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

• Published in: Magnetic resonance in medicine Vol. 84; no. 1; pp. 375 - 383
• Main 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
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.07.2020
• Subjects: Brain cancer; Brain Mapping; Brain Neoplasms - diagnostic imaging; Brain tumors; Cerebral Cortex; Coefficients; Functional magnetic resonance imaging; Homogeneity; Humans; Language; Localization; Magnetic Resonance Imaging; Mapping; Metastatic seeding; Neural networks; preoperative mapping; presurgical mapping; Regional analysis; regional homogeneity; resting state; Tumors; resting state; regional homogeneity; functional magnetic resonance imaging; preoperative mapping; presurgical mapping
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.28107

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.