标题： 挖掘大脑电路的功能架构 显示英文标题

标题： Data mining the functional architecture of the brain's circuitry

摘要： 大脑是一个高度复杂的器官，由无数子系统组成，这些子系统能够随着时间与环境灵活互动并适应，从而实现感知、认知和行为。 理解大脑的多尺度特性——即电路级与分子级相互作用如何构建起大脑功能的基本组成部分，对于开发神经退行性疾病和精神疾病的干预手段具有巨大潜力，并且有助于深入理解我们的本质。 历史上，技术限制迫使系统神经科学局限于解剖学（局部化、小范围神经群体在单一脑区）、行为学（研究单一任务）、时间维度（聚焦于学习或发展的特定阶段）以及模态（专注于单一生物量成像）。 神经记录技术和行为监测的新进展现在提供了突破局部神经科学走向全球神经科学所需的数据：即理解大脑众多子系统如何在动物和人类必须执行的众多行为中相互作用、适应和变化。 具体而言，尽管我们对大脑的解剖结构（即硬件）已有大量了解，但我们终于接近获得发现功能架构并揭示运行于硬件上的软件基本属性所需的大量数据。 我们必须抓住这个机会，通过从底层数据对齐到高阶可解释行为数学模型的诸多挑战，来连接海量数据以发现这一功能架构，从而整合各种数据集。 显示英文摘要

摘要： The brain is a highly complex organ consisting of a myriad of subsystems that flexibly interact and adapt over time and context to enable perception, cognition, and behavior. Understanding the multi-scale nature of the brain, i.e., how circuit- and moleclular-level interactions build up the fundamental components of brain function, holds incredible potential for developing interventions for neurodegenerative and psychiatric diseases, as well as open new understanding into our very nature. Historically technological limitations have forced systems neuroscience to be local in anatomy (localized, small neural populations in single brain areas), in behavior (studying single tasks), in time (focusing on specific stages of learning or development), and in modality (focusing on imaging single biological quantities). New developments in neural recording technology and behavioral monitoring now provide the data needed to break free of local neuroscience to global neuroscience: i.e., understanding how the brain's many subsystem interact, adapt, and change across the multitude of behaviors animals and humans must perform to thrive. Specifically, while we have much knowledge of the anatomical architecture of the brain (i.e., the hardware), we finally are approaching the data needed to find the functional architecture and discover the fundamental properties of the software that runs on the hardware. We must take this opportunity to bridge between the vast amounts of data to discover this functional architecture which will face numerous challenges from low-level data alignment up to high level questions of interpretable mathematical models of behavior that can synthesize the myriad of datasets together.