Identification of the Language Network from Resting-State fMRI in Patients with Brain Tumors: How Accurate Are Experts?
Journal
AJNR. American journal of neuroradiology
ISSN: 1936-959X
Titre abrégé: AJNR Am J Neuroradiol
Pays: United States
ID NLM: 8003708
Informations de publication
Date de publication:
03 2023
03 2023
Historique:
received:
28
10
2021
accepted:
04
01
2023
pmc-release:
01
03
2024
pubmed:
24
2
2023
medline:
14
3
2023
entrez:
23
2
2023
Statut:
ppublish
Résumé
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. 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. 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%). We demonstrate variability in the accuracy of blinded identification of resting-state-fMRI language networks across reviewers with different years of experience.
Sections du résumé
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.
Identifiants
pubmed: 36822828
pii: ajnr.A7806
doi: 10.3174/ajnr.A7806
pmc: PMC10187806
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
274-282Informations de copyright
© 2023 by American Journal of Neuroradiology.
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