Impact Factor
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    • "In Equation (4), G represents the global spectrogram; Atf represents an accumulated spectrum of one MEG sensor data; m indicates MEG sensor index and M indicates the total number of MEG sensors; s indicates the time slice of the spectrum; f indicates frequency bands (or bins) of MEG data. Since each sensor was positioned in a distinct location around the head (Figure 1), the global spectrogram is considered to be a " spatial summation " for each epoch of data (Xiang et al., 2009a). "
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    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- (1~70 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 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.
    Frontiers in Neuroinformatics 05/2014; 8. DOI:10.3389/fninf.2014.00057 · 3.26 Impact Factor
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    • "Magnetoencephalography (MEG), a relatively new clinical neuroimaging modality, is well suited for the study of epileptic discharges because MEG can noninvasively detect and localize neuromagnetic signals [2] [3] [4] [5]. Brain activity in a low-frequency range (<80 Hz) has been conventionally studied in epilepsy. "
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    ABSTRACT: This study aimed to use ictal high-frequency oscillations (HFOs) ranging from 80Hz to 500Hz to locate seizure onset zones in childhood absence epilepsy (CAE) using non-invasive magnetoencephalography (MEG). Ten drug-naïve children with CAE were studied using a 275-channel MEG system. MEG data were digitized at a sampling rate of 6000Hz. HFO spectral power in real-time spectrograms was assessed using Morlet continuous wavelet transform. Magnetic sources were volumetrically localized through dynamic magnetic source imaging with a slide window. HFOs were identified in all patients. The total time of fast ripples (250-500Hz) was greater than that of ripples (80-250Hz) during absence seizures. The rate of fast ripples was associated with seizure frequency. HFO duration was significantly longer when co-occurring with spikes than when occurring independently, and the maximum frequency of HFOs co-occurring with spikes was higher than that of HFOs occurring independently. HFOs were predominantly localized in the medial prefrontal cortex (MPFC), whereas spikes were widespread to a variety of regions during the absence seizures. Compared with spikes, HFOs appeared to be more focal. The findings indicate that HFOs in the MPFC have a primary function in initializing epileptic activity in CAE.
    Neuroscience Letters 02/2014; 566. DOI:10.1016/j.neulet.2014.02.038 · 2.03 Impact Factor
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