功能磁共振成像
功能连接
神经科学
心理学
认知心理学
认知
感知
大脑活动与冥想
注意力网络
任务(项目管理)
静息状态功能磁共振成像
神经影像学
脑电图
计算机科学
人工智能
经济
管理
作者
Monica D. Rosenberg,Emily S. Finn,Dustin Scheinost,Xenophon Papademetris,Xilin Shen,R. Todd Constable,Marvin M. Chun
摘要
Although attentional abilities vary widely and have profound everyday effects, a standardized measure of these abilities is lacking. This study introduces a new fMRI measure based on patterns of whole-brain connectivity, which predicts adults' attention performance and children's ADHD symptoms from data acquired while individuals are resting in the scanner. Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person's overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention—symptoms of attention deficit hyperactivity disorder—from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.
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