Head motion during MRI acquisition reduces gray matter volume and thickness estimates

By Martin Reuter, M. Dylan Tisdall, A. Qureshi, Randy L. Buckner, A. V. D. van der Kouwe, Bruce Fischl. NeuroImage, 2015

470 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/284a1fa9f431863c49695b0b42535de1107cf765

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Citations in the corpus

Mind The Hype: A Critical Evaluation and Prescriptive Agenda for Research on Mindfulness and Meditation (2017, unknown): “Physical artifacts involving head movements and cardiorespiratory effects are especially notable (Holmes, Solomon, Cappo, & Greenberg, 1983; Lutz et al., 2009; Reuter et al., 2015; Van Dijk, Sabuncu, & Buckner, 2012; R. K. Wallace, 1970; R. K. Wallace, Benson, & Wilson, 1971; cf. Lazar et al., 2000”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “, 2014) and cortical thickness measurements (Reuter et al., 2015; Savalia et al., 2017).”
Trajectories of cortical thickness maturation in normal brain development – The importance of quality control procedures (2015, unknown): “E D PVisual quality controlGiven the sensitivity of automated CTh measurements to small movement artifacts (Reuter et al., 2014), a visual quality control (QC) of each subject's extracted white and gray matter surfaces was carried out by two independent investigators to ensure that there were no…”
The impact of traditional neuroimaging methods on the spatial localization of cortical areas (2018, methodology): “S6 shows the additional surface smoothing level of 15-mm FWHM that has been reported in the literature (29) and that approaches the areal uncertainty values seen in unsmoothed volume-based alignment along with the same three levels of smoothing (4, 8, and 15 mm) with a FreeSurfer alignment.”
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline (2019, unknown): “While these group differences may not purely reflect neurodegeneration, but can include indirect factors such as head motion (Reuter et al., 2015; Tisdall et al., 2016) or hydration levels (Biller et al.”
Brain age prediction using deep learning uncovers associated sequence variants (2019, background): “Head motion is another potentially problematic confound effect, because it causes reduction of estimated gray matter volume and thickness in MRI images similar to what we expect to see due to ageing 51 .”