Estimation of head motion in structural MRI and its impact on cortical morphometry.
Overview
- École de Technologie Supérieure, Montreal, QC, Canada
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada
- Canada Canadian Institute for Advanced Research (CIFAR) Artificial Intelligence (AI) Chair/Mila, Montreal, QC, Canada
Abstract
Motion-related artifacts are inevitable in Magnetic Resonance Imaging (MRI) and can bias automated neuroanatomical metrics such as cortical thickness. These biases can interfere with statistical analysis which is a major concern as motion has been shown to be more prominent in certain populations such as children or individuals with ADHD. Manual review cannot objectively quantify motion in anatomical scans, and existing quantitative automated approaches often require specialized hardware or custom acquisition protocols. Here, we train a 3D convolutional neural network to estimate a summary motion metric in retrospective routine research scans by leveraging a large training dataset of synthetically motion-corrupted volumes. We validate our method with one held-out site from our training cohort and with 14 fully independent datasets, including one with manual ratings, achieving a Spearman Rank correlation of 0.71 vs. manual labels. We also tested the correlation of our predicted motion score with morphometric measurements known to be impacted by motion, achieving significant correlation on most datasets. Furthermore, our predicted motion correlates with subject age in line with prior studies. Our approach shows good generalization across scanner brands and protocols, enabling objective, scalable motion assessment in structural MRI studies without prospective motion correction. Finally, we provide empirical evidence that our motion estimator significantly improve model fitness when studying cortical thickness and volume. Our final model is made openly and freely available through “Agitation," a tool usable as a CLI, python package and integrated in Nipoppy and Boutiques. By providing reliable motion estimates, our method offers researchers a tool to assess and account for potential biases in cortical morphometric analyses.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
The paper links to its data, not to its authors' code: see the Data section.
The paper's code and data availability statement is in the Data section.
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Data
Datasets cited
- doi:10.18112/
openneuro.ds001486.v1.3. , at OpenNeuro; found in “Data availability statement”1 - doi:10.18112/
openneuro.ds001748.v1.0. , at OpenNeuro; found in “Data availability statement”4 - doi:10.18112/
openneuro.ds002424.v1.2. , at OpenNeuro; found in “Data availability statement”0 - doi:10.18112/
openneuro.ds002886.v1.1. , at OpenNeuro; found in “Data availability statement”0 - doi:10.18112/
openneuro.ds003499.v1.0. , at OpenNeuro; found in “Data availability statement”1 - doi:10.18112/
openneuro.ds003568.v1.0. , at OpenNeuro; found in “Data availability statement”4 - doi:10.18112/
openneuro.ds004173.v1.0. , at OpenNeuro; found in “Data availability statement”2 - doi:10.18112/
openneuro.ds005234.v2.1. , at OpenNeuro; found in “Data availability statement”7 - humanconnectome.org/
study/ , at Human Connectome Project; found in “Data availability statement”hcp-young-adult - humanconnectome.org/
study/ , at Human Connectome Project; found in “Data availability statement”human-connectome-project -for-early-psychosis - openneuro:ds000115, at OpenNeuro; found in “Data availability statement”
- openneuro:ds000144, at OpenNeuro; found in “Data availability statement”
- openneuro:ds000256, at OpenNeuro; found in “Data availability statement”
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found at: https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 2, 28 September 2026
- Funding: added Canadian Institute for Advanced Research; Canada Research Chairs: CRC-2022-00183; Alliance de recherche numérique du Canada; National Institute of Mental Health: U24 MH124629
Version 1, 28 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 3 authors, 5 keywords, 29 references.
Cite
This paper
Bricout, C., Ebrahimi Kahou, S., & Bouix, S. (2026). Estimation of head motion in structural MRI and its impact on cortical morphometry. Frontiers in neuroscience, 20, 1817743. https://
BibTeX
@article{bricout2026esti
author = {Bricout, Charles and Ebrahimi Kahou, Samira and Bouix, Sylvain},
title = {{Estimation of head motion in structural MRI and its impact on cortical morphometry}},
journal = {Frontiers in neuroscience},
year = {2026},
month = may,
volume = {20},
pages = {1817743},
publisher = {Frontiers Media SA},
issn = {1662-4548},
doi = {10.3389/
url = {https://
pmid = {42182060},
pmcid = {PMC13194591}
}
RIS
TY - JOUR
AU - Bricout, Charles
AU - Ebrahimi Kahou, Samira
AU - Bouix, Sylvain
TI - Estimation of head motion in structural MRI and its impact on cortical morphometry
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/
VL - 20
SP - 1817743
SN - 1662-4548
PB - Frontiers Media SA
DO - 10.3389/
UR - https://
LA - en
ER -
CSL-JSON
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