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      Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity

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          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 young adults. 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 results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but unmask distance-dependent artifact. In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom. Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome. Taken together, these results emphasize the heterogeneous efficacy of proposed methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.

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          Author and article information

          Journal
          9215515
          20498
          Neuroimage
          Neuroimage
          NeuroImage
          1053-8119
          1095-9572
          19 April 2017
          14 March 2017
          01 July 2017
          01 July 2018
          : 154
          : 174-187
          Affiliations
          [a ]Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
          [b ]Sackler Institute for Developmental Psychobiology, Weill Medical College of Cornell University, New York, NY, USA
          [c ]Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
          [d ]Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA, USA
          [e ]Department of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf
          [f ]Institute of Neuroscience and Medicine (INM-1), Research Center Jülich
          [g ]Department of Bioengineering, University of Pennsylvania, Philadelphia PA, USA
          [h ]Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia PA, USA
          Author notes
          [* ]Corresponding author: sattertt@ 123456mail.med.upenn.edu (Theodore D. Satterthwaite )
          Article
          PMC5483393 PMC5483393 5483393 nihpa863477
          10.1016/j.neuroimage.2017.03.020
          5483393
          28302591
          Categories
          Article

          motion, fMRI, functional connectivity, artifact, confound, noise

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