Abstract：
The superior temporal sulcus (STS) has a functional topography that has been difficult to characterize through traditional approaches. Automated atlas parcellations may be one solution while also being beneficial for both dimensional reduction and standardizing regions of interest, but they yield very different boundary definitions along the STS. Here we evaluate how well machine learning classifiers can correctly identify six social cognitive tasks from STS activation patterns dimensionally reduced using four popular atlases (Glasser et al., 2016; Gordon et al., 2016; Power et al., 2011 as projected onto the surface by Arslan et al., 2018; Schaefer et al., 2018). Functional data was summarized within each STS parcel in one of four ways, then subjected to leave-one-subject-out cross-validation SVM classification. We found that the classifiers could readily label conditions when data was parcellated using any of the four atlases, evidence that dimensional reduction to parcels did not compromise functional fingerprints. Mean activation for the social conditions was the most effective metric for classification in the right STS, whereas all the metrics classified equally well in the left STS. Interestingly, even atlases constructed from random parcellation schemes (null atlases) classified the conditions with high accuracy. We therefore conclude that the complex activation maps on the STS are readily differentiated at a coarse granular level, despite a strict topography having not yet been identified. Further work is required to identify what features have greatest potential to improve the utility of atlases in replacing functional localizers.
摘要：
上颞沟（STS）具有功能地形，难以通过传统方法进行表征。自动图集分割可能是一种解决方案，同时也有利于降维和标准化感兴趣的区域。但是它们沿着STS产生非常不同的边界定义。在这里，我们评估了机器学习分类器如何从STS激活模式中正确识别六个社会认知任务，这些STS激活模式使用四个流行的地图集（Glasser等人。,2016；戈登等人。,2016；Power等人。,2011年由Arslan等人投射到表面。,2018；Schaefer等人。,2018)。以四种方式之一在每个STS地块中总结了功能数据，然后进行留一主题交叉验证SVM分类。我们发现，当使用四个地图集中的任何一个对数据进行分组时，分类器可以很容易地标记条件，证据表明，减少包裹的尺寸不会损害功能指纹。社会条件的平均激活是正确STS分类的最有效指标，而所有度量在左侧STS中分类得同样好。有趣的是,甚至从随机分组方案构建的地图集(空地图集)也能高精度地对条件进行分类。因此，我们得出结论，STS上的复杂激活图很容易在粗粒度水平上区分，尽管尚未确定严格的地形。需要进一步的工作来确定哪些功能具有最大的潜力来提高地图集在替换功能定位器中的实用性。
