Effects of motion related outliers in dynamic functional connectivity using the sliding window method

• Published in: Journal of neuroscience methods Vol. 330; p. 108519
• Main Authors: Savva, Antonis D., Kassinopoulos, Michalis, Smyrnis, Nikolaos, Matsopoulos, George K., Mitsis, Georgios D.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.01.2020
• Subjects: Adult; Brain - diagnostic imaging; Brain - physiology; Connectome - methods; Data Interpretation, Statistical; Default mode network; Default Mode Network - diagnostic imaging; Default Mode Network - physiology; Head Movements; Humans; Magnetic Resonance Imaging - methods; Motion; Outliers; Resting-state fMRI; Time Factors; Weighted Kendall correlation; Weighted Pearson correlation; Outliers; Resting-state fMRI; Default mode network; Motion; Weighted Pearson correlation; Weighted Kendall correlation
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2019.108519

[Display omitted] •Use of ten window functions, fourteen window sizes and two correlation metrics.•In most cases, longer windows yielded a larger number of dynamic connections.•Window sizes from 60 s to 90 s were mildly affected by motion-related effects.•Subject motion considerably affects the identified number of dynamic connections.•If motion-noise is not excessive, the choice of a rectangular window is adequate. It has been suggested that the use of window functions, other than the rectangular, in the sliding window method, may be beneficial for reducing the effects of motion-related outliers in the time-series, when assessing dynamic functional connectivity (dFC) in resting-state fMRI (rs-fMRI). Ten window functions for a wide range of window lengths (20−150 s) combined with Pearson and Kendall correlation metrics, were investigated. One hundred high quality rs-fMRI datasets from healthy controls, were used to systematically assess the effect of varying the window function and length on dFC assessment. To this end, two approaches were implemented: a) simulated outliers were added to the experimental data and b) the experimental data were divided into low and high motion subgroups. The presence of experimental motion-noise tended to inflate the number of dynamic connections for longer (≥100 s) wide-shaped windows, while shorter (20−30 s) narrow-shaped windows exhibited increased sensitivity in the presence of simulated outliers. Moreover, window sizes from 60 s to 90 s were mildly affected by motion-related effects. In most cases, the number of dynamic connections increased, and gradually lower frequencies were captured, with an increasing window size. Subject motion considerably affects the obtained dFC patterns; thus, it is preferable to perform motion artefact removal in the pre-processing stage rather than using alternative window functions to mitigate their effects. Provided that motion-noise is not excessive, the choice of a rectangular window is adequate. Finally, low frequency oscillations in functional connectivity seem to play an important role in the context of dFC assessment.