De Blasi, B;
Caciagli, L;
Storti, SF;
Galovic, M;
Koepp, M;
Menegaz, G;
Barnes, A;
(2020)
Noise removal in resting-state and task fMRI: functional connectivity and activation maps.
Journal of Neural Engineering
10.1088/1741-2552/aba5cc.
(In press).
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Abstract
OBJECTIVE: BOLD-based fMRI is a widely used non-invasive tool for mapping brain function and connectivity. However, the BOLD signal is highly affected by non-neuronal contributions arising from head motion, physiological noise and scanner artefacts. Therefore, it is necessary to recover the signal of interest from the other noise-related fluctuations to obtain reliable functional connectivity results. Several pre-processing pipelines have been developed, mainly based on nuisance regression and ICA. The aim of this work was to investigate the impact of seven widely used denoising methods on both resting-state and task fMRI. APPROACH: Task-fMRI can provide some ground truth given that the task administered has well established brain activations. The resulting cleaned data were compared using a wide range of measures: motion evaluation and data quality, resting-state networks and task activations, functional connectivity. RESULTS: Improved signal quality and reduced motion artefacts were obtained with all advanced pipelines, compared to the minimally pre-processed data. Larger variability was observed in the case of brain activation and functional connectivity estimates, with ICA-based pipelines generally achieving more reliable and accurate results. SIGNIFICANCE: This work provides an evidence-based reference for investigators to choose the most appropriate method for their study and data.
| Type: | Article |
|---|---|
| Title: | Noise removal in resting-state and task fMRI: functional connectivity and activation maps |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/1741-2552/aba5cc |
| Publisher version: | https://doi.org/10.1088/1741-2552/aba5cc |
| Language: | English |
| Additional information: | As the Version of Record of this article is going to be / has been published on a gold open access basis under a CC BY 3.0 licence, this Accepted Manuscript is available for reuse under a CC BY 3.0 licence immediately. Everyone is permitted to use all or part of the original content in this article, provided that they adhere to all the terms of the licence https://creativecommons.org/licences/by/3.0 |
| Keywords: | denoising, fMRI, functional connectivity, noise, pre-processing pipelines |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Experimental Epilepsy UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10106408 |
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