Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity
University of Pennsylvania · Cornell University · +2 more institutions
Abstract
Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of participant-level confound regression methods to limit its impact. However, many of the most commonly used techniques have not been systematically evaluated using a broad range of outcome measures. Here, we provide a systematic evaluation of 14 participant-level confound regression methods in 393 youths. Specifically, we compare methods according to four benchmarks, including the residual relationship between motion and connectivity, distance-dependent effects of motion on connectivity, network identifiability, and additional degrees of freedom lost in confound regression. Our…
Citation impact
- FWCI
- 58.50
- Percentile
- 100%
- References
- 90
Authors
14Topics & keywords
- Artifact (error)
- Regression
- Context (archaeology)
- Computer science
- Artificial intelligence
- Regression analysis
- Residual
- Censoring (clinical trials)
- Peace, Justice and strong institutions