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Meta Analysis
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, and Ho-Ling Liu.
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
- Magn Reson Med. 2020 Jul 1; 84 (1): 375-383.
PurposeResting-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.MethodsTwenty-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.ResultsWith 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.ConclusionThe 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.© 2019 International Society for Magnetic Resonance in Medicine.
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