Presurgical brain mapping of the language network in patients with brain tumors using resting-state fMRI: Comparison with task fMRI

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...

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Published inHuman brain mapping Vol. 37; no. 3; pp. 913 - 923
Main Authors Sair, Haris I., Yahyavi-Firouz-Abadi, 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.
Format Journal Article
LanguageEnglish
Published United States Blackwell Publishing Ltd 01.03.2016
John Wiley & Sons, Inc
John Wiley and Sons Inc
Subjects
Adolescent
Adult
Aged
Analysis of Variance
Brain - physiopathology
Brain - surgery
Brain Mapping - methods
Brain Neoplasms - physiopathology
Brain Neoplasms - surgery
brain tumor
Female
Humans
Language
language network
Linear Models
Magnetic Resonance Imaging - methods
Male
Mental Processes - physiology
Middle Aged
Neuropsychological Tests
Preoperative Care - methods
presurgical brain mapping
Rest
resting-state fMRI
Software
task-fMRI
Young Adult
brain tumor
language network
presurgical brain mapping
resting-state fMRI
task-fMRI
Online AccessGet full text

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Abstract 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.
AbstractList 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.PURPOSETo 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.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.MATERIALS AND METHODSIndependent 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.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.RESULTSArtificial 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.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.CONCLUSIONOverall 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.
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. 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. 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. 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.
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.
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.
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.
Author Lindquist, Martin A.
Yahyavi-Firouz-Abadi, Noushin
Caffo, Brian
Gujar, Sachin K.
Intrapiromkul, Jarunee
Sair, Haris I.
Pillai, Jay J.
Airan, Raag D.
Choe, Ann S.
Agarwal, Shruti
Calhoun, Vince D.
AuthorAffiliation 2 The Mind Research Network, Departments of Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico
1 Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Sciences Johns Hopkins University School of Medicine Baltimore Maryland
3 F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute Baltimore Maryland
4 Department of Biostatistics Johns Hopkins University Baltimore Maryland
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Keywords brain tumor
language network
presurgical brain mapping
resting-state fMRI
task-fMRI
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Snippet Purpose To compare language networks derived from resting‐state fMRI (rs‐fMRI) with task‐fMRI in patients with brain tumors and investigate variables that...
To compare language networks derived from resting-state fMRI (rs-fMRI) with task-fMRI in patients with brain tumors and investigate variables that affect...
Purpose To compare language networks derived from resting-state fMRI (rs-fMRI) with task-fMRI in patients with brain tumors and investigate variables that...
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SubjectTerms Adolescent
Adult
Aged
Analysis of Variance
Brain - physiopathology
Brain - surgery
Brain Mapping - methods
Brain Neoplasms - physiopathology
Brain Neoplasms - surgery
brain tumor
Female
Humans
Language
language network
Linear Models
Magnetic Resonance Imaging - methods
Male
Mental Processes - physiology
Middle Aged
Neuropsychological Tests
Preoperative Care - methods
presurgical brain mapping
Rest
resting-state fMRI
Software
task-fMRI
Young Adult
Title Presurgical brain mapping of the language network in patients with brain tumors using resting-state fMRI: Comparison with task fMRI
URI https://api.istex.fr/ark:/67375/WNG-L4VLRBG4-R/fulltext.pdf
https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fhbm.23075
https://www.ncbi.nlm.nih.gov/pubmed/26663615
https://www.proquest.com/docview/1763732404
https://www.proquest.com/docview/1764699641
https://www.proquest.com/docview/1776664816
https://pubmed.ncbi.nlm.nih.gov/PMC6867315
Volume 37
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