Excitatory neurons and GABAergic interneurons constitute neural circuits and play important roles in information processing. In certain brain regions, such as the neocortex and the hippocampus, there are fewer interneurons than excitatory neurons. Interneurons have been quantified via immunohistochemistry, for example, for GAD67, an isoform of glutamic acid decarboxylase. Additionally, the expression level of other proteins varies among cell types. For example, NeuN, a commonly used marker protein for postmitotic neurons, is expressed differently across brain regions and cell classes. Thus, we asked whether GAD67-immunopositive neurons can be detected using the immunofluorescence signals of NeuN and the fluorescence signals of Nissl substances. To address this question, we stained neurons in layers 2/3 of the primary somatosensory cortex (S1) and the primary motor cortex (M1) of mice and manually labeled the neurons as either cell type using GAD67 immunosignals. We then sought to detect GAD67-positive neurons without GAD67 immunosignals using a custom-made deep learning-based algorithm. Using this deep learning-based model, we succeeded in the binary classification of the neurons using Nissl and NeuN signals without referring to the GAD67 signals. Furthermore, we confirmed that our deep learning-based method surpassed classic machine-learning methods in terms of binary classification performance. Combined with the visualization of the hidden layer of our deep learning algorithm, our model provides a new platform for identifying unbiased criteria for cell-type classification.

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Polyherbal medicines are composed of multiple herbs and have traditionally been used in East Asian countries for the remedy of physiological symptoms. Although the effects of polyherbal formulations have been investigated at the molecular and behavioral levels, less is known about whether and how medicinal herbs affect the central nervous system in terms of neurophysiology. We introduced a novel blended herbal formulation that consisted of 35% linden, 21% mulberry, 20% lavandin, 20% butterfly pea, and 4% tulsi. After intraperitoneal administration of this formulation or saline, we simultaneously recorded epidural electrocorticograms (ECoGs) from the olfactory bulb (OB), primary somatosensory cortex (S1), and primary motor cortex (M1), along with electromyograms (EMGs) and electrocardiograms (ECGs), of rats exploring an open field arena. Using the EMGs and OB ECoGs, we segmented the behavioral states of rats into active awake, quiet awake, and sleeping states. Compared to saline, herbal medicine significantly shortened the total sleep time. Moreover, we converted the ECoG signal into a frequency domain using a fast Fourier transform (FFT) and calculated the powers at various ECoG oscillation frequencies. In the sleeping state, a slow component (0.5-3 Hz) of S1 ECoGs was significantly enhanced following the administration of the formulation, which suggests a region-and frequency-specific modulation of extracellular field oscillations by the polyherbal medicine.

The complexity of brain functions is supported by the heterogeneity of brain tissue and millisecond-scale information processing. Understanding how complex neural circuits control animal behavior requires the precise manipulation of specific neuronal subtypes at high spatiotemporal resolution. In utero electroporation, when combined with optogenetics, is a powerful method for precisely controlling the activity of specific neurons. Optogenetics allows for the control of cellular membrane potentials through light-sensitive ion channels artificially expressed in the plasma membrane of neurons. Here, we first review the basic mechanisms and characteristics of in utero electroporation. Then, we discuss recent applications of in utero electroporation combined with optogenetics to investigate the functions and characteristics of specific regions, layers, and cell types. These techniques will pave the way for further advances in understanding the complex neuronal and circuit mechanisms that underlie behavioral outputs.