Abstract #5544

Combining regional homogeneity and Meta-analysis to improve preoperative language mapping with resting-state functional MRI

Ai-Ling Hsu^1,2, Jason M Johnson³, Kyle R Noll⁴, Sujit S Prabhu⁵, Donald F Schomer³, Jyh-Horng Chen², and Ho-Ling Liu¹

¹Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ²Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, ³Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁴Section of Neuropsychology, Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁵Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States

Resting-state (rs) fMRI has been shown its potential for pre-surgical mapping. Seed-correlation analysis is a commonly used approach for network detection. However, lesion-related spatial distortions and functional reorganization make the seed selection difficult for rs-fMRI mapping based on anatomical landmark alone. Here we proposed a novel approach to guide the seed selection for rs-fMRI mapping in patients with brain tumors by incorporating regional homogeneity (RH) confined by results of meta-analysis (MA). Our results showed performance that was equivalent to the seed localization guided by task-fMRI activation, suggesting the potential of RH+MA approach for rs-fMRI mapping in the clinical practice.

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