PDF(Pubmed)
Abstract：
Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual\'s brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject\'s brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer\'s disease, multiple sclerosis, Parkinson\'s disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.
摘要：
虚拟大脑双胞胎是个性化的，基于个人大脑数据的生成和自适应大脑模型，供科学和临床使用。在描述了虚拟大脑双胞胎的关键元素之后，我们提出了个性化全脑网络模型的标准模型。个性化是通过三种方式使用受试者的大脑成像数据完成的：（1）在受试者特定的大脑空间中组装皮层和皮层下区域；（2）直接将连通性映射到大脑模型中，可以推广到其他参数；(3)通过模型反演估计相关参数，通常使用概率机器学习。我们介绍了个性化全脑网络模型在健康老龄化和五种临床疾病中的应用：癫痫，老年痴呆症,多发性硬化症,帕金森病和精神疾病。具体来说,我们引入了相关参数的空间掩模，并根据生理和病理生理假设演示了它们的使用。最后,我们确定了关键挑战和未来方向。
