ex-vivo circuit-wide recordings from hippocampal-cortical and olfactory bulb slices using high-density microelectrode array (hd-mea)

Multimodal Large-Scale Neural Recordings and Analysis on HD-MEA 

Neuronal ensembles, often termed cell assemblies, are pivotal in neural coding, facilitating intricate computations for processing multiscale neural information1,2,3. These ensembles underpin the formation of expansive neuronal networks and their nuanced microcircuits4. Such networks and their oscillatory patterns drive advanced brain functions, including perception and cognition. While extensive research has explored specific neuronal types and synaptic pathways, a deeper understanding of how neurons collaboratively form cell assemblies and influence spatiotemporal information processing across circuits and networks remains elusive5.
Acute, ex-vivo brain slices are pivotal electrophysiological tools for studying intact neural circuits, offering a controlled setting to probe oscillatory activity patterns of neural function, synaptic transmission, and connectivity, with implications in pharmacological testing and disease modeling6,7,8. This study protocol highlights two key brain circuits - the hippocampal-cortical (HC) involved in learning and memory processes9,10, and the olfactory bulb (OB) responsible for odor discrimination11,12,13. In these two regions, new functional neurons are continuously generated by adult neurogenesis throughout life in mammalian brains14. Both circuits demonstrate multidimensional dynamic neural activity patterns and inherent plasticity that participate in rewiring the existing neural network and facilitate alternative information processing strategies when required15,16.
Acute, ex-vivo brain slice models are indispensable for delving into brain functionality and understanding disease mechanisms at the microcircuit level. However, in-vitro cell cultures derived from human induced pluripotent stem cells (iPSCs) neuronal networks offer a promising avenue of translational research, seamlessly connecting findings from animal experiments to potential human clinical treatment17,18. These human-centric in-vitro assays serve as a reliable platform for assessing pharmacological toxicity, enabling precise drug screening, and furthering research into innovative cell-based therapeutic strategies19,20. Recognizing the pivotal role of the iPSC neuronal model, we have dedicated the third module of this protocol study to thoroughly investigate the functional characteristics of its derived networks and to fine-tune the associated cell culture protocols.
These electrogenic neural modules have been commonly studied using techniques like calcium (Ca2+ imaging), patch-clamp recordings, and low-density microelectrode arrays (LD-MEA). While Ca2+ imaging offers single-cell activity mapping, it is a cell-labeling-based method hindered by its low temporal resolution and challenges in long-term recordings. LD-MEAs lack spatial precision, while patch-clamp, being an invasive single-site technique and laborious, often yields a low success rate21,22,23. To address these challenges and effectively probe network-wide activity, large-scale simultaneous neural recordings have emerged as a pivotal approach for understanding the computational principles of neural dynamics underlying brain complexity and their implications in health and disease24,25.
In this JoVE protocol, we demonstrate a large-scale neural recording method based on the high-density MEA (HD-MEA) for capturing spatiotemporal neuronal activity across various brain modalities, including hippocampal and olfactory bulb circuits from ex-vivo mouse brain acute slices (Figures 1A-C) and in-vitro human iPSC-derived neuronal networks (Figures 1D-E), previously reported by our group and other colleagues26,27,28,29,30,31,32,33,34,35. The HD-MEA, built on complementary-metal-oxide-semiconductor (CMOS) technology, boasts on-chip circuitry and amplification, allowing sub-millisecond recordings across a 7mm2 array size36. This non-invasive approach captures multi-site, label-free extracellular firing patterns from thousands of neuronal ensembles simultaneously using 4096 microelectrodes at a high spatiotemporal resolution, revealing the intricate dynamics of local field potentials (LFPs) and multiunit spiking activity (MUA)26,29.
Given the vastness of the data generated by this methodology, a sophisticated analytical framework is essential, yet poses challenges37. We have developed computational tools that encompass automatic event detection, classification, graph theory, machine learning, and other advanced techniques (Figure 1F)26,29,38,39. Integrating the HD-MEA with these analytical tools, a holistic approach is devised to probe the intricate dynamics from individual cell assemblies to broader neural networks across diverse neural modalities. This combined approach deepens our grasp of the computational dynamics in normal brain functions and offers insights into anomalies present in pathological conditions28. Moreover, insights from this approach can propel advancements in brain-inspired modeling, neuromorphic computing, and neural learning algorithms. Ultimately, this method holds promise in uncovering the core mechanisms behind neural network disruptions, potentially identifying biomarkers, and guiding the creation of precise diagnostic tools and targeted treatments for neurological conditions.

