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

全脑计算模型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学与脑动力学的生成性数学模型相结合。该项目将探索这一新方法在脑状态研究中令人兴奋的应用，以努力提高对认知功能和时空脑动力学之间关系的理解。重点将放在具有不同意识水平的状态的研究上，如觉醒、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来研究大脑状态的特征，以从处于不同意识状态的一系列患者群体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相结合，根据大脑某些区域的划分，获得结构和功能的连接性。利用动力系统理论、信息论和统计力学的方法，在神经信号的因果关系、复杂性、熵产生和统计不可逆性的度量方面存在着很有前途的研究方向。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一个建立在耦合振荡器和平均场近似网络上的框架来建立生成性全脑模型。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结构的时间变化，这是当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征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和网络动力学方面的新技术的发展。通过使用统计推断和优化，全脑模型传统上既适用于结构数据，也适用于时间序列数据，但一旦确定了这些数据，也可以适用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，如脑深部刺激、经颅磁刺激或药物刺激，如迷幻疗法，来模拟状态之间的转换的强迫。这种计算模型可以提供一种有效的方法来扰乱系统，并以一种在体内不可行的系统方式比较一系列治疗的效果。该项目的目的是促进电子实验，为更高水平的认知能力和潜在的大脑动力学之间的关系提供新的见解，并推动意识疾病的新治疗方法和生物标记物的开发，在其他困难的患者群体中可能产生真正的患者影响。这项工作属于EPSRC数学科学主题，并与医疗保健技术主题有一些重叠，与EPSRC生物信息学和数学生物学感兴趣的领域特别相关。该项目将由数学研究所和幸福与人类繁荣中心联合开展。

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.