Correlating neuronal activity and large volume nanoscale imaging using AI

使用 AI 将神经元活动与大体积纳米级成像关联起来

• 批准号: BB/Y51391X/1
• 负责人: Claudia Racca
• 金额: $ 32.92万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY51391X%2F1
• 关键词: Correlating; neuronal; activity; large; volume

Understanding how the brain works - a major driving force for the development of AI - requires knowledge of the wiring of its individual neural circuits. This can be achieved using electron microscopy to image large volumes of brain tissue and then map the connections between neurons (synapses) at the nanometre-scale. However, while the 'wiring diagram' that results from this effort is necessary to understand the brain, it is not, by itself, sufficient. We also need information about the function of each connection - i.e., how effective each synapse is at information signalling.In this proposal, our key aim is to complement brain circuit wiring diagrams ('connectomes') with a readout of synaptic activity, allowing us to better understand the relationship between brain structure and function in health and disease.We recently developed an experimental technique that directly addresses this challenge, allowing measurements of strength and activity of all synapses within a brain volume. This is achieved using a special type of electron microscopy that yields functional brain maps with extraordinary resolution. However, these datasets are very large and almost impossible for humans to analyse at scale. To solve this, in collaboration with an expert partner in AI for image analysis, Jan Funke (Janelia Research Campus-USA), we have developed powerful machine-learning approaches that can automate the readout of wiring and functional properties of these circuits.Our experimental approach involves introducing a special marker into synapses of target brain circuits to read out functional information. In order to describe structure-function relationships in existing datasets from neural circuits that do not include this marker, we will build on the close links between AI and connectomics -fully aligned with the BBSRC remit and the scopes of this call - to create a new AI-based tool that will enable us to assess synaptic function in connectomes based on structure alone. This means that the many wiring diagrams already collected by researchers across the world could be re-analysed to add crucial functional information. To achieve this exciting objective, our labelled experimental data will act as the 'ground truth', and AI networks will be trained on this dataset to learn how to relate structural characteristics of synapses to functional properties.This approach will provide a major advance in the field: the larger the tissue volume, the more compelling is the need to develop and optimise new automated tools to accelerate discovery. Our approach will thus be transformative for the many researchers interested in generating functional maps of circuits in the brain. By sharing data and methodology, we will contribute to the field of connectomics and make it more equitable and accessible to the broader scientific community. This collaborative culture will reach beneficiaries that would not otherwise be able to capitalise on such data and methodology because of economical disadvantages and/or lack of access to the technology requiredfor such experiments. It will benefit both the neuroscience and AI communities, and will add enormous value to the vast existing datasets available globally, thus deepening our understanding of how biological and artificial neural networks operate.Ultimately, our newly developed AI tools will also have the potential to predict function from structure in medical images, which could support and facilitate diagnoses, improve outcomes, widen the impact of this partnership's work to translational fields and make a positive-impact on the community's welfare.

了解大脑的工作原理 - 开发AI的主要驱动力 - 需要了解其单个神经回路的接线。这可以使用电子显微镜来成像大量脑组织，然后在纳米尺度上绘制神经元（突触）之间的连接。但是，虽然这项努力造成的“接线图”是必要的，但这本身就不够。我们还需要有关每个连接功能的信息 - 即，每个突触在信息信号中的有效性。在此提案中，我们的关键目的是补充脑电路接线图（“连接组”），并读取突触活动，使我们能够更好地理解大脑结构和疾病中的关系。我们最近在实验技术中开发了一个直接范围的概述，从而在概述中进行了概述，并允许在概述中进行概述，并在整体上进行了概述。这是使用一种特殊类型的电子显微镜来实现的，该电子显微镜产生具有非凡分辨率的功能性脑图。但是，这些数据集非常大，几乎不可能对人类进行大规模分析。为了解决这一问题，与AI的专家合作进行图像分析，Jan Funke（Janelia Research Campus-Usa），我们开发了强大的机器学习方法，可以自动化这些电路的接线和功能属性的读数。您的实验方法涉及将特殊的脑循环标记引入功能性信息的特殊标记，以读取功能性信息。为了描述不包含此标记的神经电路中现有数据集中的结构功能关系，我们将建立在AI和连接组之间的密切链接上 - 与BBSRC汇总和此呼叫的范围保持一致，以创建基于AI的新工具，以创建一个新的基于AI的工具，该工具将使我们能够基于结构单独评估连接中的突触功能。这意味着，可以重新分析全球研究人员已经收集的许多接线图，以添加关键的功能信息。为了实现这一激动人心的目标，我们标记的实验数据将充当“地面真相”，AI网络将在该数据集上进行培训，以学习如何将突触的结构性特征与功能属性联系起来。这种方法在领域中提供了重大进展：组织量越大，组织量越大，需要开发和优化新的自动化工具以加强新自动化的工具。因此，对于有兴趣生成大脑中电路功能图的许多研究人员，我们的方法将具有变革性。通过共享数据和方法，我们将为连接组的领域做出贡献，并使更广泛的科学界更加公平和访问。这种协作文化将吸引受益人，因为经济的缺点和/或缺乏对此类实验所需的技术的访问，否则将无法利用此类数据和方法。它将受益于神经科学和AI社区，并将为全球可用的广泛现有数据集增加巨大的价值，从而加深我们对生物学和人工神经网络如何运作的理解。据此，我们的新开发的AI工具还可以预测对医学图像中的结构中的功能，从而可以支持和促进诊断，并促进诊断，并促进诊断，并促进诊断的影响，并促进诊断的影响，并促进诊断的影响，并且会影响诊断的范围。 福利。