Department of Psychology, Yale University, New Haven, CT, USA; Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
Neuroimage. 2021 Jun;233:117975. doi: 10.1016/j.neuroimage.2021.117975. Epub 2021 Mar 21.
Shared information content is represented across brains in idiosyncratic functional topographies. Hyperalignment addresses these idiosyncrasies by using neural responses to project individuals' brain data into a common model space while maintaining the geometric relationships between distinct patterns of activity or connectivity. The dimensions of this common model capture functional profiles that are shared across individuals such as cortical response profiles collected during a common time-locked stimulus presentation (e.g. movie viewing) or functional connectivity profiles. Hyperalignment can use either response-based or connectivity-based input data to derive transformations that project individuals' neural data from anatomical space into the common model space. Previously, only response or connectivity profiles were used in the derivation of these transformations. In this study, we developed a new hyperalignment algorithm, hybrid hyperalignment, that derives transformations based on both response-based and connectivity-based information. We used three different movie-viewing fMRI datasets to test the performance of our new algorithm. Hybrid hyperalignment derives a single common model space that aligns response-based information as well as or better than response hyperalignment while simultaneously aligning connectivity-based information better than connectivity hyperalignment. These results suggest that a single common information space can encode both shared cortical response and functional connectivity profiles across individuals.
共享信息内容在大脑中以特有的功能拓扑结构表示。超对齐通过使用神经反应将个体的大脑数据投影到一个共同的模型空间中，同时保持活动或连接的不同模式之间的几何关系，从而解决了这些特有的问题。这个共同模型的维度捕捉了个体之间共享的功能特征，例如在共同的时间锁定刺激呈现期间（例如观看电影）收集的皮质响应特征或功能连接特征。超对齐可以使用基于响应或连接的输入数据来推导出将个体的神经数据从解剖空间投影到共同模型空间的变换。以前，这些变换的推导仅使用基于响应或连接的信息。在这项研究中，我们开发了一种新的超对齐算法，混合超对齐，该算法基于基于响应和基于连接的信息来推导出变换。我们使用三个不同的观看电影 fMRI 数据集来测试我们新算法的性能。混合超对齐推导出一个单一的共同模型空间，该空间可以对齐基于响应的信息，与响应超对齐一样好或更好，同时同时比连接超对齐更好地对齐基于连接的信息。这些结果表明，单个共同信息空间可以编码个体之间共享的皮质响应和功能连接特征。
