ABSTRACT
The organizational principles of the community architecture of human brain networks are still mostly unknown. Here, we found that brain networks have a moderate degree of community segregation but are specifically organized to achieve high community overlap while maintaining their segregated community structures. These properties are distinct from other real-world complex networks. Additionally, we found that human subjects with a higher degree of community overlap in their brain networks show greater dynamic reconfiguration and cognitive flexibility.
Subject(s)
Brain , Nerve Net , Brain/physiology , Brain Mapping/methods , Humans , Magnetic Resonance ImagingABSTRACT
Developing effective drugs for Alzheimer's disease (AD), the most common cause of dementia, has been difficult because of complicated pathogenesis. Here, we report an efficient, network-based drug-screening platform developed by integrating mathematical modeling and the pathological features of AD with human iPSC-derived cerebral organoids (iCOs), including CRISPR-Cas9-edited isogenic lines. We use 1300 organoids from 11 participants to build a high-content screening (HCS) system and test blood-brain barrier-permeable FDA-approved drugs. Our study provides a strategy for precision medicine through the convergence of mathematical modeling and a miniature pathological brain model using iCOs.
Subject(s)
Alzheimer Disease/drug therapy , Alzheimer Disease/pathology , Brain/pathology , Drug Evaluation, Preclinical , Gene Regulatory Networks , Organoids/pathology , Alzheimer Disease/genetics , Cinnamates/pharmacology , Cinnamates/therapeutic use , Gene Regulatory Networks/drug effects , High-Throughput Screening Assays , Humans , Induced Pluripotent Stem Cells/metabolism , Models, Biological , Reproducibility of Results , Risk FactorsABSTRACT
The brain controls various cognitive functions in a robust and efficient way. What is the control architecture of brain networks that enables such robust and optimal control? Is this brain control architecture distinct from that of other complex networks? Here, we developed a framework to delineate a control architecture of a complex network that is compatible with the behavior of the network and applied the framework to structural brain networks and other complex networks. As a result, we revealed that the brain networks have a distributed and overlapping control architecture governed by a small number of control nodes, which may be responsible for the robust and efficient brain functions. Moreover, our artificial network evolution analysis showed that the distributed and overlapping control architecture of the brain network emerges when it evolves toward increasing both robustness and efficiency.
