RT Journal Article
SR Electronic
T1 The Impact of In-Scanner Head Motion on Structural Connectivity Derived from Diffusion Tensor Imaging
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 185397
DO 10.1101/185397
A1 Graham L. Baum
A1 David R. Roalf
A1 Philip A. Cook
A1 Rastko Ciric
A1 Adon F.G. Rosen
A1 Cedric Xia
A1 Mark A. Elliot
A1 Kosha Ruparel
A1 Ragini Verma
A1 Birkan Tunc
A1 Ruben C. Gur
A1 Raquel E. Gur
A1 Danielle S. Bassett
A1 Theodore D. Satterthwaite
YR 2017
UL http://biorxiv.org/content/early/2017/09/07/185397.abstract
AB Multiple studies have shown that data quality is a critical confound in the construction of brain networks derived from functional MRI. This problem is particularly relevant for studies of human brain development where important variables (such as participant age) are correlated with data quality. Nevertheless, the impact of head motion on estimates of structural connectivity derived from diffusion tractography methods remains poorly characterized. Here, we evaluated the impact of in-scanner head motion on structural connectivity using a sample of 949 participants (ages 8-23 years old) who passed a rigorous quality assessment protocol for diffusion tensor imaging (DTI) acquired as part of the Philadelphia Neurodevelopmental Cohort. Structural brain networks were constructed for each participant using both deterministic and probabilistic tractography. We hypothesized that subtle variation in head motion would systematically bias estimates of structural connectivity and confound developmental inference, as observed in previous studies of functional connectivity. Even following quality assurance and retrospective correction for head motion, eddy currents, and field distortions, in-scanner head motion significantly impacted the strength of structural connectivity in a consistency-and length-dependent manner. Specifically, increased head motion was associated with reduced estimates of structural connectivity for high-consistency network edges, which included both short-and long-range connections. In contrast, motion inflated estimates of structural connectivity for low-consistency network edges that were primarily shorter-range. Finally, we demonstrate that age-related differences in head motion can both inflate and obscure developmental inferences on structural connectivity. Taken together, these data delineate the systematic impact of head motion on structural connectivity, and provide a critical context for identifying motion-related confounds in studies of structural brain network development.