Multi-voxel pattern analysis (MVPA) has been successfully applied to neuroimaging data due to its larger sensitivity compared to univariate traditional techniques. Searchlight is the most widely employed approach to assign functional value to different regions of the brain. However, its performance depends on the size of the sphere, which can overestimate the region of activation when a large sphere size is employed. In the current study, we examined the validity of two different alternatives to Searchlight: an atlas-based local averaging method (ABLA, Schrouff et al. Neuroinformatics 16, 117–143, 2013a) and a Multi-Kernel Learning (MKL, Rakotomamonjy et al. Journal of Machine Learning 9, 2491–2521, 2008) approach, in a scenario where the goal is to find the informative brain regions that support certain mental operations. These methods employ weights to measure the informativeness of a brain region and highly reduce the large computational cost that Searchlight entails. We evaluated their performance in two different scenarios where the differential BOLD activation between experimental conditions was large vs. small, and employed nine different atlases to assess the influence of diverse brain parcellations. Results show that both methods were able to localize informative regions when differences between conditions were large, demonstrating a large sensitivity and stability in the identification of regions across atlases. Moreover, the sign of the weights reported by these methods provided the directionality of univariate approaches. However, when differences were small, only ABLA localized informative regions. Thus, our results show that atlas-based methods are useful alternatives to Searchlight, but that the nature of the classification to perform should be taken into account when choosing the specific method to implement.

中文翻译：

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基于Atlas的分类算法，用于在fMRI数据中识别信息性大脑区域。

与单变量传统技术相比，多体素模式分析（MVPA）由于具有更高的灵敏度，因此已成功应用于神经影像数据。探照灯是将功能价值分配给大脑不同区域的最广泛采用的方法。但是，其性能取决于球体的大小，当使用大球体时，这可能会高估激活区域。在目前的研究中，我们考察了两种不同的方案的有效性，以探照灯：一个地图集本地平均法（ABLA，Schrouff等神经信息学16， 117-143，2013a）和多内核学习（MKL，Rakotomamonjy等al。机器学习杂志9，2491–2521，2008）方法，其目标是找到支持某些心理操作的信息丰富的大脑区域。这些方法使用权重来测量大脑区域的信息量，并极大地减少了Searchlight带来的大量计算成本。我们在两种不同的情况下评估了它们的性能，在两种不同的情况下，实验条件之间的BOLD激活差异较大，并采用了九种不同的图集来评估各种大脑碎片的影响。结果表明，当条件之间的差异较大时，这两种方法都可以定位信息区域，这表明在确定整个图集区域时，灵敏度和稳定性都很高。此外，这些方法报告的权重符号提供了单变量方法的方向性。但是，当差异很小时，只有ABLA会定位信息区域。因此，我们的结果表明，基于图集的方法是Searchlight的有用替代方法，但是在选择要实施的特定方法时，应考虑要执行的分类的性质。