Whole-brain computational modelling for the characterisation of and transition between brain states

用于表征大脑状态和之间转换的全脑计算模型

• 批准号: 2747395
• 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-2747395
• 关键词: Whole; brain; computational; modelling; characterisation

Whole-brain computational modelling is an emerging area in the mathematical and computational study of the human brain that merges neural data and connectomics with generative mathematical models of brain dynamics. This project will explore the exciting applications of this novel approach to the study of brain states in an effort to improve understanding of the relationship between cognitive functions and spatiotemporal brain dynamics. A particular focus will be placed on the study of states with various levels of consciousness such as wakefulness, deep sleep, anaesthesia, coma and other diseases of consciousness. The characterisation of brain states will be studied using data science and signal processing techniques to extract latent features in neuroimaging and neuroelectric data, such as fMRI or MEG, from a range of patient populations in varying states of consciousness. DTI and fMRI data can be combined with the probabilistic tractography to obtain the structural and functional connectivities, according to some parcellation of brain regions. Promising lines of research exist in the measurement of causality, complexity, entropy production and statistical irreversibility of neural signals using measures from dynamical systems theory, information theory and statistical mechanics. In addition to the characterisation of brain states from neural data, the project will further aim to build generative whole-brain models using a framework built on networks of coupled oscillators and mean field approximations. Existing mathematical models will be extended to incorporate the temporally changing structure of brain networks as a result of network degeneration, a key feature absent from many current whole-brain models. Another feature of the whole-brain models that will be further developed is the inclusion of neurotransmitter dynamics which are key for modelling the effects of pharmacological interventions. From a mathematical perspective, this project will facilitate the development of novel techniques in neural data science as well as dynamics on networks. Using statistical inference and optimisation, whole brain models are traditionally fitted to both the structural and time-series data but could also be fit to the latent features characterising a brain state once these are determined. Furthermore, in this project we will aim to model the forcing of transitions between states by modelling interventions such as deep brain stimulation, transcranial magnetic stimulation or pharmacological stimulation, such as psychedelic therapy. A computational model of this kind could provide an effective way to perturb the system and compare the effects of a range of treatments in a systematic way that is not feasible in vivo. The aims of this project are to facilitate in-silico experimentation that could provide new insight into the relationship between higher level cognitive abilities and the underlying brain dynamics, as well as to motivate the development of novel treatments and biomarkers for diseases of consciousness with the potential for real patient impact in otherwise difficult patient populations. This work falls under the EPSRC mathematical sciences theme as well as some overlap with the healthcare technologies theme and with a particular relevancy to the EPSRC areas of interest in biological informatics and mathematical biology. This project will be joint between the Mathematical Institute and the Centre for Eudaimonia and Human Flourishing.

全脑计算建模是人脑数学和计算研究中的一个新兴领域，它将神经数据和连接组学与脑动力学的生成数学模型相结合。该项目将探索这种新方法在大脑状态研究中的令人兴奋的应用，以提高对认知功能和时空大脑动力学之间关系的理解。一个特别的重点将放在研究具有不同意识水平的状态，如清醒，深度睡眠，麻醉，昏迷和其他意识疾病。将使用数据科学和信号处理技术研究大脑状态的表征，以从处于不同意识状态的一系列患者人群中提取神经成像和神经电数据（如fMRI或MEG）中的潜在特征。DTI和fMRI数据可以与概率纤维束成像结合，根据大脑区域的某些分区来获得结构和功能连接。有前途的研究领域存在的因果关系，复杂性，熵产生和统计不可逆性的神经信号的测量使用的措施，从动力系统理论，信息论和统计力学。除了从神经数据中表征大脑状态外，该项目还将进一步致力于使用基于耦合振荡器和平均场近似网络的框架来构建生成性全脑模型。现有的数学模型将被扩展，以纳入由于网络退化而导致的大脑网络的时间变化结构，这是目前许多全脑模型所缺乏的关键特征。将进一步开发的全脑模型的另一个特点是包括神经递质动力学，这是模拟药物干预效果的关键。从数学的角度来看，该项目将促进神经数据科学以及网络动力学的新技术的发展。使用统计推断和优化，全脑模型传统上适合于结构和时间序列数据，但也可以适合于表征大脑状态的潜在特征。此外，在这个项目中，我们的目标是通过模拟干预措施，如脑深部电刺激，经颅磁刺激或药物刺激，如迷幻治疗，来模拟状态之间的强制转换。这种计算模型可以提供一种有效的方法来干扰系统，并以系统的方式比较一系列治疗的效果，这在体内是不可行的。该项目的目的是促进计算机实验，可以提供更高水平的认知能力和潜在的大脑动力学之间的关系的新见解，以及激励开发新的治疗方法和生物标志物的意识疾病的潜在真实的患者的影响，否则困难的患者群体。这项工作属于福尔斯的EPSRC数学科学主题，以及一些重叠的医疗保健技术的主题，并与生物信息学和数学生物学的EPSRC领域的利益特别相关。该项目将由数学研究所和Eudaimonia和人类繁荣中心联合开展。