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Atlas-Based Classification Algorithms for Identification of Informative Brain Regions in fMRI Data

机译：基于地图集的分类算法，用于识别FMRI数据中的信息大脑区域

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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.
机译：与单变量传统技术相比，多体素图案分析（MVPA）已成功应用于神经影像数据，这是较大的灵敏度。探照灯是将功能值分配给大脑的不同区域的最广泛采用的方法。然而，其性能取决于球体的尺寸，这可以在采用大球尺寸时高估激活区域。在目前的研究中，我们检查了两种不同替代品到探照灯的有效性：基于地图集的本地平均方法（ABLA，Schrouff等人。神经素信息学16,117-143,2013a）和多核学习（MKL，Rakotomamonjy et al。机器学习杂志9,2491-2521,2008）在一个场景中的方法，其中目标是找到支持某些心理运营的信息大脑区域。这些方法采用权重来衡量大脑区域的信息性，高度降低探照灯所需的大型计算成本。我们在两种不同的情景中评估了它们的性能，其中实验条件之间的差异大胆激活大，并采用九种不同的地图集，以评估各种脑围种的影响。结果表明，两种方法都能够在条件之间的差异大，在识别跨地区的地区的识别中展示了大的灵敏度和稳定性。此外，这些方法报告的权重的标志提供了单变量方法的方向性。但是，当差异很小时，只有ABLA局部的信息区域。因此，我们的结果表明，基于阿特拉斯的方法是探照灯的有用替代方案，但是在选择要实现的具体方法时，应考虑分类的分类的性质。

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