Merging clinical neuropsychology and functional neuroimaging to evaluate the construct validity and neural network engagement of the n-back task.
Merging clinical neuropsychology and functional neuroimaging to evaluate the construct validity and neural network engagement of the n-back task.
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DOI：
10.1017/s135561771400054x
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发表时间：
2014-08
影响因子：
2.6
通讯作者：
James, G. Andrew
中科院分区：
文献类型：
--
作者：
Kearney-Ramos, Tonisha E.;Fausett, Jennifer S.;Gess, Jennifer L.;Reno, Ashley;Peraza, Jennifer;Kilts, Clint D.;James, G. Andrew
关键词：
n-back任务是研究工作记忆的神经基础的一种广泛使用的神经影像学范式，然而，其神经心理测量学特性却很少得到实证研究。本研究结合临床神经心理学和功能性磁共振成像（fMRI）技术，探讨n-back任务（LNB）字母变体的结构效度，并进一步识别工作记忆中的任务诱发网络。使用自举方法研究LNB任务的结构效度，以将LNB任务表现与临床验证的WM神经心理学测量相关联，以建立收敛效度，以及相关但不同的认知结构（即，注意力和短期记忆），以建立判别效度。独立成分分析（伊卡）确定了活跃在LNB任务在34名健康对照参与者的大脑网络，一般线性模型确定这些网络的任务相关性。Bootstrap相关性分析显示，预期与LNB收敛的指标之间存在中度至高度相关性（|ρ| 0.37）和预期区分的措施之间的弱相关性（|ρ| ≤0.29），控制年龄和教育。伊卡确定了35个独立的网络，其中17个表现出参与显着相关的任务条件，控制反应时间的变异性。其中，双侧额顶叶网络，双侧背外侧前额叶皮质，双侧上级顶叶小叶，包括楔前叶，额岛叶网络优先招募2回条件相比，0回控制条件，表明WM参与。这些结果支持使用的LNB作为衡量WM，并确认其用于探测WM处理的网络级神经相关。
The n-back task is a widely used neuroimaging paradigm for studying the neural basis of working memory (WM); however, its neuropsychometric properties have received little empirical investigation. The present study merged clinical neuropsychology and functional magnetic resonance imaging (fMRI) to explore the construct validity of the letter variant of the n-back task (LNB) and to further identify the task-evoked networks involved in WM. Construct validity of the LNB task was investigated using a bootstrapping approach to correlate LNB task performance across clinically validated neuropsychological measures of WM to establish convergent validity, as well as measures of related but distinct cognitive constructs (i.e., attention and short-term memory) to establish discriminant validity. Independent component analysis (ICA) identified brain networks active during the LNB task in 34 healthy control participants, and general linear modeling determined task-relatedness of these networks. Bootstrap correlation analyses revealed moderate to high correlations among measures expected to converge with LNB (|ρ| ≥0.37) and weak correlations among measures expected to discriminate (|ρ| ≤0.29), controlling for age and education. ICA identified 35 independent networks, 17 of which demonstrated engagement significantly related to task condition, controlling for reaction time variability. Of these, the bilateral frontoparietal networks, bilateral dorsolateral prefrontal cortices, bilateral superior parietal lobules including precuneus, and frontoinsular network were preferentially recruited by the 2-back condition compared to 0-back control condition, indicating WM involvement. These results support the use of the LNB as a measure of WM and confirm its use in probing the network-level neural correlates of WM processing.