Effect of brain normalization methods on the construction of functional connectomes from resting‐state functional MRI in patients with gliomas

• Published in: Magnetic resonance in medicine Vol. 86; no. 1; pp. 487 - 498
• Main Authors: Chen, Henry Szu‐Meng, Kumar, Vinodh A., Johnson, Jason M., Chen, Melissa M., Noll, Kyle R., Hou, Ping, Prabhu, Sujit S., Schomer, Donald F., Liu, Ho‐Ling
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.07.2021
• Subjects: Brain; brain parcellation; Clustering; connectome; connectomic measure; Functional magnetic resonance imaging; Glioma; Mathematical analysis; Methods; Modularity; Neural networks; resting‐state; spatial normalization; spatial normalization; connectome; connectomic measure; brain parcellation; functional magnetic resonance imaging; resting-state
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.28690

Purpose Spatial normalization is an essential step in resting‐state functional MRI connectomic analysis with atlas‐based parcellation, but brain lesions can confound it. Cost‐function masking (CFM) is a popular compensation approach, but may not benefit modern normalization methods. This study compared three normalization methods with and without CFM and determined their impact on connectomic measures in patients with glioma. Methods Fifty patients with glioma were included. T1‐weighted images were normalized using three different methods in SPM12, with and without CFM, which were then overlaid on the ICBM152 template and scored by two neuroradiologists. The Dice coefficient of gray‐matter correspondence was also calculated. Normalized resting‐state functional MRI data were parcellated using the AAL90 atlas to construct an individual connectivity matrix and calculate connectomic measures. The R2 among the different normalization methods was calculated for the connectivity matrices and connectomic measures. Results The older method (Original) performed significantly worse than the modern methods (Default and DARTEL; P < .005 in observer ranking). The use of CFM did not significantly improve the normalization results. The Original method had lower correlation with the Default and DARTEL methods (R2 = 0.71‐0.74) than Default with DARTEL (R2 = 0.96) in the connectivity matrix. The clustering coefficient appears to be the most, and modularity the least, sensitive connectomic measures to normalization performance. Conclusion The spatial normalization method can have an impact on resting‐state functional MRI connectome and connectomic measures derived using atlas‐based brain parcellation. In patients with glioma, this study demonstrated that Default and DARTEL performed better than the Original method, and that CFM made no significant difference.