PT  - JOURNAL ARTICLE
AU  - Arun S. Mahadevan
AU  - Ursula A. Tooley
AU  - Maxwell A. Bertolero
AU  - Allyson P. Mackey
AU  - Danielle S. Bassett
TI  - Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data
AID  - 10.1101/2020.05.04.072868
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.05.04.072868
4099  - http://biorxiv.org/content/early/2020/05/05/2020.05.04.072868.short
4100  - http://biorxiv.org/content/early/2020/05/05/2020.05.04.072868.full
AB  - Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternate methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of six different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that correlation-based measures (Pearson and Spearman correlation) have a relatively high residual distance-dependent relationship with motion compared to coherence and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of correlation-based measures, however, may be offset by their higher test-retest reliability and system identifiability. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.Competing Interest StatementThe authors have declared no competing interest.