期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 41, 期 7, 页码 1735-1746出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2022.3149002
关键词
Functional magnetic resonance imaging; Extracellular; Manganese; Electroencephalography; Electrodes; Electric potential; Rats; Artifacts; denoising; extracellular action potentials; fMRI; local field potentials; multi-modal; singular value shrinkage
类别
资金
- NIH [R01 MH111413, R01 NS118330, P41 EB027061, P30 NS076408, S10 RR025031]
- W. M. Keck Foundation
- University of Minnesota's MnDRIVE (Minnesota's Discovery, Research and Innovation Economy) Initiative
This study aims to estimate severe fMRI scanning artifacts in extracellular neural recordings made at ultrahigh magnetic field strengths and proposes several methods to remove the artifacts and uncover the complete neural electrophysiology signal. The results show the successful detection of extracellular action potentials recorded during fMRI gradient interferences, with the SVS methods outperforming sliding template subtraction.
This work aims to estimate severe fMRI scanning artifacts in extracellular neural recordings made at ultrahigh magnetic field strengths in order to remove the artifact interferences and uncover the complete neural electrophysiology signal. We build on previous work that used PCA to denoise EEG recorded during fMRI, adapting it to cover the much larger frequency range (1-6000 Hz) of the extracellular field potentials (EFPs) observed by extracellular neural recordings. We examine the singular value decomposition (SVD)-PCA singular value shrinkage (SVS) and compare two shrinkage rules and a sliding template subtraction approach. Additionally, we present a new technique for estimating the singular value upper bounds in spontaneous neural activity recorded in the isoflurane anesthetized rat that uses the temporal first difference of the neural signal. The approaches are tested on artificial datasets to examine their efficacy in detecting extracellular action potentials (EAPs: 300-6000 Hz) recorded during fMRI gradient interferences. Our results indicate that it is possible to uncover the EAPs recorded during gradient interferences. The methods are then tested on natural (non-artificial) datasets recorded from the cortex of isoflurane anesthetized rats, where both local field potential (LFP: 1-300 Hz) and EAP signals are analyzed. The SVS methods are shown to be advantageous compared to sliding template subtraction, especially in the high frequency range corresponding to EAPs. Our novel approach moves us towards simultaneous fMRI and completely sampled neural recording (1-6000Hz with no temporal gaps), providing the opportunity for further study of spontaneous brain function and neurovascular coupling at ultrahigh field in the isoflurane anesthetized rat.
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