Robust graph analysis of brain connectivity

一系列神经系统疾病和障碍都与大脑连接的病理改变有关。然而，缺乏可靠的方法来识别和表征连接中的异常，它们随时间的演变和对治疗的反应；解决这一遗漏代表着紧迫的临床需求。在这项提议中，我们的目标是使用基于医学成像和图论的新方法来满足这一需求，图论是描述和表征相互关联的系统的成熟的数学框架。为了建立一个明确的基线，我们将描述典型的“正常”连接网络的特征，并对其在健康人群中的可变性进行统计建模。我们还将研究将图表分类的方法，并确定潜在的结构和功能连接的共同因素，作为新的生物标记物。我们将使用磁共振成像(MRI)和脑电(EEG)来获取活脑中的连接信息；儿童癫痫和自闭症谱系障碍将被调查为神经疾病，可以显示大脑连接的改变。我们的发展也将适用于其他可以用图形表示的数据。这项工作将为脑连通性的图形分析提供重要的新工具，从而推动连通性网络方法走向可靠和常规的临床应用。通过我们与临床同事的密切联系，我们将确保我们的开发为广泛的适用性做好准备，阐明疾病中的连接故障及其通过治疗恢复的情况。

A broad range of neurological diseases and disorders have been linked with pathological alterations in the connectivity of the brain. However, robust methods for identifying and characterising abnormalities in connectivity, their evolution over time and response to treatments, are lacking; and addressing this omission represents a pressing clinical need. In this proposal we aim to meet this need using novel methods based on medical imaging and graph theory, a well-established mathematical framework with which to describe and characterise interconnected systems. To establish a clear baseline, we will characterise the typical "normal" connectivity network, and model statistically its variability in a healthy population. We will also investigate approaches to classifying graphs into groups, and identify common factors underlying structural and functional connectivity, to be used as new biomarkers. We will use magnetic resonance imaging (MRI) and electroencephalography (EEG) to obtain connectivity information in the living brain; and childhood epilepsy and autism spectrum disorders will be investigated as neurological disorders which can display altered brain connectivity. Our developments will also be applicable to other data which can be represented graphically. This work will provide major novel tools for graph analysis of brain connectivity, thereby driving connectivity network methods towards reliable and routine clinical application. Through our close links with clinical colleagues we will ensure that our developments are well positioned for wide-ranging applicability, elucidating connectivity failures in disease and their recovery with treatment.