No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI

NeuroImage
Rotem KopelFrank Scharnowski

Abstract

As a consequence of recent technological advances in the field of functional magnetic resonance imaging (fMRI), results can now be made available in real-time. This allows for novel applications such as online quality assurance of the acquisition, intra-operative fMRI, brain-computer-interfaces, and neurofeedback. To that aim, signal processing algorithms for real-time fMRI must reliably correct signal contaminations due to physiological noise, head motion, and scanner drift. The aim of this study was to compare performance of the commonly used online detrending algorithms exponential moving average (EMA), incremental general linear model (iGLM) and sliding window iGLM (iGLMwindow). For comparison, we also included offline detrending algorithms (i.e., MATLAB's and SPM8's native detrending functions). Additionally, we optimized the EMA control parameter, by assessing the algorithm's performance on a simulated data set with an exhaustive set of realistic experimental design parameters. First, we optimized the free parameters of the online and offline detrending algorithms. Next, using simulated data, we systematically compared the performance of the algorithms with respect to varying levels of Gaussian and colored noise, linear a...Continue Reading

Citations

Apr 1, 2021·Journal of Neurophysiology·Ethan OblakJames Sulzer
Dec 22, 2019·NeuroImage·Michelle HampsonJunichi Ushiba
Jun 15, 2021·Journal of Neural Engineering·Masaya Misaki, Jerzy Bodurka

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Software Mentioned

Matlab
OpenNFT
SPM8
iGLM
BART
SPM

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