Detection of time-varying signals in event-related fMRI designs.

Detection of time-varying signals in event-related fMRI designs.
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DOI:
10.1016/j.neuroimage.2008.07.065
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发表时间:
2008-11-15
期刊:
影响因子:
5.7
通讯作者:
Hirsch J
Hirsch J
中科院分区:
医学1区
文献类型:
--
作者:
Grinband J;Wager TD;Lindquist M;Ferrera VP;Hirsch J

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在对注意力、认知控制、决策以及反应时间(RT)是关键变量的其他领域的神经影像学研究中,与决策相关的时间变异性通常被认为与快速事件相关设计中的血流动力学反应(HDR)无关。在此基础上,大多数已发表的研究对持续不到4秒的大脑活动进行建模,其中短暂的脉冲代表神经或认知事件的开始,然后将其与血液动力学脉冲响应函数(HRF)进行卷积。然而,电生理学研究表明,与决策相关的神经元活动不是瞬时的,而事实上,往往会持续到运动反应。因此,神经处理持续时间的微小差异(类似于人类RT)可能会在HDR中产生明显的变化,从而在回归分析的结果中产生明显的变化。在这项研究中,我们比较了传统模型的有效性,假设没有时间上的差异与模型,明确占神经活动的非常短暂的时期的持续时间。使用模拟和功能磁共振成像数据,我们表明,短暂的差异持续时间是可检测的,使之有可能解离的刺激强度的影响,刺激持续时间,并优化模型的类型被检测到的活动提高了统计能力,一致性和结果的可解释性。
In neuroimaging research on attention, cognitive control, decision-making, and other areas where response time (RT) is a critical variable, the temporal variability associated with the decision is often assumed to be inconsequential to the hemodynamic response (HDR) in rapid event-related designs. On this basis, the majority of published studies model brain activity lasting less than four seconds with brief impulses representing the onset of neural or cognitive events, which are then convolved with the hemodynamic impulse response function (HRF). However, electrophysiological studies have shown that decision-related neuronal activity is not instantaneous, but in fact, often lasts until the motor response. It is therefore possible that small differences in neural processing durations, similar to human RTs, will produce noticeable changes in the HDR, and therefore in the results of regression analyses. In this study we compare the effectiveness of traditional models that assume no temporal variance with a model that explicitly accounts for the duration of very brief epochs of neural activity. Using both simulations and fMRI data, we show that brief differences in duration are detectable, making it possible to dissociate of the effects of stimulus intensity from stimulus duration, and that optimizing the model for the type of activity being detected improves the statistical power, consistency, and interpretability of results.
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