Item analysis in functional magnetic resonance imaging
Item analysis in functional magnetic resonance imaging
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DOI：
10.1016/j.neuroimage.2007.01.039
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发表时间：
2007-04-15
期刊：
影响因子：
5.7
通讯作者：
Thompson-Schill, Sharon L.
中科院分区：
文献类型：
--
作者：
Bedny, Marina;Aguirre, Geoffrey K.;Thompson-Schill, Sharon L.
认知的行为和神经成像研究经常测试关于不同刺激类别（例如动词，面孔，动物，场景等）的心理处理的假设。这些研究的结论取决于他们的发现从实验中使用的特定刺激到整个类别的普遍性。这种类型的概括性是明确的行为研究测试，使用“项目分析”。然而，到目前为止，在神经影像学研究中，对刺激类别的概括性一直是假设的，而没有采用项目分析进行统计验证。本文将项目分析应用于名词和动词的功能磁共振成像研究，论证了其理论意义和可行性。在主题方面的分析，左前额叶和左后颞区的利益表明推定的语法类的影响。一个项目明智的分析显示，然而，只有左后颞效应是概括的刺激类别的名词和动词。总的来说，主题和项目分析的结果表明，左前额叶皮层的语法类效应取决于所使用的特定单词刺激，而不是反映名词和动词之间的类别差异。这个实证的例子说明，项目分析不仅是足够的权力，以检测任务相关的变化，在BOLD信号，但也可以使理论上重要的发现之间的区别，概括到项目人群，和那些不。(C)2007年爱思唯尔公司All rights reserved.
Behavioral and neuroimaging studies of cognition frequently test hypotheses regarding mental processing of different stimulus categories (e.g. verbs, faces, animals, scenes, etc.). The conclusions of such studies hinge upon the generalizability of their findings from the specific stimuli used in the experiment to the category as a whole. This type of generalizability is explicitly tested in behavioral studies, using "item analysis". However, generalizability to stimulus categories has up until now been assumed in neuroimaging studies, without employing item analysis for statistical validation. Here we apply item analysis to a functional magnetic resonance imaging study of nouns and verbs, demonstrating its theoretical importance and feasibility. In the subject-wise analysis, a left prefrontal and a left posterior-temporal region of interest showed putative grammatical class effects. An item-wise analysis revealed, however, that only the left posterior-temporal effect was generalizable to the stimulus categories of nouns and verbs. Taken together, the findings of the subject- and item-wise analyses suggest that grammatical-class effects in the left prefrontal cortex depend on the particular word stimuli used, rather than reflecting categorical differences between nouns and verbs. This empirical example illustrates that item analysis not only is sufficiently powered to detect task relevant changes in BOLD signal but also can make theoretically important distinctions between findings that generalize to the item populations, and those that do not. (C) 2007 Elsevier Inc. All rights reserved.