On the Optimal Strategy for Tackling Head Motion in fMRI Data

Júlia Soares, Rodolfo Abreu, Ana Lima, Sónia Batista, Sónia Batista, Lívia Sousa, Lívia Sousa, Miguel Castelo-Branco, Miguel Castelo-Branco, João Duarte, João Duarte

2021

Abstract

Head motion critically hampers the quality of functional magnetic resonance imaging (fMRI) data, with several methods for its correction being already available in the literature. Head shifts are usually corrected by realigning all functional volumes with relation to a reference volume using affine transformations, from which the estimated motion parameters (MPs) can be additionally regressed out from fMRI data. However, a consensus regarding the number of MPs to regress has not been achieved yet. More critically, abrupt head motion induces the so-called motion outliers in the data, which cannot be accounted for by affine transformations. Two common approaches are widely used to tackle this type of motion, namely modelling strategies such as censoring, and volume interpolation. However, a direct comparison between strategies to tackle motion outliers has not been performed so far. Importantly, to our knowledge no study has focused on determining the extent at which the effects of different head motion correction methods differ between groups in clinical studies. This is particularly relevant in task-related functional connectivity fMRI studies, which are rapidly increasing in clinical research. In this study, we started by determining the optimal number of MPs (between 6 and 24) to be regressed out from fMRI data collected from 8 participants (4 patients with Multiple Sclerosis and 4 healthy controls) performing a perceptual decision-making task. Then we tested motion censoring and volume interpolation for correcting motion outliers, using FD and DVARS metrics to detect the outlier volumes. We found that task-specific activated brain regions were detected with higher sensitivity when using 6 MPs relatively to using 24 MPs. As for the correction of motion outliers, our results suggest that volume interpolation is the best method to use, however more data and external validation is needed to achieve a definite conclusion. Importantly, the performance of motion correction algorithms was irrespective of the subject group (patients and healthy participants). Our results pave the way towards finding an optimal motion correction strategy, which is required to improve the accuracy of fMRI analyses in healthy and patient populations and are an encouragement to test comprehensively different approaches.

Paper Citation

in Harvard Style

Soares J., Abreu R., Lima A., Batista S., Sousa L., Castelo-Branco M. and Duarte J. (2021). On the Optimal Strategy for Tackling Head Motion in fMRI Data.In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS, ISBN 978-989-758-490-9, pages 306-313. DOI: 10.5220/0010327803060313

in Bibtex Style

@conference{biosignals21,
author={Júlia Soares and Rodolfo Abreu and Ana Lima and Sónia Batista and Lívia Sousa and Miguel Castelo-Branco and João Duarte},
title={On the Optimal Strategy for Tackling Head Motion in fMRI Data},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS,},
year={2021},
pages={306-313},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010327803060313},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS,
TI - On the Optimal Strategy for Tackling Head Motion in fMRI Data
SN - 978-989-758-490-9
AU - Soares J.
AU - Abreu R.
AU - Lima A.
AU - Batista S.
AU - Sousa L.
AU - Castelo-Branco M.
AU - Duarte J.
PY - 2021
SP - 306
EP - 313
DO - 10.5220/0010327803060313