Presurgical resting‐state functional MRI language mapping with seed selection guided by regional homogeneity. Issue 1 (2nd December 2019)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Magnetic resonance in medicine. Volume 84:Issue 1(2020)
• Page Start: 375
• Page End: 383
• Publication Date: 2019-12-02
• Subjects: functional magnetic resonance imaging -- preoperative mapping -- presurgical mapping -- regional homogeneity -- resting state; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28107
• Languages: English
• ISSNs: 0740-3194

Abstract

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.