Tentacle Microelectrode Arrays Uncover Soft Boundary Neurons in Hippocampal CA1.

Hippocampal CA1 neurons show intense firing at specific spatial locations, modulated by isolated landmarks. However, the impact of real-world scene transitions on neuronal activity remains unclear. Moreover, long-term neural recording during movement challenges device stability. Conventional rigid-based electrodes cause inflammatory responses, restricting recording durations. Inspired by the jellyfish tentacles, the multi-conductive layer ultra-flexible microelectrode arrays (MEAs) are developed. The tentacle MEAs ensure stable recordings during movement, thereby enabling the discovery of soft boundary neurons. The soft boundary neurons demonstrate high-frequency firing that aligns with the boundaries of scene transitions. Furthermore, the localization ability of soft boundary neurons improves with more scene transition boundaries, and their activity decreases when these boundaries are removed. The innovation of ultra-flexible, high-biocompatible tentacle MEAs improves the understanding of neural encoding in spatial cognition. They offer the potential for long-term in vivo recording of neural information, facilitating breakthroughs in the understanding and application of brain spatial navigation mehanisms.

High-Performance Bidirectional Microelectrode Array for Assessing Sevoflurane Anesthesia Effects and In Situ Electrical Stimulation in Deep Brain Regions.

Precise assessment of wakefulness states during sevoflurane anesthesia and timely arousal are of paramount importance to refine the control of anesthesia. To tackle this issue, a bidirectional implantable microelectrode array (MEA) is designed with the capability to detect electrophysiological signal and perform in situ deep brain stimulation (DBS) within the dorsomedial hypothalamus (DMH) of mice. The MEA, modified with platinum nanoparticles/IrOx nanocomposites, exhibits exceptional characteristics, featuring low impedance, minimal phase delay, substantial charge storage capacity, high double-layer capacitance, and longer in vivo lifetime, thereby enhancing the sensitivity of spike firing detection and electrical stimulation (ES) effectiveness. Using this MEA, sevoflurane-inhibited neurons and sevoflurane-excited neurons, together with changes in the oscillation characteristics of the local field potential within the DMH, are revealed as indicative markers of arousal states. During the arousal period, varying-frequency ESs are applied to the DMH, eliciting distinct arousal effects. Through in situ detection and stimulation, the disparity between these outcomes can be attributed to the influence of DBS on different neurons. These advancements may further our understanding of neural circuits and their potential applications in clinical contexts.

Real-Time Precise Targeting of the Subthalamic Nucleus via Transfer Learning in a Rat Model of Parkinson's Disease Based on Microelectrode Arrays.

In neurodegenerative disorders, neuronal firing patterns and oscillatory activity are remarkably altered in specific brain regions, which can serve as valuable biomarkers for the identification of deep brain regions. The subthalamic nucleus (STN) has been the primary target for DBS in patients with Parkinson's disease (PD). In this study, changes in the spike firing patterns and spectral power of local field potentials (LFPs) in the pre-STN (zona incerta, ZI) and post-STN (cerebral peduncle, cp) regions were investigated in PD rats, providing crucial evidence for the functional localization of the STN. Sixteen-channel microelectrode arrays (MEAs) with sites distributed at different depths and widths were utilized to record neuronal activities. The spikes in the STN exhibited higher firing rates than those in the ZI and cp. Furthermore, the LFP power in the delta band in the STN was the greatest, followed by that in the ZI, and was greater than that in the cp. Additionally, increased LFP power was observed in the beta bands in the STN. To identify the best performing classification model, we applied various convolutional neural networks (CNNs) based on transfer learning to analyze the recorded raw data, which were processed using the Gram matrix of the spikes and the fast Fourier transform of the LFPs. The best transfer learning model achieved an accuracy of 95.16%. After fusing the spike and LFP classification results, the time precision for processing the raw data reached 500 ms. The pretrained model, utilizing raw data, demonstrated the feasibility of employing transfer learning for training models on neural activity. This approach highlights the potential for functional localization within deep brain regions.

Research on the Characteristics of Action Potentials and Local Field Potentials in Cortex of Parkinson's Disease Mode Monkey.

Parkinson's disease (PD) is characterized by excessively synchronized neural activity. In this paper, we recorded electrophysiological signals in Cortex of normal and PD mode monkey using homemade implantable microelectrode arrays (MEA), and analyzed the characteristics of action potentials (APs) and local field potentials (LFPs). Results showed that, comparing to normal monkey, the spike-firing activity of PD mode monkey could be divided into two stages: the continuous spike-firing stage and the burst spike-firing stage. The continuous spike-firing lasted for about 20s and oscillated at low frequency about 0.03Hz. APs fired in a burst mode between two continuous discharges. In the continuous spike-firing stage, the spike-firing activity was related to the ripple rhythm (100-200Hz) of LFPs with a coherence 0.86, while, in the burst spike-firing stage, it was related to the phase of theta rhythm (4-7 Hz). APs tended to discharge in the valley of theta rhythm (average peak phase is -10°).Clinical Relevance- This article can provide some references for the study of PD neuropathology.

Inhibition of telomere recombination by inactivation of KEOPS subunit Cgi121 promotes cell longevity.

DNA double strand break (DSB) is one of the major damages that cause genome instability and cellular aging. The homologous recombination (HR)-mediated repair of DSBs plays an essential role in assurance of genome stability and cell longevity. Telomeres resemble DSBs and are competent for HR. Here we show that in budding yeast Saccharomyces cerevisiae telomere recombination elicits genome instability and accelerates cellular aging. Inactivation of KEOPS subunit Cgi121 specifically inhibits telomere recombination, and significantly extends cell longevity in both telomerase-positive and pre-senescing telomerase-negative cells. Deletion of CGI121 in the short-lived yku80(tel) mutant restores lifespan to cgi121Δ level, supporting the function of Cgi121 in telomeric single-stranded DNA generation and thus in promotion of telomere recombination. Strikingly, inhibition of telomere recombination is able to further slow down the aging process in long-lived fob1Δ cells, in which rDNA recombination is restrained. Our study indicates that HR activity at telomeres interferes with telomerase to pose a negative impact on cellular longevity.