Fortenbaugh Francesca C, DeGutis Joseph, Esterman Michael
Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, Massachusetts.
Boston Attention & Learning Laboratory, VA Boston Healthcare System, Boston, Massachusetts.
Ann N Y Acad Sci. 2017 May;1396(1):70-91. doi: 10.1111/nyas.13318. Epub 2017 Mar 5.
Models of attention often distinguish among attention subtypes, with classic models separating orienting, switching, and sustaining functions. Compared with other forms of attention, the neurophysiological basis of sustaining attention has received far less notice, yet it is known that momentary failures of sustained attention can have far-ranging negative effects in healthy individuals, and lasting sustained attention deficits are pervasive in clinical populations. In recent years, however, there has been increased interest in characterizing moment-to-moment fluctuations in sustained attention, in addition to the overall vigilance decrement, and understanding how these neurocognitive systems change over the life span and across various clinical populations. The use of novel neuroimaging paradigms and statistical approaches has allowed for better characterization of the neural networks supporting sustained attention and has highlighted dynamic interactions within and across multiple distributed networks that predict behavioral performance. These advances have also provided potential biomarkers to identify individuals with sustained attention deficits. These findings have led to new theoretical models explaining why sustaining focused attention is a challenge for individuals and form the basis for the next generation of sustained attention research, which seeks to accurately diagnose and develop theoretically driven treatments for sustained attention deficits that affect a variety of clinical populations.
注意力模型通常会区分不同的注意力亚型,经典模型将定向、转换和维持功能区分开来。与其他形式的注意力相比,维持注意力的神经生理基础受到的关注要少得多,但众所周知,在健康个体中,持续注意力的瞬间失败可能会产生广泛的负面影响,而在临床人群中,持续的注意力缺陷普遍存在。然而,近年来,除了整体警觉性下降之外,人们对刻画持续注意力的瞬间波动以及理解这些神经认知系统在整个生命周期和不同临床人群中的变化越来越感兴趣。新型神经成像范式和统计方法的使用使得能够更好地刻画支持持续注意力的神经网络,并突出了多个分布式网络内部和之间预测行为表现的动态交互作用。这些进展还提供了潜在的生物标志物来识别存在持续注意力缺陷的个体。这些发现催生了新的理论模型,解释了为什么持续集中注意力对个体来说是一项挑战,并为下一代持续注意力研究奠定了基础,该研究旨在准确诊断并开发出基于理论的治疗方法,用于治疗影响各种临床人群的持续注意力缺陷。