Reduction of Motion Artifacts in Functional Connectivity Resulting from Infrequent Large Motion

Abstract

Introduction:

Subject head motion is an ongoing challenge in functional magnetic resonance imaging, particularly in the estimation of functional connectivity. Infants (1-month old) scanned during nonsedated sleep often have occasional but large movements of several millimeters separated by periods with relatively little movement. This results in residual signal changes even after image realignment and can distort estimates of functional connectivity. A new motion correction technique, JumpCor, is introduced to reduce the effects of this motion and compared to other existing techniques.

Methods:

Different approaches for reducing residual motion artifacts after image realignment were compared both in actual and simulated data: JumpCor, regressing out the estimated subject motion, and regressing out the average white matter, cerebrospinal fluid (CSF), and global signals and their temporal derivatives.

Results:

Motion-related signal changes resulting from infrequent large motion were significantly reduced both by regressing out the estimated motion parameters and by JumpCor. Furthermore, JumpCor significantly reduced artifacts and improved the quality of functional connectivity estimates when combined with typical preprocessing approaches.

Discussion:

Motion-related signal changes resulting from occasional large motion can be effectively corrected using JumpCor and to a certain extent also by regressing out the estimated motion. This technique should reduce the data loss in studies where participants exhibit this type of motion, such as sleeping infants.

Impact statement

Functional magnetic resonance imaging scans from 1-month-old infants scanned during nonsedated sleep often show occasional but large head motion. Data from subjects that move this much are commonly discarded in functional connectivity studies. However, this study introduces a new motion correction technique, JumpCor, that can effectively reduce this type of motion artifact and compares this to other existing motion correction techniques. The ability to retain valuable subject data despite large motion will be highly important for the study of brain connectivity, particularly in neurodevelopmental studies where this type of motion is common.