Connectome based modelling to reveal multi-scale mechanisms in stroke

中风是一种具有高社会经济负担的破坏性医疗状况。目前的治疗和康复战略有所改进，但没有充分考虑到个别疾病的模式。这可能部分解释了目前的治疗结果不令人满意。先进的神经影像学提供了必要的新技术来创建个性化的疾病评估。它可以为改善治疗和个性化康复提供依据。我们的目标是应用和完善一个开源的分析和脑网络建模框架，该框架从多模态脑成像数据中创建全脑模拟，用于中风患者的个性化临床应用。这项名为“虚拟大脑”（TVB）的技术资源是在一个社区标准平台上开发的，可以在多个网站上灵活使用。在目前的项目中，TVB将应用于现有的大型纵向中风患者队列的跨学科跨区域的努力。我们将调整生物物理大脑模型，以最佳地表示在从获得性脑损伤恢复过程中重新学习和发展失去的运动技能的过程中改变的大脑连接和功能。在卒中设置中使用TVB应最终产生临床相关应用程序，能够在干预前后模拟个体患者的大脑。这将允许选择个性化的治疗，并将更好地预测恢复的轨迹。我们将通过识别低于非侵入性成像的空间和时间分辨率的大多数通用脑模型和机制来实现这一目标，即，微观尺度的过程，可以从TVB模型推断。目标.我们的目标是提供概念证明，即TVB可用于识别微尺度（即，细胞的）过程，其在个体水平上预测治疗成功。我们的具体目标是：1）虚拟化处于疾病急性和慢性阶段的中风个体患者的大脑，并揭示改变的生物物理参数; 2）基于生物物理参数识别恢复潜力的候选生物标志物; 3）通过有针对性地探测中枢和边缘特性（如定向抑制或促进）来验证推断的标记物的神经生理学解释，通过双脉冲经颅磁刺激; 4）创建具有标准化处理管道的交互式数据共享工具，允许跨中心汇集数据，并增加应用TVB的临床中风研究的能力。冲击该项目的新奇之处在于，从信息处理架构中断的角度来理解中风是一种大脑疾病，这超出了传统的形态学或神经生理学方法，即，从模型中推断出的微观过程。

Stroke is a devastating medical condition with high socioeconomic burden. Current treatment and rehabilitation strategies have improved but do not account sufficiently for individual disease patterns. This may partly explain that current treatment results are not satisfactory. Advanced neuroimaging provides the necessary new technology to create personalized disease assessment. It can provide grounds for improved treatment and personalized rehabilitation. We aim at applying and refining an open-source analysis and brain network-modelling framework that creates whole-brain simulations from multimodal brain imaging data for individualized clinical use in stroke patients. The technological resource, called The Virtual Brain (TVB), has been developed on a community standard platform, allowing usability and flexibility for implementation across multiple sites. In the current project, TVB will be applied in an interdisciplinary transregional effort to an existing large longitudinal cohort of stroke patients. We will adjust the biophysical brain model to optimally represent altered brain connectivity and function in the process of re-learning and developing lost motor skills during recovery from an acquired focal damage to the brain. The use of TVB in the setting of stroke shall ultimately result in a clinically relevant application enabling to simulate individual patient brains before and after interventions. This will allow for selection of individually tailored therapies and will better predict trajectories of recovery. We will achieve this goal by identifying most generic brain models and mechanisms below the spatial and temporal resolution of non-invasive imaging, i.e., micro-scale processes which can be inferred from the TVB model. Objectives. We aim at providing proof of concept that TVB is useful to identify micro-scale (i.e., cellular) processes in stroke that are predictive of therapy success on an individual level. Our specific goals are to 1) virtualize the brains of individual patients with stroke at the acute and chronic stages of the disease and reveal altered biophysical parameters; 2) identify candidate biomarkers of the recovery potential based on biophysical parameters; 3) validate the neurophysiological interpretation of the inferred markers through targeted probing of hub and edge properties, like directed inhibition or facilitation, by double-pulse transcranial magnetic stimulation; 4) create an interactive data sharing tool with standardized processing pipelines allowing to pool data across centres and increase the power of clinical stroke studies that apply TVB. Impact. The novelty of the project is to understand stroke as a brain disorder from the perspective of disruption of information processing architectures that goes beyond the conventional morphological or neurophysiological approaches, i.e., towards micro-scale processes inferred from modelling.