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.

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Background: Calcium promotes gastric wound repair, but the in vivo mechanisms are unknown. Results: Signaling pathways sequentially mobilize intracellular and extracellular calcium as a requirement to repair gastric lesions. PMCA1 mediates extracellular calcium increases. Conclusion: Endogenous calcium is an essential second and third messenger driving gastric repair. Significance: Signaling and ion flux pathways are newly identified targets for enhancing gastric repair.

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Aberrant brain activity in childhood absence epilepsy (CAE) during seizures has been well recognized as synchronous 3 Hz spike-and-wave discharges on electroencephalography. However, brain activity from low- to very high-frequency ranges in subjects with CAE between seizures (interictal) has rarely been studied. Using a high-sampling rate magnetoencephalography (MEG) system, we studied ten subjects with clinically diagnosed but untreated CAE in comparison with age- and gender-matched controls. MEG data were recorded from all subjects during the resting state. MEG sources were assessed with accumulated source imaging, a new method optimized for localizing and quantifying spontaneous brain activity. MEG data were analyzed in nine frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low-gamma (30-55 Hz), high-gamma (65-90 Hz), ripple (90-200 Hz), high-frequency oscillation (HFO, 200-1,000 Hz), and very high-frequency oscillation (VHFO, 1,000-2,000 Hz). MEG source imaging revealed that subjects with CAE had higher odds of interictal brain activity in 200-1,000 and 1,000-2,000 Hz in the parieto-occipito-temporal junction and the medial frontal cortices as compared with controls. The strength of the interictal brain activity in these regions was significantly elevated in the frequency bands of 90-200, 200-1,000 and 1,000-2,000 Hz for subjects with CAE as compared with controls. The results indicate that CAE has significantly aberrant brain activity between seizures that can be noninvasively detected. The measurements of high-frequency neuromagnetic oscillations may open a new window for investigating the cerebral mechanisms of interictal abnormalities in CAE.