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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 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 results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in 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 existing 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
          Neuroimage
          NeuroImage
          Elsevier BV
          1095-9572
          1053-8119
          Jul 01 2017
          : 154
          Affiliations
          [1 ] Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
          [2 ] Department of Psychiatry, Weill Cornell Medical College, NY, NY, USA.
          [3 ] Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
          [4 ] Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
          [5 ] Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Germany.
          [6 ] Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
          [7 ] Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
          [8 ] Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: sattertt@mail.med.upenn.edu.
          Article
          S1053-8119(17)30228-8 NIHMS863477
          10.1016/j.neuroimage.2017.03.020
          5483393
          28302591
          70c418ea-06ac-48ad-9c5f-b054ee992e7b

          Motion, fMRI, Noise, Functional connectivity, Confound, Artifact

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