Spurious group differences due to head motion in a diffusion MRI study

By Anastasia Yendiki, Kami Koldewyn, Sita Kakunoori, Nancy Kanwisher, Bruce Fischl. NeuroImage, 2013

514 citations recorded by the source corpus.

Subfields: Pediatrics, Perinatology and Child Health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/207b892455f05431aac74e41d89aa0728d0d1f36

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In Pediatrics, Perinatology and Child Health

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Quantitative Assessment of Structural Image Quality (2017, background): “Notably, because data quality is likely to be collinear with other subject-level variables including cognitive performance (Siegel et al., 2017), symptom burden, and group status (Yendiki et al., 2014), this effect has the potential to similarly confound a wide variety of studies of brain structure.”
Using connectome-based predictive modeling to predict individual behavior from brain connectivity (2017, background): “The subject’s head movement has been known to introduce confounding effects into both functional 26 and structural 27 data during data acquisition.”
Head motion during MRI acquisition reduces gray matter volume and thickness estimates (2015, unknown): “Twelve healthy adult volunteers (having given informed consent) were scanned on a 3T TIM Trio MRI system (Siemens Healthcare, Erlangen) using the vendorsupplied 12-channel head matrix coil supplied.”
Recent progress and outstanding issues in motion correction in resting state fMRI (2014, unknown): “Within the last year, scrutiny of small movements in diffusion weighted imaging (DWI) has determined that small movements also create previously unrecognized, spurious differences in this modality (Yendiki et al., 2013).”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “Strong artifacts due to motion, in addition to their relevance for fMRI, have been noted in diffusion weighted imaging (Roalf et al., 2016; Yendiki et al., 2014) and cortical thickness measurements (Reuter et al.”
Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement (2017, result): “This observation goes a long way towards explaining findings such as those in Yendiki et al. (2014). They found that by stratifying a group of normal children by how much they moved in the scanner they saw significant differences in FA between the low- and high-movement groups.”