Individualised modelling of dynamic brain networks in fMRI.

功能磁共振成像中动态大脑网络的个性化建模。

• 批准号: 2747512
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2747512
• 关键词: Individualised; modelling; dynamic; brain; networks

The brain is a complex organ. Functional magnetic resonance imaging (fMRI) is a powerful non-invasive tool to record brain activity in humans. It detects the blood-oxygen-level-dependent (BOLD) signal as an indirect measure of neural activity, with high spatial resolution across different brain regions. To study how these regions interact with each other in brain networks, scientists calculate functional connectivity, which in fMRI is the correlation between BOLD signals from distinct regions. Neuroscientists have identified brain networks that coordinate with each other in both rest and task. Traditionally, functional connectivity is estimated using a static approach - calculating a single correlation value for each pair of brain regions using the entirety of a long scanning session. Recently, there has been increasing interest in understanding brain network dynamics, i.e., dynamics in functional connectivity. Brain activity is expected to be changing all the time, and hence time-varying network descriptions can capture information being missed by static approaches. Another important trend in this field is to consider subject variability. For example, the spatial locations of functional brain regions can vary over subjects. Typically, functional brain regions are identified by grouping together voxels with similar activity. A single timeseries is then extracted for the brain region and fed in as data to the dynamic approaches such as the Hidden Markov Modelling (HMM). Obtaining these brain region time courses has previously been done using group-level Independent Component Analysis (ICA) combined with dual regression to give a subject-specific version of each subject's functional brain region. However, this has been shown to underperform compared to newly developed methods such as PROFUMO, which explicitly model subject variability in a generative model. Accounting for subject variability is critically important for understanding the human brain in both health and diseased; as, rather than just one overall vague group description, it provides more specific descriptions tuned to different types of brains. This is crucial for the delivery of personalised medicine in the era of big data. The last decade has witnessed the development of large-scale publicly available neuroimaging data, such as the Human Connectome Project (HCP) and UK Biobank (UKB), and posed a great challenge: how do we understand functioning and malfunctioning of individual subject brain networks by using information from the large cohort? This DPhil project faces up to the challenge from the perspective of dynamic brain networks. We aim to model network dynamics with subject-specific spatiotemporal variability in fMRI. First, we will develop robust and trustworthy metrics to validate the different dynamic models such as the Hidden Markov Modelling (HMM) and Dynamic Network Modes (Dynemo). We will explore Bayesian inference metrics (such as variational free energy) and machine learning methods (such as cross validation) to measure the ability of different models to reliably represent the brain network dynamics. This will inform model and hyperparameter selection for our future work. Second, we are going to explore the genetic basis of network dynamics. Previous work has found that dynamics are highly heritable using twin structure in HCP data. We will go beyond this work by exploring associations between single nucleotide polymorphisms and phenotypes obtained from existing dynamic network models in UK Biobank data. Finally, we will build a new dynamic network model that better handles subject variability. Different from previous methods, this model will be end-to-end - combining these two steps into one large model with subject-specific descriptions on 100,000 subjects of UKB data.This project falls within the EPSRC research area of medical imaging - including medical image and vision computing. The industrial supervisor is Dr. Stanislaw Adaszeewski from Roche.

大脑是一个复杂的器官。功能磁共振成像(FMRI)是一种强大的非侵入性工具，可以记录人类的大脑活动。它检测血氧水平依赖(BOLD)信号，作为神经活动的间接测量，具有跨不同大脑区域的高空间分辨率。为了研究大脑网络中这些区域如何相互作用，科学家们计算了功能连接性，在fMRI中，功能连接性是来自不同区域的大胆信号之间的关联。神经学家已经确定了在休息和任务中相互协调的大脑网络。传统上，功能连通性是使用静态方法来估计的--使用整个长扫描过程来计算每对大脑区域的单个相关值。最近，人们对了解脑网络动力学，即功能连接的动力学越来越感兴趣。大脑活动预计会一直在变化，因此时变的网络描述可以捕捉到静态方法遗漏的信息。这个领域的另一个重要趋势是考虑主题的可变性。例如，大脑功能区的空间位置可能会因受试者而异。通常，大脑功能区域是通过将具有相似活动的体素组合在一起来识别的。然后提取大脑区域的单个时间序列，并将其作为数据馈送到动态方法，如隐马尔可夫模型(HMM)。这些大脑区域的时间进程以前是通过组级独立成分分析(ICA)和双重回归相结合来获得每个受试者大脑功能区域的特定受试者版本。然而，与Profumo等新开发的方法相比，这已经被证明表现不佳，Profumo在生成性模型中明确地模拟了受试者的可变性。考虑受试者的可变性对于理解健康和疾病中的人类大脑至关重要；因为，它提供了更具体的描述，而不是一个整体的模糊的群体描述，以适应不同类型的大脑。这对于大数据时代的个性化医疗服务至关重要。在过去的十年里，见证了大规模公开可用的神经成像数据的发展，如人类连接组项目(HCP)和英国生物库(UKB)，并提出了一个巨大的挑战：我们如何通过使用来自大队列的信息来理解单个受试者大脑网络的功能和故障？这个DPhil项目从动态大脑网络的角度出发，直面挑战。我们的目标是在功能磁共振成像中用特定于受试者的时空变异性来模拟网络动力学。首先，我们将开发稳健和可信的度量来验证不同的动态模型，如隐马尔可夫模型(HMM)和动态网络模式(DYNEMO)。我们将探索贝叶斯推理度量(如变分自由能)和机器学习方法(如交叉验证)，以衡量不同模型可靠地表示脑网络动态的能力。这将为我们今后的工作提供模型和超参数选择的参考。其次，我们将探索网络动力学的遗传基础。以前的工作已经发现，在HCP数据中使用孪生结构的动力学是高度可遗传的。我们将超越这项工作，探索单核苷酸多态与从英国生物库数据中现有动态网络模型获得的表型之间的关联。最后，我们将建立一个新的动态网络模型，更好地处理主题的可变性。与以前的方法不同，这个模型将是端到端的-将这两个步骤结合到一个大型模型中，其中包含对10万个UKB数据对象的特定主题描述。该项目属于EPSRC医学成像-包括医学图像和视觉计算-的研究领域。行业主管是来自罗氏的斯坦尼斯瓦夫·阿达谢夫斯基博士。