Data quality influences observed links between functional connectivity and behavior

Joshua S. Siegel, Anish Mitra, Timothy O. Laumann, Benjamin A. Seitzman, Marcus Raichle, Maurizio Corbetta, Abraham Z. Snyder

Research output: Contribution to journalArticlepeer-review

126 Scopus citations

Abstract

A growing field of research explores links between behavioral measures and functional connectivity (FC) assessed using resting-state functional magnetic resonance imaging. Recent studies suggest that measurement of these relationships may be corrupted by head motion artifact. Using data from the Human Connectome Project (HCP), we find that a surprising number of behavioral, demographic, and physiological measures (23 of 122), including fluid intelligence, reading ability, weight, and psychiatric diagnostic scales, correlate with head motion. We demonstrate that "trait" (across-subject) and "state" (across-day, within-subject) effects of motion on FC are remarkably similar in HCP data, suggesting that state effects of motion could potentially mimic trait correlates of behavior. Thus, head motion is a likely source of systematic errors (bias) in the measurement of FC:behavior relationships. Next, we show that data cleaning strategies reduce the influence of head motion and substantially alter previously reported FC:behavior relationship. Our results suggest that spurious relationships mediated by head motion may be widespread in studies linking FC to behavior.

Original languageEnglish
Pages (from-to)4492-4502
Number of pages11
JournalCerebral Cortex
Volume27
Issue number9
DOIs
StatePublished - Sep 1 2017

Keywords

  • Functional connectivity
  • Head motion
  • IQ
  • Movement
  • Resting state

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