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

Haris Sair, Noushin Yahyavi-Firouz-Abadi, Vince Daniel Calhoun, Raag D. Airan, Shruti Agarwal, Jarunee Intrapiromkul, Ann Choe, Sachin K Gujar, Brian S Caffo, Martin Lindquist, Jay Pillai

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)913-923
Number of pages11
JournalHuman Brain Mapping
Volume37
Issue number3
DOIs
StatePublished - Mar 1 2016

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Brain Mapping
Brain Neoplasms
Language
Magnetic Resonance Imaging
Noise
Linear Models
Analysis of Variance
Functional Laterality
Brain
Task Performance and Analysis

Keywords

  • Brain tumor
  • Language network
  • Presurgical brain mapping
  • Resting-state fMRI
  • Task-fMRI

ASJC Scopus subject areas

  • Clinical Neurology
  • Anatomy
  • Neurology
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

@article{1f0398d13d1642bfac05af42ff993f7a,
title = "Presurgical brain mapping of the language network in patients with brain tumors using resting-state fMRI: Comparison with task fMRI",
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.",
keywords = "Brain tumor, Language network, Presurgical brain mapping, Resting-state fMRI, Task-fMRI",
author = "Haris Sair and Noushin Yahyavi-Firouz-Abadi and Calhoun, {Vince Daniel} and Airan, {Raag D.} and Shruti Agarwal and Jarunee Intrapiromkul and Ann Choe and Gujar, {Sachin K} and Caffo, {Brian S} and Martin Lindquist and Jay Pillai",
year = "2016",
month = "3",
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doi = "10.1002/hbm.23075",
language = "English (US)",
volume = "37",
pages = "913--923",
journal = "Human Brain Mapping",
issn = "1065-9471",
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TY - JOUR

T1 - Presurgical brain mapping of the language network in patients with brain tumors using resting-state fMRI

T2 - Comparison with task fMRI

AU - Sair, Haris

AU - Yahyavi-Firouz-Abadi, Noushin

AU - Calhoun, Vince Daniel

AU - Airan, Raag D.

AU - Agarwal, Shruti

AU - Intrapiromkul, Jarunee

AU - Choe, Ann

AU - Gujar, Sachin K

AU - Caffo, Brian S

AU - Lindquist, Martin

AU - Pillai, Jay

PY - 2016/3/1

Y1 - 2016/3/1

N2 - 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.

AB - 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.

KW - Brain tumor

KW - Language network

KW - Presurgical brain mapping

KW - Resting-state fMRI

KW - Task-fMRI

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