Motion artifact in studies of functional connectivity: Characteristics and mitigation strategies
Year: 2017
Type: review
Abstract: Motion artifacts are now recognized as a major methodological challenge for studies of functional connectivity. As in-scanner motion is frequently correlated with variables of interest such as age, clinical status, cognitive ability, and symptom severity, in-scanner motion has the potential to introduce systematic bias. In this article, we describe how motion-related artifacts influence measures of functional connectivity and discuss the relative strengths and weaknesses of commonly used denoising strategies. Furthermore, we illustrate how motion can bias inference, using a study of brain development as an example. Finally, we highlight directions of ongoing and future research, and provide recommendations for investigators in the field. Hum Brain Mapp, 40:2033-2051, 2019. © 2017 Wiley Periodicals, Inc.
Source: Human Brain Mapping
DOI: 10.1002/hbm.23665
Institution: University of Pennsylvania
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: Virtual Resection to Treat Epilepsy, G3824545241, Quantitative Behavioral and Imaging Phenotypes of Amotivation in Schizophrenia, Neurocognitive variability in schizophrenia and youth at-risk for psychosis, Longitudinal multi-modal neuroimaging of irritability in youth, Evolution of the Linked Architecture of Network Control and Executive Function in Adolescence, CRCNS: US-France Modeling & Predicting BCI Learning from Dynamic Networks, Inter-modal Coupling Image Analytics, Interpretable Deep Learning for Analyzing Brain Development Heterogeneity with Personalized Functional Networks Across Multi-Site Data, G90128336
OpenAlex ID: W2766922554