Analysis of Functional Image Analysis Contest (FIAC) data with BrainVoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis
Analysis of Functional Image Analysis Contest (FIAC) data with BrainVoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis
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DOI：
10.1002/hbm.20249
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发表时间：
2006-05-01
影响因子：
4.8
通讯作者：
Formisano, E
中科院分区：
文献类型：
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作者：
Goebel, R;Esposito, F;Formisano, E
使用BrainVoyager QX分析了2005年功能图像分析竞赛（FIAC）数据集。首先，我们对功能和解剖学数据进行了标准分析，包括预处理，空间归一化到Talairach空间，假设驱动的统计学（单因子和双因子，单受试者和组水平随机效应，一般线性模型[GLM]）的块和事件相关范例。强句子和弱扬声器组水平的影响被检测到在颞叶和额叶。在这个标准分析之后，我们进行了单受试者和组水平（基于Talairach）的独立成分分析（伊卡），该分析突出了颞叶和额叶区域中用于句子处理的功能连接簇的存在，此外还揭示了与听觉刺激或大脑默认状态相关的其他网络。最后，我们应用高分辨率皮质对齐方法来改善大脑之间的空间对应性，并在该空间中重新运行随机效应组GLM以及组级伊卡。使用空间和时间上未平滑的数据，这种基于皮层的分析揭示了可比的结果，但与一组空间上更有限的组群和更多的差分组感兴趣区域的时间过程。
The Functional Image Analysis Contest (FIAC) 2005 dataset was analyzed using BrainVoyager QX. First, we performed a standard analysis of the functional and anatomical data that includes preprocessing, spatial normalization into Talairach space, hypothesis-driven statistics (one- and two-factorial, single-subject and group-level random effects, General Linear Model [GLM]) of the block- and event-related paradigms. Strong sentence and weak speaker group-level effects were detected in temporal and frontal regions. Following this standard analysis, we performed single-subject and group-level (Talairach-based) Independent Component Analysis (ICA) that highlights the presence of functionally connected clusters in temporal and frontal regions for sentence processing, besides revealing other networks related to auditory stimulation or to the default state of the brain. Finally, we applied a high-resolution cortical alignment method to improve the spatial correspondence across brains and re-run the random effects group GLM as well as the group-level ICA in this space. Using spatially and temporally unsmoothed data, this cortex-based analysis revealed comparable results but with a set of spatially more confined group clusters and more differential group region of interest time courses.