Correlating neuronal activity and large volume nanoscale imaging using AI

了解大脑的工作原理——这是人工智能发展的主要动力——需要了解大脑各个神经回路的连接方式。这可以通过电子显微镜对大量脑组织成像，然后在纳米尺度上绘制神经元（突触）之间的连接来实现。然而，尽管通过这种努力得出的“接线图”对于理解大脑是必要的，但它本身是不够的。我们还需要关于每个连接功能的信息，即每个突触在信息信号传递方面的有效性。在这个提议中，我们的主要目标是用突触活动的读数来补充大脑回路接线图（“连接体”），使我们能够更好地理解健康和疾病中大脑结构和功能之间的关系。我们最近开发了一种实验技术，可以直接解决这一挑战，可以测量大脑体积内所有突触的强度和活动。这是通过一种特殊类型的电子显微镜来实现的，这种电子显微镜可以产生具有非凡分辨率的功能脑图。然而，这些数据集非常大，人类几乎不可能进行大规模分析。为了解决这个问题，我们与人工智能图像分析领域的专家合作伙伴Jan Funke （Janelia Research Campus-USA）合作，开发了强大的机器学习方法，可以自动读出这些电路的布线和功能属性。我们的实验方法包括在目标脑回路的突触中引入一种特殊的标记物，以读出功能性信息。为了描述不包括该标记的神经回路现有数据集中的结构-功能关系，我们将建立在人工智能和连接组学之间的密切联系上-完全符合BBSRC的职权范围和本次呼叫的范围-创建一个新的基于人工智能的工具，使我们能够仅基于结构评估连接组中的突触功能。这意味着世界各地的研究人员已经收集的许多接线图可以重新分析，以增加关键的功能信息。为了实现这一令人兴奋的目标，我们标记的实验数据将作为“基础事实”，人工智能网络将在此数据集上进行训练，以学习如何将突触的结构特征与功能特性联系起来。这种方法将在该领域取得重大进展：组织体积越大，开发和优化新的自动化工具以加速发现的需求就越迫切。因此，我们的方法将对许多对生成大脑回路功能图感兴趣的研究人员产生革命性的影响。通过共享数据和方法，我们将为连接组学领域做出贡献，使其更加公平，并为更广泛的科学界所利用。这种协作文化将惠及由于经济劣势和/或缺乏获得此类实验所需技术的机会而无法利用此类数据和方法的受益者。它将使神经科学和人工智能社区受益，并将为全球现有的庞大数据集增加巨大价值，从而加深我们对生物和人工神经网络如何运作的理解。最终，我们新开发的人工智能工具也将有潜力从医学图像的结构中预测功能，这可以支持和促进诊断，改善结果，扩大这一伙伴关系工作对转化领域的影响，并对社区福利产生积极影响。

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.