Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity

连接体 人类连接体项目 功能磁共振成像 神经科学 静息状态功能磁共振成像 功能连接 心理学 认知 大脑定位 神经网络 鉴定(生物学) 神经影像学 连接组学 计算机科学 人工智能 生物 植物
作者
Emily S. Finn,Xilin Shen,Dustin Scheinost,Monica D. Rosenberg,Jessica S. Huang,Marvin M. Chun,Xenophon Papademetris,R. Todd Constable
出处
期刊:Nature Neuroscience [Springer Nature]
卷期号:18 (11): 1664-1671 被引量:2874
标识
DOI:10.1038/nn.4135
摘要

This study shows that every individual has a unique pattern of functional connections between brain regions. This functional connectivity profile acts as a ‘fingerprint’ that can accurately identify the individual from a large group. Furthermore, an individual's connectivity profile can predict his or her level of fluid intelligence. Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.
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