Tonic extrasynaptic GABAA receptor (GABAAR) activation is under the tight control of tonic GABA release from astrocytes to maintain the brain's excitation/inhibition (E/I) balance; any slight E/I balance disturbance can cause serious pathological conditions including epileptic seizures. However, the pathophysiological role of tonic GABA release from astrocytes has not been tested in epileptic seizures. Here, we report that pharmacological or genetic intervention of the GABA‐permeable Bestrophin‐1 (Best1) channel prevented the generation of tonic GABA inhibition, disinhibiting CA1 pyramidal neuronal firing and augmenting seizure susceptibility in kainic acid (KA)‐induced epileptic mice. Astrocyte‐specific Best1 over‐expression in KA‐injected Best1 knockout mice fully restored the generation of tonic GABA inhibition and effectively suppressed seizure susceptibility. We demonstrate for the first time that tonic GABA from reactive astrocytes strongly contributes to the compensatory shift of E/I balance in epileptic hippocampi, serving as a good therapeutic target against altered E/I balance in epileptic seizures.

The phase reconstruction process in digital holographic microscopy involves a trade‐off between the phase error and the high‐spatial‐frequency components. In this reconstruction process, if the narrow region of the sideband is windowed in the Fourier domain, the phase error from the DC component will be reduced, but the high‐spatial‐frequency components will be lost. However, if the wide region is windowed, the 3D profile will include the high‐spatial‐frequency components, but the phase error will increase. To solve this trade‐off, we propose the high‐variance pixel averaging method, which uses the variance map of the reconstructed depth profiles of the windowed sidebands of different sizes in the Fourier domain to classify the phase error and the high‐spatial‐frequency components. Our proposed method calculates the average of the high‐variance pixels because they include the noise from the DC component. In addition, for the nonaveraged pixels, the reconstructed phase data created by the spatial frequency components of the widest window are used to include the high‐spatial‐frequency components. We explain the mathematical algorithm of our proposed method and compare it with conventional methods to verify its advantages.

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Digital holographic microscopy (DHM) is a three-dimensional (3D) imaging technique that uses the phase information of coherent light. In the reconstruction process of DHM, a narrow region around the positive or negative sideband from the Fourier domain is windowed to avoid noise due to the DC spectrum of the hologram spectrum. However, the limited size of the window also degrades the high-frequency information of the 3D object profile. Although a large window can have more detailed information of the 3D object shape, the noise is increased. To solve this trade-off, we propose phase difference averaging (PDA). The proposed method yields high-frequency information of the specimen while reducing the DC noise. In this paper, we explain the reconstruction algorithm for this method and compare it to various conventional filtering methods including Gaussian, Wiener, average, median, and bilateral filtering methods.

In the image processing method of digital holographic microscopy (DHM), we can obtain a phase information of an object by windowing a sideband in Fourier domain and taking inverse Fourier transform. In this method, it is necessary to window a wide sideband to obtain detailed information on the object. However, since the information of the DC spectrum is widely distributed over the entire range from the center of Fourier domain, the window sideband includes not only phase information but also DC information. For this reason, research on acquiring only the phase information of an object without noise in digital holography is a challenging issue for many researchers. Therefore, in this paper, we propose the use of a windowed sideband array (WiSA) as an image processing method to obtain an accurate three-dimensional (3D) profile of an object without noise in DHM. The proposed method does not affect the neighbor pixels of the filtered pixel but removes noise while maintaining the detail of the object. Thus, a more accurate 3D profile can be obtained compared with the conventional filter. In this paper, we create an ideal comparison target i.e., microspheres for comparison, and verify the effect of the filter through additional experiments using red blood cells.