Mapping brain network activity from structural connectivity using AI and Deep Learning

结构-功能关系是自然科学的基础。神经系统被组织成一个逐步复杂和相互联系的神经群体的层次结构。传统上，解剖学信息提供了高度的生物学特异性和可解释性，但它不足以表征个体之间的差异，部分原因是神经回路功能受到组织微结构变化的影响。现代神经成像允许探索和高度详细地重建大脑的结构和功能连接网络。利用这些，早期的模型证明了结构相连的区域之间存在功能耦合。然而，功能区之间的相关性既取决于通路的存在，也取决于从整个网络接收的信号。为了解释这一点，人们开发了通过神经种群之间的高阶相互作用来预测功能的模型，但它们要么缺乏生物学上的可信性，要么很难在个体之间推广。最近的统计方法成功地使用微结构衍生的方法来预测功能连通性的变化，而基于神经网络的技术在预测功能连通性方面已经取得了一些成功；然而，直接从结构信息估计功能连通性的目标仍然难以实现。该项目建立在以前的工作的基础上，利用从扩散磁共振和静息状态功能磁共振提取的信息来突出结构和功能之间的相关性，通过学习个体的结构和功能连通性之间的潜在关系来开发能够在结构和功能连通性之间进行转换的DL算法。由于能够学习复杂的特征和关系，CNN和GANS等算法在医学成像中的应用越来越多。这项任务既涉及学习模式，也涉及几个移除的突触之间的直接信号和功能关联的表示。所需的生物学洞察力和可变性是通过使用来自英国生物库的大型临床成像数据集实现的。为了保留结构路径和功能信息的空间上下文相关性，在联合通道间建模之前不使用数据约简。这一点，再加上丰富的模式间建模所需的高得多的维度，刺激了比典型的DL成像应用更复杂的算法的开发。最初，网络的输入是使用标准空间协议从特定于对象的概率扩散束成像获得的27个白质束的总和，确保提供足够的空间信息以允许学习相关的路径和功能相关性。目标是rsfMRI空间地图，通过集团级ICA获得的21个主要功能分区中，最初使用的是默认模式网络。其他的模式和数据，如个人的遗传、生活方式、认知和身体测量，将被添加为输入，而输出将包括所有功能细分，从而帮助确定它们与个人的功能连接性之间是否可以建立相关性(以及它与总体平均水平的不同方式)。实现这一点将有助于开发概率模型，评估个体偏离群体分布的情况，并确定对此有贡献的特定大脑区域。此外，这也有助于为身体受损的受试者开发术前功能映射方法，而不需要挑战明确的认知/运动任务。该项目属于EPSRC医学成像研究领域，但也有助于人工智能技术和图像和视觉计算。这是与F.Hoffmann-La Roche的合作

The structure-function relationship is fundamental to natural sciences. The nervous system is organised as a hierarchy of progressively complex and interconnected neural populations. Traditionally, anatomical information provided high biological specificity and interpretability, yet it was inadequate for characterising the differences between individuals, in part because neural circuit functionality is influenced by tissue microstructure variations.Modern neuroimaging allows probing into, and highly detailed reconstructions of, the brain's structural and functional connectivity networks. Using these, early modelling proved the existence of functional coupling between structurally linked regions. Correlations between functional regions however depend both on the presence of pathways and signals received from the overall network. To account for this, models predicting function through higher order interactions between neural populations were developed, yet they either lacked biological plausibility or were hard to generalise across individuals. Recent statistical methods successfully used microstructure derived measures to predict functional connectivity variations while neural networks based techniques have seen some success in predicting functional connectivity; however, the goal of estimating this directly from structural information has remained elusive.This project builds on previous work highlighting correlations between structure and function using information extracted from diffusion MRI and resting-state functional MRI by developing a DL algorithm capable of translating between an individual's structural and functional connectivities, by learning the underlying relationships between them. Algorithms such as CNNs and GANs have been increasingly used in medical imaging due to their ability to learn complex features and relationships. This task involves both learning patterns and representations of direct signal and functional correlations between regions several synapses removed. The required biological insight and variability is achieved by using large clinical imaging datasets from UK Biobank. To retain the spatial contextual correlations of structural pathways and functional information, no data reductions are used before the joint between-modality modelling. This, together with the substantially higher dimensionality required for rich between-modality modelling stimulate the development of algorithms of much higher complexity than typical DL imaging applications.Initially, the inputs to the network are the summation of 27 white matter tracts obtained from subject-specific probabilistic diffusion tractography using standard-space protocols, ensuring sufficient spatial information is provided to allow the learning of relevant pathway and functional correlations. The targets are the rsfMRI spatial maps, with the Default Mode Network being initially used out of the 21 major functional sub-divisions obtained through group-level ICA. Additional modalities and data, such as an individual's genetics, lifestyle, cognitive and physical measures, will be later added as inputs while the outputs will incorporate all the functional sub-divisions, thus aiding in determining whether correlations can be established between them and an individual's functional connectivity (and the way in which it differs from the population average). Achieving this would contribute to the development of probabilistic models assessing an individual's deviation from the population distribution and identify specific brain regions which contribute to this. Moreover, this could also aid in the development of pre-surgical functional mapping methods for physically impaired subjects without the need for challenging explicit cognitive/motor tasks.This project falls within the EPSRC Medical Imaging research area, but also contributes to the Artificial Intelligence Technologies and Image and Vision Computing. It is a collaboration with F.Hoffmann-La Roche