PT  - JOURNAL ARTICLE
AU  - Gujar, S.K.
AU  - Manzoor, K.
AU  - Wongsripuemtet, J.
AU  - Wang, G.
AU  - Ryan, D.
AU  - Agarwal, S.
AU  - Lindquist, M.
AU  - Caffo, B.
AU  - Pillai, J.J.
AU  - Sair, H.I.
TI  - Identification of the Language Network from Resting-State fMRI in Patients with Brain Tumors: How Accurate Are Experts?
AID  - 10.3174/ajnr.A7806
DP  - 2023 Mar 01
TA  - American Journal of Neuroradiology
PG  - 274--282
VI  - 44
IP  - 3
4099  - http://www.ajnr.org/content/44/3/274.short
4100  - http://www.ajnr.org/content/44/3/274.full
SO  - Am. J. Neuroradiol.2023 Mar 01; 44
AB  - BACKGROUND AND PURPOSE: Resting-state fMRI helps identify neural networks in presurgical patients who may be limited in their ability to undergo task-fMRI. The purpose of this study was to determine the accuracy of identifying the language network from resting-state-fMRI independent component analysis (ICA) maps.MATERIALS AND METHODS: Through retrospective analysis, patients who underwent both resting-state-fMRI and task-fMRI were compared by identifying the language network from the resting-state-fMRI data by 3 reviewers. Blinded to task-fMRI maps, these investigators independently reviewed resting-state-fMRI ICA maps to potentially identify the language network. Reviewers ranked up to 3 top choices for the candidate resting-state-fMRI language map. We evaluated associations between the probability of correct identification of the language network and some potential factors.RESULTS: Patients included 29 men and 14 women with a mean age of 41 years. Reviewer 1 (with 17 years’ experience) demonstrated the highest overall accuracy with 72%; reviewers 2 and 3 (with 2 and 7 years’ experience, respectively) had a similar percentage of correct responses (50% and 55%). The highest accuracy used ICA50 and the top 3 choices (81%, 65%, and 60% for reviewers 1, 2, and 3, respectively). The lowest accuracy used ICA50, limiting each reviewer to the top choice (58%, 35%, and 42%).CONCLUSIONS: We demonstrate variability in the accuracy of blinded identification of resting-state-fMRI language networks across reviewers with different years of experience.BOLDblood oxygen level–dependentICAindependent component analysisrsresting-state