文章基本信息

标题：Functional connectivity predicts changes in attention observed across minutes, days, and months
本地全文：下载
作者：Monica D. Rosenberg ; Dustin Scheinost ; Abigail S. Greene 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2020
卷号：117
期号：7
页码：3797-3807
DOI：10.1073/pnas.1912226117
出版社：The National Academy of Sciences of the United States of America
摘要：The ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same patterns capture fluctuations in attention within individuals remains unclear. Here, across five independent studies, we demonstrate that the sustained attention connectome-based predictive model (CPM), a validated model of sustained attention function, generalizes to predict attentional state from data collected across minutes, days, weeks, and months. Furthermore, the sustained attention CPM is sensitive to within-subject state changes induced by propofol as well as sevoflurane, such that individuals show functional connectivity signatures of stronger attentional states when awake than when under deep sedation and light anesthesia. Together, these results demonstrate that fluctuations in attentional state reflect variability in the same functional connectivity patterns that predict individual differences in sustained attention.
关键词：sustained attention ; attention fluctuations ; individual differences ; functional connectivity ; predictive modeling