Data Quality Influences Observed Links Between Functional Connectivity and Behavior

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.

Age-related functional brain changes in young children

“…Xia et al, 2013), and clinical parameters Cai et al, 2015;Holiga et al, 2015;W.-C. Lin et al, 2015;Qiu et al, 2011;W. Xia et al, 2013) Pernet, 2013;Siegel et al, 2016). Our present study takes those issues into account by using robust correlations with bootstrapping, outlier detection and control of confound artifacts, ensuring that our results can be interpreted with confidence.…”

Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature

“…Thus, GSR is likely to be a useful tool in developmental rs-fcMRI studies, where good control of such artifact is exceptionally important, but this debate may need to be revisted as methodologies in acquisition continue to advance. Moving forward, there is strong consensus about the importance of preventing head motion during acquisition and rigorously quantifying motion effects in study data (Goto et al, 2016; Power et al, 2015; Siegel et al, 2016). …”

“…Strong artifacts due to motion, in addition to their relevance for fMRI, have been noted in diffusion weighted imaging (Roalf et al, 2016; Yendiki et al, 2014) and cortical thickness measurements (Reuter et al, 2015; Savalia et al, 2017). Motion artifacts appear to be tightly related to clinical factors (Fair et al, 2012b) and a whole host of behavioral phenotypes and metrics (Siegel et al, 2016). Some of the techniques currently being used to remove these artifacts are non-optimal (Burgess et al, 2016; Goto et al, 2016).…”

From connectome to cognition: The search for mechanism in human functional brain networks

“…Relationships between FC and behavior can help verify that network changes across task conditions arise from functionally relevant rather than artifactual sources. However, this approach is not infallible when applied in isolation, as supported by recent evidence that head motion negatively correlates with a number of individual difference measures (Siegel et al, 2016). Rather, truly explanatory network mechanisms will likely be revealed through a convergence across multiple methods, combining FC-behavioral relationships with careful task manipulations, as well as multiple FC estimation algorithms and imaging modalities.…”