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

Abstract

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.

Fig. 1

Language atlas topography. Probabilistic functional atlas for the language > control contrast based on overlaid individual binarized activation maps (where in each map, the top 10% of voxels are selected, as described in the text). (a) SPM-analyzed volume data in the MNI template space (based on 806 individual maps). (b) FreeSurfer-analyzed surface data in the FSaverage template space (based on 804 individual maps). In both figures, the color scale reflects the proportion of participants for whom that voxel/vertex belongs to the top 10% of language > control voxels/vertices (threholded at p = 0.2 for visualization purposes).

Fig. 2

Data processing flowchart. Overview of the SPM and FreeSurfer preprocessing and analysis pipelines. Raw dicom images were converted to NIfTI format, motion-corrected, mapped to a common space and smoothed during preprocessing. Each session was then modeled, t-maps were extracted and thresholded, and all sessions were aggregated to create the probabilistic atlas.