Presurgical brain mapping of the ventral somatomotor network in patients with brain tumors using resting-state fMRI

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Abstract

BACKGROUND AND PURPOSE: Resting-state fMRI readily identifies the dorsal but less consistently the ventral somatomotor network. Our aim was to assess the relative utility of resting-state fMRI in the identification of the ventral somatomotor network via comparison with task-based fMRI in patients with brain tumor. MATERIALS AND METHODS: We identified 26 surgically naïve patients referred for presurgical fMRI brain mapping who had undergone both satisfactory ventral motor activation tasks and resting-state fMRI. Following standard preprocessing for task-based fMRI and resting-state fMRI, general linear model analysis of the ventral motor tasks and independent component analysis of resting-state fMRI were performed with the number of components set to 20, 30, 40, and 50. Visual overlap of task-based fMRI and resting-state fMRI at different component levels was assessed and categorized as full match, partial match, or no match. Rest-versus-task-fMRI concordance was calculated with Dice coefficients across varying fMRI thresholds before and after noise removal. Multithresholded Dice coefficient volume under the surface was calculated. RESULTS: The ventral somatomotor network was identified in 81% of patients. At the subject level, better matches between resting-state fMRI and task-based fMRI were seen with an increasing order of components (53% of cases for 20 components versus 73% for 50 components). Noise-removed group-mean volume under the surface improved as component numbers increased from 20 to 50, though ANOVA demonstrated no statistically significant difference among the 4 groups. CONCLUSIONS: In most patients, the ventral somatomotor network can be identified with an increase in the probability of a better match at a higher component number. There is variable concordance of the ventral somatomotor network at the single-subject level between resting-state and task-based fMRI.

Original languageEnglish (US)
Pages (from-to)1006-1012
Number of pages7
JournalAmerican Journal of Neuroradiology
Volume38
Issue number5
DOIs
StatePublished - May 1 2017

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Brain Mapping
Brain Neoplasms
Magnetic Resonance Imaging
Noise

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Clinical Neurology

Cite this

@article{ffef836d2e904a07b9d10c91dc987087,
title = "Presurgical brain mapping of the ventral somatomotor network in patients with brain tumors using resting-state fMRI",
abstract = "BACKGROUND AND PURPOSE: Resting-state fMRI readily identifies the dorsal but less consistently the ventral somatomotor network. Our aim was to assess the relative utility of resting-state fMRI in the identification of the ventral somatomotor network via comparison with task-based fMRI in patients with brain tumor. MATERIALS AND METHODS: We identified 26 surgically na{\"i}ve patients referred for presurgical fMRI brain mapping who had undergone both satisfactory ventral motor activation tasks and resting-state fMRI. Following standard preprocessing for task-based fMRI and resting-state fMRI, general linear model analysis of the ventral motor tasks and independent component analysis of resting-state fMRI were performed with the number of components set to 20, 30, 40, and 50. Visual overlap of task-based fMRI and resting-state fMRI at different component levels was assessed and categorized as full match, partial match, or no match. Rest-versus-task-fMRI concordance was calculated with Dice coefficients across varying fMRI thresholds before and after noise removal. Multithresholded Dice coefficient volume under the surface was calculated. RESULTS: The ventral somatomotor network was identified in 81{\%} of patients. At the subject level, better matches between resting-state fMRI and task-based fMRI were seen with an increasing order of components (53{\%} of cases for 20 components versus 73{\%} for 50 components). Noise-removed group-mean volume under the surface improved as component numbers increased from 20 to 50, though ANOVA demonstrated no statistically significant difference among the 4 groups. CONCLUSIONS: In most patients, the ventral somatomotor network can be identified with an increase in the probability of a better match at a higher component number. There is variable concordance of the ventral somatomotor network at the single-subject level between resting-state and task-based fMRI.",
author = "N. Yahyavi-Firouz-Abadi and Jay Pillai and Martin Lindquist and Calhoun, {Vince Daniel} and Shruti Agarwal and Airan, {R. D.} and Caffo, {Brian S} and Gujar, {Sachin K} and Haris Sair",
year = "2017",
month = "5",
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doi = "10.3174/ajnr.A5132",
language = "English (US)",
volume = "38",
pages = "1006--1012",
journal = "American Journal of Neuroradiology",
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TY - JOUR

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

AU - Yahyavi-Firouz-Abadi, N.

