Imaging dynamical brain networks using hybrid dynamical models

功能性大脑网络的动态重组可能与大脑状态的变化有关，例如与适应和学习有关的变化。从临床角度来看，大脑区域的相对贡献（可塑性）或其相互作用（有效连接）的变化支持许多发育（例如阅读障碍）和精神病（例如抑郁症，精神病）的早期阶段，但最突出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，有效连接的变化可能先于大脑结构或观察到的行为的任何变化。如果早期干预和早期康复策略要取得成功，通过EEG（或MEG）等非侵入性成像技术早期检测和监测这些变化至关重要。目前大多数有效连接（EC）的分析假设的架构和连接强度的功能网络是静态的时间，或在已知的时间点的任务条件之间的不同。因此，当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，例如在上述神经病症的持续EEG监测和诊断期间。我建议开发一种新的模型和工具箱，用于估计正在进行的EEG的基础上的动态脑网络的时间依赖性的有效连接，并测试其准确性和限制，在检测经颅磁刺激（TMS）引起的有效脑连接的变化。来自扩散加权磁共振成像（DWMRI）的先前解剖连接信息和概率图谱将分别用于告知模型功能网络的可能架构和源的可能解剖位置。我们的目标是提供一个低成本和快速的应用程序来检测，跟踪和预测大脑因果网络的早期变化，并从正在进行的EEG动态。鉴于目前的重点是减少神经退行性疾病的社会和经济影响，正如总理的痴呆症挑战所强调的那样，这里的具体重点是使用TMS来诱导分布式网络的两个范例中的微小变化，这些网络模拟语义和运动神经退行性疾病，以证明这种方法可以检测到什么。该模型有可能改变神经退行性疾病的筛查和早期诊断策略。它为开发新的临床应用开辟了可能性，这些应用对大脑老化和心理健康的研究产生了影响，并对各种大脑活动进行了分析，包括决策，休息状态和社会行为期间的正常活动。

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.