Presurgical resting-state functional MRI language mapping with seed selection guided by regional homogeneity

Ai Ling Hsu, Henry Szu Meng Chen, Ping Hou, Changwei W. Wu, Jason M. Johnson, Kyle R. Noll, Sujit S. Prabhu, Sherise D. Ferguson, Vinodh A. Kumar, Donald F. Schomer, Jyh Horng Chen, Ho Ling Liu

Research output: Contribution to journalArticle

Abstract

PURPOSE Resting-state functional MRI (rs-FMRI) has shown potential for presurgical mapping of eloquent cortex when a patient's performance on task-based FMRI is compromised. The seed-based analysis is a practical approach for detecting rs-FMRI functional networks; however, seed localization remains challenging for presurgical language mapping. Therefore, we proposed a data-driven approach to guide seed localization for presurgical rs-FMRI language mapping. METHODS Twenty-six patients with brain tumors located in left perisylvian regions had undergone task-based FMRI and rs-FMRI before tumor resection. For the seed-based rs-FMRI language mapping, a seeding approach that integrates regional homogeneity and meta-analysis maps (RH+MA) was proposed to guide the seed localization. Canonical and task-based seeding approaches were used for comparison. The performance of the 3 seeding approaches was evaluated by calculating the Dice coefficients between each rs-FMRI language mapping result and the result from task-based FMRI. RESULTS With the RH+MA approach, selecting among the top 6 seed candidates resulted in the highest Dice coefficient for 81% of patients (21 of 26) and the top 9 seed candidates for 92% of patients (24 of 26). The RH+MA approach yielded rs-FMRI language mapping results that were in greater agreement with the results of task-based FMRI, with significantly higher Dice coefficients (P <.05) than that of canonical and task-based approaches within putative language regions. CONCLUSION The proposed RH+MA approach outperformed the canonical and task-based seed localization for rs-FMRI language mapping. The results suggest that RH+MA is a robust and feasible method for seed-based functional connectivity mapping in clinical practice.
Original languageEnglish
JournalMagnetic Resonance in Medicine
DOIs
Publication statusAccepted/In press - Jan 1 2019

Fingerprint

Seeds
Language
Magnetic Resonance Imaging
Task Performance and Analysis
Brain Neoplasms
Meta-Analysis
Neoplasms

Keywords

  • functional magnetic resonance imaging
  • preoperative mapping
  • presurgical mapping
  • regional homogeneity
  • resting state

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Presurgical resting-state functional MRI language mapping with seed selection guided by regional homogeneity. / Hsu, Ai Ling; Chen, Henry Szu Meng; Hou, Ping; Wu, Changwei W.; Johnson, Jason M.; Noll, Kyle R.; Prabhu, Sujit S.; Ferguson, Sherise D.; Kumar, Vinodh A.; Schomer, Donald F.; Chen, Jyh Horng; Liu, Ho Ling.

In: Magnetic Resonance in Medicine, 01.01.2019.

Research output: Contribution to journalArticle

Hsu, AL, Chen, HSM, Hou, P, Wu, CW, Johnson, JM, Noll, KR, Prabhu, SS, Ferguson, SD, Kumar, VA, Schomer, DF, Chen, JH & Liu, HL 2019, 'Presurgical resting-state functional MRI language mapping with seed selection guided by regional homogeneity', Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.28107
Hsu, Ai Ling ; Chen, Henry Szu Meng ; Hou, Ping ; Wu, Changwei W. ; Johnson, Jason M. ; Noll, Kyle R. ; Prabhu, Sujit S. ; Ferguson, Sherise D. ; Kumar, Vinodh A. ; Schomer, Donald F. ; Chen, Jyh Horng ; Liu, Ho Ling. / Presurgical resting-state functional MRI language mapping with seed selection guided by regional homogeneity. In: Magnetic Resonance in Medicine. 2019.
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abstract = "PURPOSE Resting-state functional MRI (rs-FMRI) has shown potential for presurgical mapping of eloquent cortex when a patient's performance on task-based FMRI is compromised. The seed-based analysis is a practical approach for detecting rs-FMRI functional networks; however, seed localization remains challenging for presurgical language mapping. Therefore, we proposed a data-driven approach to guide seed localization for presurgical rs-FMRI language mapping. METHODS Twenty-six patients with brain tumors located in left perisylvian regions had undergone task-based FMRI and rs-FMRI before tumor resection. For the seed-based rs-FMRI language mapping, a seeding approach that integrates regional homogeneity and meta-analysis maps (RH+MA) was proposed to guide the seed localization. Canonical and task-based seeding approaches were used for comparison. The performance of the 3 seeding approaches was evaluated by calculating the Dice coefficients between each rs-FMRI language mapping result and the result from task-based FMRI. RESULTS With the RH+MA approach, selecting among the top 6 seed candidates resulted in the highest Dice coefficient for 81{\%} of patients (21 of 26) and the top 9 seed candidates for 92{\%} of patients (24 of 26). The RH+MA approach yielded rs-FMRI language mapping results that were in greater agreement with the results of task-based FMRI, with significantly higher Dice coefficients (P <.05) than that of canonical and task-based approaches within putative language regions. CONCLUSION The proposed RH+MA approach outperformed the canonical and task-based seed localization for rs-FMRI language mapping. The results suggest that RH+MA is a robust and feasible method for seed-based functional connectivity mapping in clinical practice.",
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T1 - Presurgical resting-state functional MRI language mapping with seed selection guided by regional homogeneity

