Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data

View ORCID ProfileArun S. Mahadevan, Ursula A. Tooley, Maxwell A. Bertolero, Allyson P. Mackey, Danielle S. Bassett
doi: https://doi.org/10.1101/2020.05.04.072868
Arun S. Mahadevan
1Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Arun S. Mahadevan
Ursula A. Tooley
6Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA 19104, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Maxwell A. Bertolero
1Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Allyson P. Mackey
7Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Danielle S. Bassett
1Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
2Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
3Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104 USA
4Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
5Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
8Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501 USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: dsb@seas.upenn.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternate methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of six different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that correlation-based measures (Pearson and Spearman correlation) have a relatively high residual distance-dependent relationship with motion compared to coherence and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of correlation-based measures, however, may be offset by their higher test-retest reliability and system identifiability. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted May 05, 2020.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data
Arun S. Mahadevan, Ursula A. Tooley, Maxwell A. Bertolero, Allyson P. Mackey, Danielle S. Bassett
bioRxiv 2020.05.04.072868; doi: https://doi.org/10.1101/2020.05.04.072868
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data
Arun S. Mahadevan, Ursula A. Tooley, Maxwell A. Bertolero, Allyson P. Mackey, Danielle S. Bassett
bioRxiv 2020.05.04.072868; doi: https://doi.org/10.1101/2020.05.04.072868

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (2647)
  • Biochemistry (5271)
  • Bioengineering (3682)
  • Bioinformatics (15799)
  • Biophysics (7261)
  • Cancer Biology (5629)
  • Cell Biology (8102)
  • Clinical Trials (138)
  • Developmental Biology (4769)
  • Ecology (7524)
  • Epidemiology (2059)
  • Evolutionary Biology (10588)
  • Genetics (7734)
  • Genomics (10138)
  • Immunology (5199)
  • Microbiology (13921)
  • Molecular Biology (5392)
  • Neuroscience (30805)
  • Paleontology (215)
  • Pathology (879)
  • Pharmacology and Toxicology (1525)
  • Physiology (2256)
  • Plant Biology (5026)
  • Scientific Communication and Education (1042)
  • Synthetic Biology (1389)
  • Systems Biology (4150)
  • Zoology (812)