Voxel-wise motion artifacts in population-level whole-brain connectivity analysis of resting-state FMRI

PLoS One. 2014 Sep 4;9(9):e104947. doi: 10.1371/journal.pone.0104947. eCollection 2014.

Abstract

Functional Magnetic Resonance Imaging (fMRI) based brain connectivity analysis maps the functional networks of the brain by estimating the degree of synchronous neuronal activity between brain regions. Recent studies have demonstrated that "resting-state" fMRI-based brain connectivity conclusions may be erroneous when motion artifacts have a differential effect on fMRI BOLD signals for between group comparisons. A potential explanation could be that in-scanner displacement, due to rotational components, is not spatially constant in the whole brain. However, this localized nature of motion artifacts is poorly understood and is rarely considered in brain connectivity studies. In this study, we initially demonstrate the local correspondence between head displacement and the changes in the resting-state fMRI BOLD signal. Than, we investigate how connectivity strength is affected by the population-level variation in the spatial pattern of regional displacement. We introduce Regional Displacement Interaction (RDI), a new covariate parameter set for second-level connectivity analysis and demonstrate its effectiveness in reducing motion related confounds in comparisons of groups with different voxel-vise displacement pattern and preprocessed using various nuisance regression methods. The effect of using RDI as second-level covariate is than demonstrated in autism-related group comparisons. The relationship between the proposed method and some of the prevailing subject-level nuisance regression techniques is evaluated. Our results show that, depending on experimental design, treating in-scanner head motion as a global confound may not be appropriate. The degree of displacement is highly variable among various brain regions, both within and between subjects. These regional differences bias correlation-based measures of brain connectivity. The inclusion of the proposed second-level covariate into the analysis successfully reduces artifactual motion-related group differences and preserves real neuronal differences, as demonstrated by the autism-related comparisons.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Artifacts
  • Brain / pathology*
  • Brain / physiopathology*
  • Brain Mapping / statistics & numerical data
  • Case-Control Studies
  • Child
  • Child Development Disorders, Pervasive / pathology
  • Child Development Disorders, Pervasive / physiopathology
  • Female
  • Functional Neuroimaging / statistics & numerical data*
  • Humans
  • Magnetic Resonance Imaging / statistics & numerical data*
  • Male
  • Motion
  • Regression Analysis
  • Young Adult

Grants and funding

The study was partially supported by the European Union and the European Social Fund through project “Supercomputer, the national virtual lab” (grant. no.: TÁMOP-4.2.2.C-11/1/KONV-2012-0010). The work was supported by the European Union and the State of Hungary, co-financed by the European Social Fund through project “Basic and applied research to assist the development of speech for the deaf” (TÁMOP 4.2.2.C-11/1/KONV). The project was supported by the State of Hungary through the National Brain Research Program (“Charting the normal and pathological macro-scale brain connectome by in vivo neuroimaging”, KTIA_13_NAP-A-II/3). T.S. was supported by the European Union and the State of Hungary, co-financed by the European Social Fund in the framework of TÁMOP-4.2.4.A/2-11/1-2012-0001 ‘National Excellence Program’. A.J. was supported by the European Commission, 7th European Community Framework Programme, Marie Curie IEF Research grant FABRIC – “exploring the Formation and Adaptation of the Brain Connectome”, 2012-PIEF-GA-33003. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.