Analysing and interpreting neuroimaging data using mathematical frameworks for network dynamics

使用网络动力学数学框架分析和解释神经影像数据

基本信息

批准号：
2100870
负责人：
金额：
--
依托单位：
University of Nottingham
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2100870
关键词：
Analysing interpreting neuroimaging data using

项目摘要

Modern non-invasive probes of human brain activity, such as magneto-encephalography, give high temporal resolution and increasingly improved spatial resolution. With such a detailed picture of the workings of the brain, it becomes possible to use mathematical modelling to establish increasingly complete mechanistic theories of spatio-temporal neuroimaging signals. There is an ever-expanding toolkit of mathematical techniques for addressing the dynamics of oscillatory neural networks allowing for the analysis of the interplay between local population dynamics and structural network connectivity in shaping emergent spatial functional connectivity patterns. This project will be primarily mathematical in nature, making use of notions from nonlinear dynamical systems and network theory, such as coupled-oscillator theory and phase-amplitude network dynamics. Using experimental data and data from the output of dynamical systems on networks with appropriate connectivities, we will obtain insights on structural connectivity (the underlying network) versus functional connectivity (constructed from similarity of real time series or from time-series output of oscillator models on networks). The project will focus in particular on developing techniques for the analysis of dynamics on "multi-layer networks" to better understand functional connectivity within and between frequency bands of neural oscillations.

人脑活动的现代非侵入性探针，例如磁脑摄影，可提供高度分辨率和越来越改善的空间分辨率。有了如此详细的大脑起作用图，就有可能使用数学建模来建立越来越完整的时空神经影像学信号的机械理论。数学技术有一个不断扩展的工具包，用于解决振荡性神经网络的动力学，可以分析局部人群动力学与结构网络连接性之间的相互作用，以塑造新兴的空间功能连接模式。该项目本质上将主要是数学上的，它利用了非线性动力学系统和网络理论的概念，例如耦合振荡器理论和相位振幅网络动力学。使用具有适当连接性的网络上动态系统输出的实验数据和数据，我们将获得有关结构连接性（基础网络）与功能连接性的见解（由实时序列的相似性或网络上振荡器模型的时间序列输出构建）。该项目将特别集中在开发“多层网络”动力学分析的技术上，以更好地了解神经振荡频率频段内和之间的功能连接性。