Open AccessPresurgical brain mapping of the language network in patients with brain tumors using resting‐state f MRI : Comparison with task f MRI
Author(s) -
Sair Haris I.,
YahyaviFirouzAbadi Noushin,
Calhoun Vince D.,
Airan Raag D.,
Agarwal Shruti,
Intrapiromkul Jarunee,
Choe Ann S.,
Gujar Sachin K.,
Caffo Brian,
Lindquist Martin A.,
Pillai Jay J.
Publication year - 2016
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.23075
Subject(s) - concordance , resting state fmri , analysis of variance , general linear model , functional magnetic resonance imaging , linear regression , brain mapping , brain activity and meditation , psychology , medicine , nuclear medicine , audiology , neuroscience , statistics , electroencephalography , mathematics
Purpose To compare language networks derived from resting‐state fMRI (rs‐fMRI) with task‐fMRI in patients with brain tumors and investigate variables that affect rs‐fMRI vs task‐fMRI concordance. Materials and Methods Independent component analysis (ICA) of rs‐fMRI was performed with 20, 30, 40, and 50 target components (ICA20 to ICA50) and language networks identified for patients presenting for presurgical fMRI mapping between 1/1/2009 and 7/1/2015. 49 patients were analyzed fulfilling criteria for presence of brain tumors, no prior brain surgery, and adequate task‐fMRI performance. Rs‐vs‐task‐fMRI concordance was measured using Dice coefficients across varying fMRI thresholds before and after noise removal. Multi‐thresholded Dice coefficient volume under the surface (DiceVUS) and maximum Dice coefficient (MaxDice) were calculated. One‐way Analysis of Variance (ANOVA) was performed to determine significance of DiceVUS and MaxDice between the four ICA order groups. Age, Sex, Handedness, Tumor Side, Tumor Size, WHO Grade, number of scrubbed volumes, image intensity root mean square (iRMS), and mean framewise displacement (FD) were used as predictors for VUS in a linear regression. Results Artificial elevation of rs‐fMRI vs task‐fMRI concordance is seen at low thresholds due to noise. Noise‐removed group‐mean DiceVUS and MaxDice improved as ICA order increased, however ANOVA demonstrated no statistically significant difference between the four groups. Linear regression demonstrated an association between iRMS and DiceVUS for ICA30‐50, and iRMS and MaxDice for ICA50. Conclusion Overall there is moderate group level rs‐vs‐task fMRI language network concordance, however substantial subject‐level variability exists; iRMS may be used to determine reliability of rs‐fMRI derived language networks. Hum Brain Mapp 37:913–923, 2016 . © 2015 Wiley Periodicals, Inc.