Improved Task-based Functional MRI Language Mapping in Patients with Brain Tumors through Marchenko-Pastur Principal Component Analysis Denoising

Background: Functional MRI improves preoperative planning in patients with brain tumors, but task-correlated signal intensity changes are only 2%-3% above baseline. This makes accurate functional mapping challenging. Marchenko-Pastur principal component analysis (MP-PCA) provides a novel strategy to separate functional MRI signal from noise without requiring user input or prior data representation. Purpose: To determine whether MP-PCA denoising improves activation magnitude for task-based functional MRI language mapping in patients with brain tumors. Materials and Methods: In this Health Insurance Portability and Accountability Act-compliant study, MP-PCA performance was first evaluated by using simulated functional MRI data with a known ground truth. Right-handed, left-language-dominant patients with brain tumors who successfully performed verb generation, sentence completion, and finger tapping functional MRI tasks were retrospectively identified between January 2017 and August 2018. On the group level, for each task, histograms of z scores for original and MP-PCA denoised data were extracted from relevant regions and contralateral homologs were seeded by a neuroradiologist blinded to functional MRI findings. Z scores were compared with paired two-sided t tests, and distributions were compared with effect size measurements and the Kolmogorov-Smirnov test. The number of voxels with a z score greater than 3 was used to measure task sensitivity relative to task duration. Results: Twenty-three patients (mean age +/- standard deviation, 43 years +/- 18; 13 women) were evaluated. MP-PCA denoising led to a higher median z score of task-based functional MRI voxel activation in left hemisphere cortical regions for verb generation (from 3.8 +/- 1.0 to 4.5 +/- 1.4; P =.001), sentence completion (from 3.7 +/- 1.0 to 4.3 +/- 1.4; P,.001), and finger tapping (from 6.9 +/- 2.4 to 7.9 +/- 2.9; P =.001). Median z scores did not improve in contralateral homolog regions for verb generation (from 22.7 +/- 0.54 to 22.5 +/- 0.40; P =.90), sentence completion (from 22.3 6 0.21 to 22.4 +/- 0.37; P =.39), or finger tapping (from 22.3 +/- 1.20 to 22.7 +/- 1.40; P =.07). Individual functional MRI task durations could be truncated by at least 40% after MP-PCA without degradation of clinically relevant correlations between functional cortex and functional MRI tasks. Conclusion: Denoising with Marchenko-Pastur principal component analysis led to higher task correlations in relevant cortical regions during functional MRI language mapping in patients with brain tumors. (C) RSNA, 2020

Automated summary

Using MP-PCA denoising improves task correlation in relevant cortical regions during functional MRI language mapping in patients with brain tumors. The study found that denoising led to higher median z scores in cortical regions for various tasks, while contralateral homolog regions did not show improvement in z scores.