Open AccessThe impact of real-time fMRI denoising on online evaluation of brain activity and functional connectivity
Author(s) -
Masaya Misaki,
Jerzy Bodurka
Publication year - 2021
Publication title -
journal of neural engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.594
H-Index - 111
eISSN - 1741-2560
pISSN - 1741-2552
DOI - 10.1088/1741-2552/ac0b33
Subject(s) - communication noise , functional magnetic resonance imaging , artificial intelligence , noise reduction , computer science , noise (video) , smoothing , signal (programming language) , pattern recognition (psychology) , computer vision , voxel , signal processing , digital signal processing , psychology , neuroscience , philosophy , linguistics , programming language , computer hardware , image (mathematics)
Objective . Comprehensive denoising is imperative in functional magnetic resonance imaging (fMRI) analysis to reliably evaluate neural activity from the blood oxygenation level dependent signal. In real-time fMRI, however, only a minimal denoising process has been applied and the impact of insufficient denoising on online brain activity estimation has not been assessed comprehensively. This study evaluated the noise reduction performance of online fMRI processes in a real-time estimation of regional brain activity and functional connectivity. Approach. We performed a series of real-time processing simulations of online fMRI processing, including slice-timing correction, motion correction, spatial smoothing, signal scaling, and noise regression with high-pass filtering, motion parameters, motion derivatives, global signal, white matter/ventricle average signals, and physiological noise models with image-based retrospective correction of physiological motion effects (RETROICOR) and respiration volume per time (RVT). Main results. All the processing was completed in less than 400 ms for whole-brain voxels. Most processing had a benefit for noise reduction except for RVT that did not work due to the limitation of the online peak detection. The global signal regression, white matter/ventricle signal regression, and RETROICOR had a distinctive noise reduction effect, depending on the target signal, and could not substitute for each other. Global signal regression could eliminate the noise-associated bias in the mean dynamic functional connectivity across time. Significance. The results indicate that extensive real-time denoising is possible and highly recommended for real-time fMRI applications.