AU - Hsu, Ai Ling

AU - Chen, Henry Szu Meng

AU - Hou, Ping

AU - Wu, Changwei W.

AU - Johnson, Jason M.

AU - Noll, Kyle R.

AU - Prabhu, Sujit S.

AU - Ferguson, Sherise D.

AU - Kumar, Vinodh A.

AU - Schomer, Donald F.

AU - Chen, Jyh Horng

AU - Liu, Ho Ling

N1 - © 2019 International Society for Magnetic Resonance in Medicine.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - PURPOSE Resting-state functional MRI (rs-FMRI) has shown potential for presurgical mapping of eloquent cortex when a patient's performance on task-based FMRI is compromised. The seed-based analysis is a practical approach for detecting rs-FMRI functional networks; however, seed localization remains challenging for presurgical language mapping. Therefore, we proposed a data-driven approach to guide seed localization for presurgical rs-FMRI language mapping. METHODS Twenty-six patients with brain tumors located in left perisylvian regions had undergone task-based FMRI and rs-FMRI before tumor resection. For the seed-based rs-FMRI language mapping, a seeding approach that integrates regional homogeneity and meta-analysis maps (RH+MA) was proposed to guide the seed localization. Canonical and task-based seeding approaches were used for comparison. The performance of the 3 seeding approaches was evaluated by calculating the Dice coefficients between each rs-FMRI language mapping result and the result from task-based FMRI. RESULTS With the RH+MA approach, selecting among the top 6 seed candidates resulted in the highest Dice coefficient for 81% of patients (21 of 26) and the top 9 seed candidates for 92% of patients (24 of 26). The RH+MA approach yielded rs-FMRI language mapping results that were in greater agreement with the results of task-based FMRI, with significantly higher Dice coefficients (P <.05) than that of canonical and task-based approaches within putative language regions. CONCLUSION The proposed RH+MA approach outperformed the canonical and task-based seed localization for rs-FMRI language mapping. The results suggest that RH+MA is a robust and feasible method for seed-based functional connectivity mapping in clinical practice.

AB - PURPOSE Resting-state functional MRI (rs-FMRI) has shown potential for presurgical mapping of eloquent cortex when a patient's performance on task-based FMRI is compromised. The seed-based analysis is a practical approach for detecting rs-FMRI functional networks; however, seed localization remains challenging for presurgical language mapping. Therefore, we proposed a data-driven approach to guide seed localization for presurgical rs-FMRI language mapping. METHODS Twenty-six patients with brain tumors located in left perisylvian regions had undergone task-based FMRI and rs-FMRI before tumor resection. For the seed-based rs-FMRI language mapping, a seeding approach that integrates regional homogeneity and meta-analysis maps (RH+MA) was proposed to guide the seed localization. Canonical and task-based seeding approaches were used for comparison. The performance of the 3 seeding approaches was evaluated by calculating the Dice coefficients between each rs-FMRI language mapping result and the result from task-based FMRI. RESULTS With the RH+MA approach, selecting among the top 6 seed candidates resulted in the highest Dice coefficient for 81% of patients (21 of 26) and the top 9 seed candidates for 92% of patients (24 of 26). The RH+MA approach yielded rs-FMRI language mapping results that were in greater agreement with the results of task-based FMRI, with significantly higher Dice coefficients (P <.05) than that of canonical and task-based approaches within putative language regions. CONCLUSION The proposed RH+MA approach outperformed the canonical and task-based seed localization for rs-FMRI language mapping. The results suggest that RH+MA is a robust and feasible method for seed-based functional connectivity mapping in clinical practice.

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KW - preoperative mapping

KW - presurgical mapping

KW - regional homogeneity

KW - resting state

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