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Neuroimage. 2020 Feb 7:116594. doi: 10.1016/j.neuroimage.2020.116594. [Epub ahead of print]

Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion.

Author information

1
MR Research Program, Department of Medicine, John A. Burns School of Medicine, University of Hawai'i, HI, USA. Electronic address: danibeen@hotmail.com.
2
Department of Radiology, University of São Paulo, São Paulo, S.P, Brazil.
3
D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil.
4
MR Research Program, Department of Medicine, John A. Burns School of Medicine, University of Hawai'i, HI, USA.
5
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.

Abstract

The quality of functional MRI (fMRI) data is affected by head motion. It has been shown that fMRI data quality can be improved by prospectively updating the gradients and radio-frequency pulses in response to head motion during image acquisition by using an MR-compatible optical tracking system (prospective motion correction, or PMC). Recent studies showed that PMC improves the temporal Signal to Noise Ratio (tSNR) of resting state fMRI data (rs-fMRI) acquired from subjects not moving intentionally. Besides that, the time courses of Independent Components (ICs), resulting from Independent Component Analysis (ICA), were found to present significant temporal correlation with the motion parameters recorded by the camera. However, the benefits of applying PMC for improving the quality of rs-fMRI acquired under large head movements and its effects on resting state networks (RSN) and connectivity matrices are still unknown. In this study, subjects were instructed to cross their legs at will while rs-fMRI data with and without PMC were acquired, which generated head motion velocities ranging from 4 to 30 mm/s. We also acquired fMRI data without intentional motion. Independent component analysis of rs-fMRI was performed to evaluate IC maps and time courses of RSNs. We also calculated the temporal correlation among different brain regions and generated connectivity matrices for the different motion and PMC conditions. In our results we verified that the crossing leg movements reduced the tSNR of sessions without and with PMC by 45 and 20%, respectively, when compared to sessions without intentional movements. We have verified an interaction between head motion speed and PMC status, showing stronger attenuation of tSNR for acquisitions without PMC than for those with PMC. Additionally, the spatial definition of major RSNs, such as default mode, visual, left and right central executive networks, was improved when PMC was enabled. Furthermore, motion altered IC-time courses by decreasing power at low frequencies and increasing power at higher frequencies (typically associated with artefacts). PMC partially reversed these alterations of the power spectra. Finally, we showed that PMC provides temporal correlation matrices for data acquired under motion conditions more comparable to those obtained by fMRI sessions where subjects were instructed not to move.

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