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iPROBE: in-vivo Platform for the Real-time Observation of Brain Extracellular activity

iPROBE: in-vivo Platform for the Real-time Observation of Brain Extracellular activity
iPROBE:实时观察脑细胞外活动的体内平台
批准号:
EP/K015141/1
负责人:
Kenneth Harris
金额:
$33.79万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2013
资助国家:
英国
项目状态:
已结题
起止时间:
2013 至 --

项目摘要

项目成果

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中文摘要
翻译
了解大脑数十亿个神经元的万亿动作电位如何产生我们的思想、感知和行动,是21世纪科学面临的最大挑战之一。同样,了解这种活动是如何被神经和精神疾病扰乱的,是21世纪医学面临的最大挑战之一。由于大脑计算的大规模并行性质,从实验上回答这些问题依赖于能够同时监测非常大量的神经元。电极微制造和高通量数据分析的进步使科学家能够记录大脑小区域内数百个神经元的数据。然而,由于健康和不健康的神经运作都是由多个广泛分布的大脑电路相互作用产生的,理解它需要一种技术步骤-改变,允许监测许多大脑区域的更多数量的神经元。对这一提议的研究将首次使这成为可能。这不仅将为了解健康的大脑如何运作提供一个以前无法想象的机会,还将使我们和其他人能够开发基于经验的治疗方法,以治疗帕金森氏症、癫痫、精神分裂症和阿尔茨海默氏症等疾病。大规模神经元记录依赖于微制造多电极阵列(MEA)的使用。能够记录数百个局部神经元的阵列现在已经商业化。原则上,这些阵列提供了记录来自多个大脑结构的数千个神经元的能力,只需同时使用大量探头即可。然而,使用现有技术无法访问这些电极产生的数据,因为在当前的无源连接系统中,根本不可能传递足够数量的极低幅度模拟信号。我们将使用一种在计算中常见的方法来解决这个问题:菊花链数字串行接口。通过实现几个多电极阵列的简单、健壮和低噪声连接,这将使我们能够使用单个接口来监控来自多个结构的数千个神经元。该系统将利用廉价的、商业上可用的微电极阵列(例如,NeuroNexus),通过高密度柔性带状电缆连接到定制的CMOS集成电路(IC)。CMOSIC成本低、产量高、面积效率高,适用于放大、滤波、模数转换和编码每个电极阵列的尖峰神经元数据。每个菊花链(即一组串联连接的探头)将端接到标准USB接口。新的USB-3.0协议(使用超高速术语销售)可以支持5Gbps的串行数据速度。对于以25kS/秒、12位分辨率采样的数据,这可以提供能够支持超过10,000个电极的带宽:比目前的技术高出两个数量级。我们开发的记录系统将产生大量数据。因此,该平台的第二个也是必不可少的部分是开发算法和软件,这些算法和软件对于及时将这些信息转换为关于大脑功能的简明结论至关重要。我们将通过利用我们以前的工作来做到这一点,我们的工作现在是处理多神经元记录的事实上的全球标准。我们的目标是产生一个被英国和世界各地的神经科学界广泛采用的系统,从而最大限度地影响对非常广泛的疾病的理解和治疗。为了确保该系统满足基础和临床脑研究的需求,我们的团队包括世界领先的神经元群体记录专家以及英国领先的神经记录系统制造商。因此,我们不仅拥有开发该系统所需的专业知识,而且还使其能够迅速商业化并分发给世界各地的科学家。
英文摘要
Understanding how the trillions of action potentials of the brain's billions of neurons produce our thoughts, perceptions, and actions is one of the greatest challenges of 21st century science. Similarly, understanding how this activity is disrupted by neurological and psychiatric diseases is one of the greatest challenges of 21st century medicine. Due to the massively parallel nature of the brain's computations, answering these questions experimentally relies on being able to monitor very large numbers of neurons simultaneously. Advances in electrode microfabrication and high-throughput data analysis have allowed scientists to record from hundreds of neurons in a small local area of brain. However, as both healthy and unhealthy neural operation arises from interaction of multiple, widely-distributed brain circuits, its understanding requires a technological step-change that allows monitoring of much larger numbers of neurons over many brain areas. The research of this proposal will for the first time make this possible. This will not only provide a previously unimaginable opportunity for understanding how the healthy brain functions, but also allow us and others to develop empirically-based treatments for diseases such as Parkinson's, epilepsy, schizophrenia, and Alzheimer's. Large-scale neuronal recording relies on the use of microfabricated multielectrode arrays (MEAs). Arrays capable of recording from hundreds of local neurons are now commercially available. In principle, these arrays provide the ability to record from thousands of neurons across multiple brain structures, simply by using a large number of probes simultaneously. However, accessing the data produced by these electrodes cannot be achieved with current technologies, as it is simply impossible to pass a sufficient number of very low amplitude analogue signals, as in current passive connection systems. We will solve this problem by using an approach common in computing: a daisy-chain digital serial interface. By allowing simple, robust, and low-noise connection of several multi-electrode arrays, this will allow us to monitor thousands of neurons from multiple structures using a single interface. The system will exploit cheap, commercially available microelectrode arrays (eg. NeuroNexus), connected to a custom CMOS Integrated Circuit (IC) via high-density