Imaging dynamical brain networks using hybrid dynamical models

大脑功能网络的动态重组可能与大脑状态的变化有关，例如与适应和学习相关的变化。从临床角度来看，大脑区域(可塑性)或其相互作用(有效连通性)的相对贡献的变化是许多发育性(例如阅读障碍)和精神(例如抑郁症、精神病)的早期阶段的基础，但最显著的是神经退行性疾病(例如阿尔茨海默氏症或帕金森氏症)。由于可塑性，有效连通性的变化可能先于大脑结构或观察到的行为的任何变化。如果要取得早期干预和早期康复策略的成功，通过脑电(或脑磁图)等非侵入性成像技术及早发现和监测这种变化是至关重要的。目前对有效连通性的分析大多假定功能网络的结构和连接强度在时间上是静态的，或者在已知时间点的不同任务条件下是不同的。因此，目前的方法不能用于在任意时间点或在没有认知任务的情况下检测网络组织的动态变化，例如在正在进行的脑电监测和上述神经疾病的诊断期间。我建议开发一个新的模型和工具箱来估计持续脑电背后的动态脑网络的随时间变化的有效连通性，并测试其在检测经颅磁刺激(TMS)引起的有效脑连通性变化方面的准确性和局限性。来自扩散加权磁共振成像(DWMRI)的先前解剖连通性信息和概率图谱将分别用于向模型提供有关功能网络的可能架构和源的可能解剖位置的信息。其目标是提供一种低成本和快速的应用程序，以从正在进行的脑电中检测、跟踪和预测大脑因果网络及其动力学的早期变化。鉴于当前的重点是减少神经变性的社会和经济影响，正如首相的痴呆症挑战所强调的那样，这里的具体重点是使用TMS在模拟语义和运动神经退化的两个分布式网络样本中诱导微小变化，以演示该方法可以检测到什么。建议的模型有可能改变对神经退行性疾病的筛查和早期诊断的策略。它为开发新的临床应用打开了可能性，这些应用影响到对大脑老化和精神健康的研究，以及对各种大脑活动的分析，包括决策过程中的正常、休息状态和社会行为。

Dynamic reorganisation of a functional brain network may be related to shifts in brain state, such as those associated with adaptation and learning. From a clinical perspective, changes in the relative contribution of brain areas (plasticity) or their interactions (effective connectivity) underpin the early stages of a number of developmental (e.g Dyslexia) and psychiatric (e.g. depression, psychosis), but most prominently neurodegenerative (e.g. Alzheimer's or Parkinson's) conditions. Due to plasticity, changes in effective connectivity may precede any changes in brain structure or in observed behaviour. Early detection and monitoring of such changes by means of non-invasive imaging techniques like EEG (or MEG) is vital if early intervention and early rehabilitation strategies were to be successful . Most current analyses of effective connectivity (EC) assume that the architecture and connection strengths of the functional network are static in time, or differ between task conditions at known time points. Thus, current approaches cannot be used to detect dynamic changes in network organisation at arbitrary time points or in the absence of a cognitive task, such as during on-going EEG monitoring and diagnosis of the above neurological conditions. I propose to develop a novel model and a toolbox for estimating the time-dependent effective connectivity of the dynamical brain network underlying the on-going EEG, and to test its accuracy and limits in detecting changes in effective brain connectivity induced by Transcranial Magnetic Stimulation (TMS). Prior anatomical connectivity information and probabilistic atlases, derived from Diffusion Weighted Magnetic Resonance Imaging (DWMRI), will be used to inform the model about the likely architecture of the functional network and about the likely anatomical location of sources, respectively. The goal is to provide a low-cost and fast application to detect, track and predict early changes in brain causal networks and their dynamics from the on-going EEG. Given the current emphasis on reducing the social and economic impact of neurodegeneration as highlighted by the Prime Minister's dementia challenge, the specific focus here is on using TMS to induce small changes in two exemplars of distributed networks that simulate semantic and motor neurodegeneration as a demonstration of what this method can detect. The proposed model has the potential to change strategies for screening and early diagnosis of neurodegenerative conditions. It opens the possibility for developing new clinical applications that impact on the study of the aging brain and mental health, as well as the analysis of a wide variety of brain activity including normal during decision-making, resting-state and social behaviour.