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Our research explores the frontiers of neural technology by integrating high-density CMOS-based microelectrode array for decoding neural communication and large networks. We aim to answer how neural information across scales is encoded in unique detail, enhancing our understanding of brain function and dysfunction in health and disease. Navigating the complex area of neuronal ensemble research, we confront challenges like achieving precise signal resolution amidst brain activity, and ensuring the biocompatibility of our CMOS-based microelectrode arrays.These hurdles are pivotal in accurately capturing and interpreting the rich tapestry of neural interaction using multimodal recordings. Our research addresses a critical gap in neuroscience, the lack of a comprehensive method to recode and analyze the dynamics of larger scale neuronal ensemble with high spatial and temporal resolution. This gap hinders our understanding of complex brain networks and function in health and disease.Our protocol enables multimodal, label-free, high-resolution recordings across the hippocampus, olfactory bulb, and human IPSC-derived neurons, providing a versatile tool for diverse experiments. This unique approach facilitates unparalleled insight into neuronal dynamics, bridging the research gap between various brain regions and model systems, significantly advancing our understanding of neural function and disorder. Future endeavors in our lab will deeply investigate neural computations and dynamics from genes to networks, aiming to bridge molecular and functional signatures in health and disease.Through advanced bioelectronics and neural technology, we'll focus on neuroplasticity, olfactory coding, developing AI, and memory-enhancing strategies for novel therapeutics and brain machine interfaces.

Watch this Scientific Journal Video about Ex-Vivo Circuit-Wide Recordings from Hippocampal-Cortical and Olfactory Bulb Slices Using High-Density Microelectrode Array HD-MEA at JoVE.com

Ex-Vivo Circuit-Wide Recordings from Hippocampal-Cortical and Olfactory Bulb Slices Using High-Density Microelectrode Array (HD-MEA)

ex-vivo-circuit-wide-recordings-from-hippocampal-cortical-olfactory

Research

JoVE Journal

Neuroscience

382 조회수.  German Center for Neurodegenerative Diseases (DZNE). 시작하려면 필요한 모든 도구를 지정된 작업 공간에 배치하십시오. HD-MEA 칩을 PDLO로 코팅하여 조직-칩 결합을 향상시킵니다. 획득 기록 플랫폼에 칩을 놓습니다.저장소를 인공 뇌척수액으로 채우고 칩 저장소에 앵커를 놓아 평형을 이룹니다. 회수실에서 준비된 마우스 뇌 절편을 꺼내 연속 카보진이 있는 90mm 배양 접시에 넣습니다. 해마 또는 후각구를 분리한 후 유리 ?...

비디오: HD-MEA(High-Density Microelectrode Array)를 사용한 해마-피질 및 후각 전구 슬라이스의 Ex-Vivo 회로 전체 기록

Large-scale neuronal networks and their complex distributed microcircuits are essential to generate perception, cognition, and behavior that emerge from patterns of spatiotemporal neuronal activity. These dynamic patterns emerging from functional groups of interconnected neuronal ensembles facilitate precise computations for processing and coding multiscale neural information, thereby driving higher brain functions. To probe the computational principles of neural dynamics underlying this complexity and investigate the multiscale impact of biological processes in health and disease, large-scale simultaneous recordings have become instrumental. Here, a high-density microelectrode array (HD-MEA) is employed to study two modalities of neural dynamics - hippocampal and olfactory bulb circuits from ex-vivo mouse brain slices and neuronal networks from in-vitro cell cultures of human induced pluripotent stem cells (iPSCs). The HD-MEA platform, with 4096 microelectrodes, enables non-invasive, multi-site, label-free recordings of extracellular firing patterns from thousands of neuronal ensembles simultaneously at high spatiotemporal resolution. This approach allows the characterization of several electrophysiological network-wide features, including single/-multi-unit spiking activity patterns and local field potential oscillations. To scrutinize these multidimensional neural data, we have developed several computational tools incorporating machine learning algorithms, automatic event detection and classification, graph theory, and other advanced analyses. By supplementing these computational pipelines with this platform, we provide a methodology for studying the large, multiscale, and multimodal dynamics from cell assemblies to networks. This can potentially advance our understanding of complex brain functions and cognitive processes in health and disease. Commitment to open science and insights into large-scale computational neural dynamics could enhance brain-inspired modeling, neuromorphic computing, and neural learning algorithms. Furthermore, understanding the underlying mechanisms of impaired large-scale neural computations and their interconnected microcircuit dynamics could lead to the identification of specific biomarkers, paving the way for more accurate diagnostic tools and targeted therapies for neurological disorders.

Here, we employ HD-MEA to delve into computational dynamics of large-scale neuronal ensembles, particularly in hippocampal, olfactory bulb circuits, and human neuronal networks. Capturing spatiotemporal activity, combined with computational tools, provides insights into neuronal ensemble complexity. The method enhances understanding of brain functions, potentially identifying biomarkers and treatments for neurological disorders.

Large-scale neuronal networks and their complex distributed microcircuits are essential to generate perception, cognition, and behavior that emerge from ...