Year: 2023
Type: preprint
Abstract: MRIQC (Esteban et al. 2017) is a tool to help researchers perform quality control (QC) on their structural and functional MRI data. Not only does MRIQC generate visual reports for reliable, manual assessment but it also automatically extracts a set of image quality metrics (IQMs). However, these IQMs are hard to interpret, and many related questions remain open, such as which IQMs are more important. In this project, which emerged as a BrainHack Geneva 2022 initiative, we show that head motion during the acquisition of whole-brain T1-weighted (T1w) MRI of healthy volunteers can be predicted based on the IQMs using supervised machine learning. To do so, we employ the open MR-ART (movement-related artifacts; Nárai et al. 2022) dataset, which includes T1w images acquired under three different motion conditions. We show that signal-to-noise ratio (SNR) derived metrics are the most important features to predict motion presence.
Institutions: Centre Hospitalier Universitaire Vaudois, University of Lausanne, European Organization for Nuclear Research, University of Geneva, Norwegian University of Science and Technology, King's College London, University of Arizona, Icahn School of Medicine at Mount Sinai, University of Washington, University Hospital Southampton NHS Foundation Trust, University of Southampton
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Discovering the connectome spectral signatures of behavior, Uncovering the interplay of structure, function, and dynamics of brain connectivity using MRI, 2021 BUDGET REQUEST FOR THE DOE COMPUTATIONAL SCIENCE GRADUATE FELLOWSHIP (CSGF) GRANT, NIPreps: integrating neuroimaging preprocessing workflows across modalities, populations, and species, G2900401177, NCCR 'SYNAPSY - The synaptic bases of mental diseases', 2021 BUDGET REQUEST FOR THE DOE COMPUTATIONAL SCIENCE GRADUATE FELLOWSHIP (CSGF) GRANT, G4861237547
OpenAlex ID: W4316669458