AU - Pillai, Jay

AU - Lindquist, Martin

AU - Calhoun, Vince Daniel

AU - Agarwal, Shruti

AU - Airan, R. D.

AU - Caffo, Brian S

AU - Gujar, Sachin K

AU - Sair, Haris

PY - 2017/5/1

Y1 - 2017/5/1

N2 - BACKGROUND AND PURPOSE: Resting-state fMRI readily identifies the dorsal but less consistently the ventral somatomotor network. Our aim was to assess the relative utility of resting-state fMRI in the identification of the ventral somatomotor network via comparison with task-based fMRI in patients with brain tumor. MATERIALS AND METHODS: We identified 26 surgically naïve patients referred for presurgical fMRI brain mapping who had undergone both satisfactory ventral motor activation tasks and resting-state fMRI. Following standard preprocessing for task-based fMRI and resting-state fMRI, general linear model analysis of the ventral motor tasks and independent component analysis of resting-state fMRI were performed with the number of components set to 20, 30, 40, and 50. Visual overlap of task-based fMRI and resting-state fMRI at different component levels was assessed and categorized as full match, partial match, or no match. Rest-versus-task-fMRI concordance was calculated with Dice coefficients across varying fMRI thresholds before and after noise removal. Multithresholded Dice coefficient volume under the surface was calculated. RESULTS: The ventral somatomotor network was identified in 81% of patients. At the subject level, better matches between resting-state fMRI and task-based fMRI were seen with an increasing order of components (53% of cases for 20 components versus 73% for 50 components). Noise-removed group-mean volume under the surface improved as component numbers increased from 20 to 50, though ANOVA demonstrated no statistically significant difference among the 4 groups. CONCLUSIONS: In most patients, the ventral somatomotor network can be identified with an increase in the probability of a better match at a higher component number. There is variable concordance of the ventral somatomotor network at the single-subject level between resting-state and task-based fMRI.

AB - BACKGROUND AND PURPOSE: Resting-state fMRI readily identifies the dorsal but less consistently the ventral somatomotor network. Our aim was to assess the relative utility of resting-state fMRI in the identification of the ventral somatomotor network via comparison with task-based fMRI in patients with brain tumor. MATERIALS AND METHODS: We identified 26 surgically naïve patients referred for presurgical fMRI brain mapping who had undergone both satisfactory ventral motor activation tasks and resting-state fMRI. Following standard preprocessing for task-based fMRI and resting-state fMRI, general linear model analysis of the ventral motor tasks and independent component analysis of resting-state fMRI were performed with the number of components set to 20, 30, 40, and 50. Visual overlap of task-based fMRI and resting-state fMRI at different component levels was assessed and categorized as full match, partial match, or no match. Rest-versus-task-fMRI concordance was calculated with Dice coefficients across varying fMRI thresholds before and after noise removal. Multithresholded Dice coefficient volume under the surface was calculated. RESULTS: The ventral somatomotor network was identified in 81% of patients. At the subject level, better matches between resting-state fMRI and task-based fMRI were seen with an increasing order of components (53% of cases for 20 components versus 73% for 50 components). Noise-removed group-mean volume under the surface improved as component numbers increased from 20 to 50, though ANOVA demonstrated no statistically significant difference among the 4 groups. CONCLUSIONS: In most patients, the ventral somatomotor network can be identified with an increase in the probability of a better match at a higher component number. There is variable concordance of the ventral somatomotor network at the single-subject level between resting-state and task-based fMRI.

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U2 - 10.3174/ajnr.A5132

DO - 10.3174/ajnr.A5132

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JO - American Journal of Neuroradiology

JF - American Journal of Neuroradiology

SN - 0195-6108

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