ArticleRadiology2021
Improved Task-based Functional MRI Language Mapping in Patients with Brain Tumors through Marchenko-Pastur Principal Component Analysis Denoising.
Article in Radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 39 citations in OpenAlex.
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- Mapping Activity and Functional Organisation of the Motor and Visual Pathways Using ADC-fMRI in the Human Brain.Human brain mapping · 2025Article
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- Reliability of task-based fMRI in the dorsal horn of the human spinal cord.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Reliability of resting-state functional connectivity in the human spinal cord: Assessing the impact of distinct noise sources.NeuroImage · 2023Article
- Enhanced clinical task-based fMRI metrics through locally low-rank denoising of complex-valued data.The neuroradiology journal · 2023Article
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- Tensor denoising of multidimensional MRI data.Magnetic resonance in medicine · 2023Article
- Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias.Frontiers in aging neuroscience · 2023Article
- Efficient PCA denoising of spatially correlated redundant MRI data.Imaging neuroscience (Cambridge, Mass.) · 2023Article
- Mapping of magnetic resonance imaging's transverse relaxation time at low signal-to-noise ratio using Bloch simulations and principal component analysis image denoising.NMR in biomedicine · 2022Article
- Denoise Functional Magnetic Resonance Imaging With Random Matrix Theory Based Principal Component Analysis.IEEE transactions on bio-medical engineering · 2022Article
- Subject-specific features of excitation/inhibition profiles in neurodegenerative diseases.Frontiers in aging neuroscience · 2022Article
- PIRACY: An Optimized Pipeline for Functional Connectivity Analysis in the Rat Brain.Frontiers in neuroscience · 2021Article
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7 authors at 1 institution in 1 country.
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Abstract
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
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.