Probabilistic atlas for the language network based on precision fMRI data from >800 individuals.

Two analytic traditions characterize fMRI language research. One relies on averaging activations across individuals. This approach has limitations: because of inter-individual variability in the locations of language areas, any given voxel/vertex in a common brain space is part of the language network in some individuals but in others, may belong to a distinct network. An alternative approach relies on identifying language areas in each individual using a functional ‘localizer’. Because of its greater sensitivity, functional resolution, and interpretability, functional localization is gaining popularity, but it is not always feasible, and cannot be applied retroactively to past studies. To bridge these disjoint approaches, we created a probabilistic functional atlas using fMRI data for an extensively validated language localizer in 806 individuals. This atlas enables estimating the probability that any given location in a common space belongs to the language network, and thus can help interpret group-level activation peaks and lesion locations, or select voxels/electrodes for analysis. More meaningful comparisons of findings across studies should increase robustness and replicability in language research.

功能性磁共振成像（fMRI）语言研究有两种分析传统。一种是对个体的激活情况进行平均。这种方法存在局限性：由于语言区域位置在个体间存在差异，通用大脑空间中的任何给定体素/顶点在一些个体中是语言网络的一部分，但在其他个体中可能属于不同的网络。另一种方法是使用功能性“定位器”在每个个体中识别语言区域。由于其具有更高的敏感性、功能分辨率和可解释性，功能定位越来越受欢迎，但它并不总是可行的，也不能追溯应用于过去的研究。为了连接这些不相关的方法，我们利用806名个体中经过广泛验证的语言定位器的fMRI数据创建了一个概率性功能图谱。该图谱能够估计通用空间中任何给定位置属于语言网络的概率，从而有助于解释群体水平的激活峰值和病变位置，或选择体素/电极进行分析。对不同研究结果进行更有意义的比较应该会提高语言研究的稳健性和可重复性。