Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands; Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands.
Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands; Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands.
Neuroimage. 2014 Aug 1;96:54-66. doi: 10.1016/j.neuroimage.2014.02.006. Epub 2014 Feb 12.
When multivariate pattern decoding is applied to fMRI studies entailing more than two experimental conditions, a most common approach is to transform the multiclass classification problem into a series of binary problems. Furthermore, for decoding analyses, classification accuracy is often the only outcome reported although the topology of activation patterns in the high-dimensional features space may provide additional insights into underlying brain representations. Here we propose to decode and visualize voxel patterns of fMRI datasets consisting of multiple conditions with a supervised variant of self-organizing maps (SSOMs). Using simulations and real fMRI data, we evaluated the performance of our SSOM-based approach. Specifically, the analysis of simulated fMRI data with varying signal-to-noise and contrast-to-noise ratio suggested that SSOMs perform better than a k-nearest-neighbor classifier for medium and large numbers of features (i.e. 250 to 1000 or more voxels) and similar to support vector machines (SVMs) for small and medium numbers of features (i.e. 100 to 600voxels). However, for a larger number of features (>800voxels), SSOMs performed worse than SVMs. When applied to a challenging 3-class fMRI classification problem with datasets collected to examine the neural representation of three human voices at individual speaker level, the SSOM-based algorithm was able to decode speaker identity from auditory cortical activation patterns. Classification performances were similar between SSOMs and other decoding algorithms; however, the ability to visualize decoding models and underlying data topology of SSOMs promotes a more comprehensive understanding of classification outcomes. We further illustrated this visualization ability of SSOMs with a re-analysis of a dataset examining the representation of visual categories in the ventral visual cortex (Haxby et al., 2001). This analysis showed that SSOMs could retrieve and visualize topography and neighborhood relations of the brain representation of eight visual categories. We conclude that SSOMs are particularly suited for decoding datasets consisting of more than two classes and are optimally combined with approaches that reduce the number of voxels used for classification (e.g. region-of-interest or searchlight approaches).
当多元模式解码应用于涉及两个以上实验条件的 fMRI 研究时，最常见的方法是将多类分类问题转化为一系列二进制问题。此外，对于解码分析，分类准确性通常是唯一报告的结果，尽管在高维特征空间中的激活模式拓扑可能提供对潜在大脑表示的额外见解。在这里，我们提出了一种使用监督自组织映射 (SSOM) 的变体对包含多个条件的 fMRI 数据集进行解码和可视化的方法。我们使用模拟和真实 fMRI 数据评估了我们基于 SSOM 的方法的性能。具体来说，对具有不同信噪比和对比度噪声比的模拟 fMRI 数据的分析表明，SSOM 在中等数量和大量特征（即 250 到 1000 个或更多体素）的情况下比 k-最近邻分类器表现更好，与小中和中等数量的特征（即 100 到 600 个体素）的支持向量机 (SVM) 相似。然而，对于更多数量的特征（>800 个体素），SSOM 的性能不如 SVM。当应用于一个具有挑战性的 3 类 fMRI 分类问题时，该问题使用收集的数据来检查个体说话者水平的三个人类声音的神经表示，基于 SSOM 的算法能够从听觉皮质激活模式中解码说话者身份。SSOM 与其他解码算法的分类性能相似；然而，SSOM 可视化解码模型和底层数据拓扑的能力促进了对分类结果的更全面理解。我们通过重新分析一个检查腹侧视觉皮层中视觉类别的表示的数据集进一步说明了 SSOM 的这种可视化能力（Haxby 等人，2001）。该分析表明，SSOM 可以检索和可视化八个视觉类别的大脑表示的地形和邻域关系。我们得出结论，SSOM 特别适合于包含两个以上类别的数据集的解码，并且与用于减少用于分类的体素数量的方法（例如，感兴趣区域或搜索灯方法）最佳结合。
