Multiscale Brain Network Models and Their Applications in Neuropsychiatric Diseases

Abstract

With the rapid development of advanced neuroimaging techniques, understanding the brain in terms of structural and functional connectomes has become one of the frontier topics in neuroscience. Different from traditional descriptive brain network models, which focused on single neuroimaging modal and temporal scales, multiscale brain network models consisting of mesoscopic neuronal activity and macroscopic functional dynamics can provide a mechanistic understanding for brain disorders. Here, we review the foundation of multiscale brain network models and their applications in neuropsychiatric diseases. We first describe some basic elements of a multiscale brain network model, including network connections, dynamics of regional neuronal populations, and model fittings to different metrics of fMRI. Secondly, we draw comparisons between multiscale brain network models and other large-scale brain models. Additionally, then we survey the related applications of multiscale brain network models in understanding underlying mechanisms of some brain disorders, such as Parkinson’s disease, Alzheimer’s disease, and Schizophrenia. Finally, we discuss the limitations of current multiscale brain network models and future potential directions for model development. We argue that multiscale brain network models are more comprehensive than traditional single modal brain networks and would be a powerful tool to explore neuronal mechanisms underlying different brain disorders measured by neuroimaging.