Processing of brain time series to unravel changes in dynamic functional connectivity in disease

处理大脑时间序列以揭示疾病中动态功能连接的变化

• 批准号: 2434508
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2434508
• 关键词: Processing; brain; time; series; unravel

This paper aims to describe the applicant's research approach on the subject of implementing new analysis and computational methods on acquired brain signals. Research in this area has started to emphasize the importance of getting a connectivity-based understanding of the relationship between fast time series events in the brain. In order to do this, computational models are being built as an approximation of brain functionality. The traditional, "stationary" approach to understanding brain dynamics is to consider the structural connectivity of the brain as the main neural activity mapping means in trying to identify the networks responsible for certain cognitive activities, as proposed in the 1980s. More modern approaches recognise the non-linear and evasive nature of neural paths while also taking into consideration the anatomical structure (more recently called the connectome). This is as they recognize that brain activity varies in time and space dependent on the type of activity the brain is involved in and on the mental state (Fornito et al., 2016) [1]. Since this historic milestone in neuroscience, research in the area of aim to create a correct approximation of the spatiotemporal nature of brain signals and build a coherent functional and effective connectivity model. A few main challenges could be identified in this direction:1. Moving away from brain modularity and instead identifying spontaneous and task-related connectivity general patterns. The model must also account for individual variability. This can be defined as the functional connectivity problem. To tackle this challenge, models are built to describe or predict neural pattern activation as response to an applied simulated excitation input, without emphasizing time dependency between different regions of the brain [2], [3]. 2. Identifying the causal relationship between statistically dependent signals - i.e. defining the effective connectivity (or dynamic functional connectivity), a complex mathematical and computational task. To overcome this challenge, models are built which allow a trial-and-error approach to identifying the cause of the studied brain activity [2], [3]. Unlike the previously mentioned type of models, this line of action emphasizes the causal relationship between activatedneural regions

本文旨在描述申请人关于对获得的脑信号实施新的分析和计算方法的主题的研究方法。这一领域的研究已经开始强调对大脑中快速时间序列事件之间关系的基于连接性的理解的重要性。为了做到这一点，计算模型正在被构建为大脑功能的近似。传统的理解大脑动力学的“静态”方法是将大脑的结构连接性视为主要的神经活动映射手段，试图识别负责某些认知活动的网络，如20世纪80年代提出的。更现代的方法认识到神经通路的非线性和回避性质，同时也考虑到解剖结构（最近称为连接体）。这是因为他们认识到，大脑活动在时间和空间上的变化取决于大脑参与的活动类型和精神状态（Fornito等人，2016）[1]。自这一神经科学的历史性里程碑以来，该领域的研究旨在创建对大脑信号时空性质的正确近似，并建立连贯的功能和有效的连接模型。在这方面可以确定几个主要挑战：1.远离大脑模块化，而是识别自发和任务相关的连接一般模式。该模型还必须考虑个体差异。这可以被定义为功能连接问题。为了应对这一挑战，建立模型来描述或预测神经模式激活作为对所应用的模拟激励输入的响应，而不强调大脑不同区域之间的时间依赖性[2]，[3]。2.识别统计相关信号之间的因果关系-即定义有效连通性（或动态功能连通性），这是一项复杂的数学和计算任务。为了克服这一挑战，建立了模型，允许试错法来识别所研究的大脑活动的原因[2]，[3]。与前面提到的模型类型不同，这一行动路线强调了激活的神经区域之间的因果关系