Probabilistic atlas for the language network based on precision fMRI data from >800 individuals
Name
s41597-022-01645-3.pdf
Description
Published version
Size
1.56 MB
Format
Adobe PDF
Checksum (MD5)
2cb121d51b9b1bb412db0276678879c0
Author(s)
Lipkin, Benjamin
Tuckute, Greta
Affourtit, Josef
Small, Hannah
Mineroff, Zachary
Kean, Hope
Jouravlev, Olessia
Rakocevic, Lara
Pritchett, Brianna
Siegelman, Matthew
Date Issued
2022
Journal
Scientific Data
Publisher
Springer Science and Business Media LLC
Citation
Lipkin, Benjamin, Tuckute, Greta, Affourtit, Josef, Small, Hannah, Mineroff, Zachary et al. 2022. "Probabilistic atlas for the language network based on precision fMRI data from >800 individuals." Scientific Data, 9 (1).
Version
Final published version
Abstract
AbstractTwo 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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
10.1038/S41597-022-01645-3
