Evaluation of denoising strategies for task-based functional connectivity: Equalizing residual motion artifacts between rest and cognitively demanding tasks

Daniele Mascali, Marta Moraschi, Mauro DiNuzzo, Silvia Tommasin, Michela Fratini, Tommaso Gili, Richard G. Wise, Silvia Mangia, Emiliano Macaluso, Federico Giove

Research output: Contribution to journalArticlepeer-review

Abstract

In-scanner head motion represents a major confounding factor in functional connectivity studies and it raises particular concerns when motion correlates with the effect of interest. One such instance regards research focused on functional connectivity modulations induced by sustained cognitively demanding tasks. Indeed, cognitive engagement is generally associated with substantially lower in-scanner movement compared with unconstrained, or minimally constrained, conditions. Consequently, the reliability of condition-dependent changes in functional connectivity relies on effective denoising strategies. In this study, we evaluated the ability of common denoising pipelines to minimize and balance residual motion-related artifacts between resting-state and task conditions. Denoising pipelines—including realignment/tissue-based regression, PCA/ICA-based methods (aCompCor and ICA-AROMA, respectively), global signal regression, and censoring of motion-contaminated volumes—were evaluated according to a set of benchmarks designed to assess either residual artifacts or network identifiability. We found a marked heterogeneity in pipeline performance, with many approaches showing a differential efficacy between rest and task conditions. The most effective approaches included aCompCor, optimized to increase the noise prediction power of the extracted confounding signals, and global signal regression, although both strategies performed poorly in mitigating the spurious distance-dependent association between motion and connectivity. Censoring was the only approach that substantially reduced distance-dependent artifacts, yet this came at the great cost of reduced network identifiability. The implications of these findings for best practice in denoising task-based functional connectivity data, and more generally for resting-state data, are discussed.

Original languageEnglish (US)
Pages (from-to)1805-1828
Number of pages24
JournalHuman Brain Mapping
Volume42
Issue number6
DOIs
StatePublished - Apr 15 2021

Bibliographical note

Funding Information:
European Union's Horizon 2020 research and innovation programme, Grant/Award Number: 691110; Italian Ministry of Health (Ricerca Corrente); National Institutes of Health, Grant/Award Number: R01 DK099137; Italian Ministry of Health (Young Researcher), Grant/Award Number: 2013: GR‐2013‐02358177 Funding information

Funding Information:
Partially supported by the Italian Ministry of Health (Ricerca Corrente). This project has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Skłodowska‐Curie grant agreement No 691110. Michela Fratini was partially supported by the Italian Ministry of Health Young Researcher Grant 2013 (GR‐2013‐02358177). Silvia Mangia was partially supported by the National Institutes of Health (NIH R01 DK099137). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding bodies. '

Publisher Copyright:
© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

Keywords

  • artifact
  • denoising
  • functional connectivity
  • motion
  • resting-state fMRI
  • task-concurrent connectivity

PubMed: MeSH publication types

  • Journal Article

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