Connectome based modelling to reveal multi-scale mechanisms in stroke

基于连接组的建模揭示中风的多尺度机制

• 批准号: 347469655
• 负责人: Professor Dr. Christian Gerloff
• 金额: --
• 依托单位: Klinik und Poliklinik für Neurologie
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/347469655?language=en
• 关键词: Connectome; based; modelling; reveal; multi

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.

中风是一种毁灭性的疾病，具有很高的社会经济负担。目前的治疗和康复策略已经有所改善，但没有充分考虑个体疾病模式。这可能部分解释了目前的治疗结果并不令人满意。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基础。我们的目标是应用和完善开源分析和脑网络建模框架，该框架根据多模态脑成像数据创建全脑模拟，以供中风患者个体化临床使用。该技术资源称为“虚拟大脑”(TVB)，是在社区标准平台上开发的，具有跨多个站点实施的可用性和灵活性。在当前的项目中，TVB 将应用于跨学科跨区域的努力，以治疗现有的大型纵向中风患者队列。我们将调整生物物理大脑模型，以最佳地代表在从后天性大脑局灶性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。 TVB 在中风治疗中的使用最终将产生临床相关的应用，能够在干预前后模拟个体患者的大脑。这将允许选择单独定制的疗法，并更好地预测康复轨迹。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大脑模型和机制来实现这一目标，即可以从 TVB 模型推断出的微观过程。目标。我们的目标是提供概念证明，证明 TVB 可用于识别中风的微观（即细胞）过程，从而预测个体水平上的治疗成功。我们的具体目标是 1) 虚拟化中风患者在疾病急性和慢性阶段的大脑，并揭示改变的生物物理参数； 2) 根据生物物理参数确定恢复潜力的候选生物标志物； 3）通过双脉冲经颅磁刺激有针对性地探测中心和边缘特性（例如定向抑制或促进），验证推断标记的神经生理学解释； 4) 创建一个具有标准化处理流程的交互式数据共享工具，允许跨中心汇集数据，并提高应用 TVB 的临床中风研究的能力。影响。该项目的新颖之处在于从信息处理架构破坏的角度来理解中风作为一种脑部疾病，这超出了传统的形态学或神经生理学方法，即从建模推断出微观过程。