Title: Virtual brain twins: from basic neuroscience to clinical use

Author: Wang, Huifang E, Triebkorn, Paul, Breyton, Martin, Dollomaja, Borana, Lemarechal, Jean-Didier, Petkoski, Spase, Sorrentino, Pierpaolo, Depannemaecker, Damien, Hashemi, Meysam, Jirsa, Viktor K

Issue&Volume: 2024-02-28

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 description of 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 generalised 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 uses: epilepsy, Alzheimer’s disease, multiple sclerosis, Parkinson’s disease and psychiatric disorder. 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.

DOI: 10.1093/nsr/nwae079

Source: https://dx.doi.org/10.1093/nsr/nwae079