Wang Huifang E, Triebkorn Paul, Breyton Martin, Dollomaja Borana, Lemarechal Jean-Didier, Petkoski Spase, Sorrentino Pierpaolo, Depannemaecker Damien, Hashemi Meysam, Jirsa Viktor K
Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France.
Service de Pharmacologie Clinique et Pharmacosurveillance, AP-HM, Marseille, 13005, France.
Natl Sci Rev. 2024 Feb 28;11(5):nwae079. doi: 10.1093/nsr/nwae079. eCollection 2024 May.
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.
虚拟脑孪生子是基于个体大脑数据的个性化、生成性和适应性脑模型，用于科学和临床应用。在描述了虚拟脑孪生子的关键要素之后，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利用受试者的脑成像数据来实现的：（1）在受试者特定的脑空间中组装皮质和皮质下区域；（2）将连接性直接映射到脑模型中，这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。我们展示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、阿尔茨海默病、多发性硬化症、帕金森病和精神疾病。具体来说，我们介绍了相关参数的空间掩码，并基于生理和病理生理假设展示了它们的用途。最后，我们指出了关键挑战和未来方向。
