神经周围网
神经科学
连接体
精神分裂症(面向对象编程)
计算机科学
生物
神经可塑性
功能连接
程序设计语言
作者
Mikhail Paveliev,Anton Egorchev,Foat Musin,Nikita Lipachev,Anastasiia Melnikova,Rustem M. Gimadutdinov,Aidar Kashipov,Dmitry Molotkov,D.E. Chickrin,А. В. Аганов
摘要
Perineuronal nets (PNN) are a special highly structured type of extracellular matrix encapsulating synapses on large populations of CNS neurons. PNN undergo structural changes in schizophrenia, epilepsy, Alzheimer’s disease, stroke, post-traumatic conditions, and some other brain disorders. The functional role of the PNN microstructure in brain pathologies has remained largely unstudied until recently. Here, we review recent research implicating PNN microstructural changes in schizophrenia and other disorders. We further concentrate on high-resolution studies of the PNN mesh units surrounding synaptic boutons to elucidate fine structural details behind the mutual functional regulation between the ECM and the synaptic terminal. We also review some updates regarding PNN as a potential pharmacological target. Artificial intelligence (AI)-based methods are now arriving as a new tool that may have the potential to grasp the brain’s complexity through a wide range of organization levels—from synaptic molecular events to large scale tissue rearrangements and the whole-brain connectome function. This scope matches exactly the complex role of PNN in brain physiology and pathology processes, and the first AI-assisted PNN microscopy studies have been reported. To that end, we report here on a machine learning-assisted tool for PNN mesh contour tracing.
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