flexible ribbon cables. CMOS ICs are low cost, produce high yield and area efficient active electronics suitable for amplifying, filtering, analog-to-digital conversion and encoding of each electrode array's spiking neuron data. Each daisy chain (i.e. group of serially-connected probes) will terminate into a standard USB interface. The new USB-3.0 protocol (marketed using the SuperSpeed term) can allow for serial data speeds of 5Gbps. For data sampled at 25kS/sec at 12-bit resolution, this could provide a bandwidth capable of supporting over 10,000 electrodes: two orders of magnitude beyond current technology.The recording systems we develop will produce vast quantities of data. A second, and essential, part of the platform is thus to develop the algorithms and software that are essential for the timely conversion of this information to concise conclusions about brain function. We will do this by leveraging our previous work, now the de facto worldwide standard for processing of multi-neuron recordings.Our aim is to produce a system that is widely adopted by the UK and worldwide neuroscientific communities, thereby maximizing its impact on the understanding and treatment of a very wide range of disorders. To ensure that the system meets the need of both basic and clinical brain research, our team includes the world's leading expert on neuronal population recording, as well as the UK's leading manufacturer of neural recording systems. We thus have the expertise needed not only to develop the system, but also enable its rapid commercialization and distribution to scientists worldwide.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
Cortical computation in mammals and birds.
哺乳动物和鸟类的皮质计算。
DOI: 10.1073/pnas.1502209112
发表时间: 2015
期刊: Proceedings of the National Academy of Sciences of the United States of America
影响因子: 11.1
作者: [Harris KD]
通讯作者: Harris KD
Sleep replay meets brain-machine interface.
睡眠回放与脑机接口的结合。
DOI: 10.1038/nn.3769
发表时间: 2014
期刊: Nature neuroscience
影响因子: 25
作者: [Harris KD]
通讯作者: Harris KD
DOI: 10.1162/neco_a_00661
发表时间: 2014-11
期刊: Neural computation
影响因子: 2.9
作者: [Kadir SN, Goodman DF, Harris KD]
通讯作者: Harris KD
DOI: 10.1007/s10827-014-0505-9
发表时间: 2014-10
期刊: JOURNAL OF COMPUTATIONAL NEUROSCIENCE
影响因子: 1.2
作者: [Le Mouel, Charlotte, Harris, Kenneth D., Yger, Pierre]
通讯作者: Yger, Pierre
共 8 条
    Computations of transcriptomic neuron types in cortex
    • 批准号:
      EP/Y028295/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $269.67万
    • 财政年份:
      2024
    • 负责人:
      Kenneth Harris
    • 依托单位:
    Neuronal mechanisms of learning-evoked stimulus orthogonalization
    • 批准号:
      BB/W015293/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $77.03万
    • 财政年份:
      2022
    • 负责人:
      Kenneth Harris
    • 依托单位:
    Cellular-resolution in situ transcriptomics of the mouse brain and Alzheimer's disease models
    • 批准号:
      MR/V003402/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $110.16万
    • 财政年份:
      2021
    • 负责人:
      Kenneth Harris
    • 依托单位:
    The Neural Marketplace
    • 批准号:
      EP/I005102/2
    • 项目类别:
      Fellowship
    • 资助金额:
      $90.08万
    • 财政年份:
      2012
    • 负责人:
      Kenneth Harris
    • 依托单位:
    国内基金
    海外基金
    基于ex vivo模型联合多组学手段绘制胃癌曲妥珠单抗继发耐药机制并探索克服耐药策略
    • 批准号:
      82072728
    • 项目类别:
      面上项目
    • 资助金额:
      55.0万元
    • 批准年份:
      2020
    • 负责人:
      高静
    • 依托单位:
    神经干细胞治疗帕金森病大鼠模型:在体(in vivo)实时记录纹状体多巴胺分泌
    • 批准号:
      81571235
    • 项目类别:
      面上项目
    • 资助金额:
      57.0万元
    • 批准年份:
      2015
    • 负责人:
      康新江
    • 依托单位:
    基于in vivo动力学分析的波动环境下黑曲霉产酶得率调控机制研究
    • 批准号:
      21506052
    • 项目类别:
      青年科学基金项目
    • 资助金额:
      21.0万元
    • 批准年份:
      2015
    • 负责人:
      夏建业
    • 依托单位:
    siRNA基因沉默与诱导双向基因治疗关节炎的软骨、滑膜生物学响应及ex vivo系统转基因在体示踪研究
    • 批准号:
      81171774
    • 项目类别:
      面上项目
    • 资助金额:
      60.0万元
    • 批准年份:
      2011
    • 负责人:
      张海宁
    • 依托单位: