Rosenberg Monica D, Finn Emily S, Scheinost Dustin, Papademetris Xenophon, Shen Xilin, Constable R Todd, Chun Marvin M
Department of Psychology, Yale University, New Haven, Connecticut, USA.
Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, USA.
Nat Neurosci. 2016 Jan;19(1):165-71. doi: 10.1038/nn.4179. Epub 2015 Nov 23.
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.
尽管注意力在感知和认知中起着普遍作用，但研究人员缺乏一种简单的方法来测量一个人的整体注意力能力。由于行为测量方法多样且难以标准化，我们利用功能磁共振成像技术寻找注意力一个重要方面——持续注意力——的神经标志物。为此，我们识别出了功能性脑网络，其在持续注意力任务中的强度可预测个体在任务表现上的差异。基于这些网络的模型能够推广到未曾见过的个体，甚至仅根据静息态连接就能预测表现。此外，这些相同的模型在一个独立的儿童和青少年样本中，根据静息态连接预测了注意力的一项临床指标——注意力缺陷多动障碍症状。这些结果表明，全脑功能网络强度为持续注意力提供了一种广泛适用的神经标志物。
