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Abstract #1579

Characterizing Motion-Related Contamination of Group Independent Component Analysis Networks in Resting State fMRI

Sam Laxer1,2, Amr Eed1,2, Miranda Bellyou2, Peter Zeman2, Kyle M Gilbert1,2, and Ravi S Menon1,2
1Medical Biophysics, Western University, London, ON, Canada, 2Centre for Functional and Metabolic Mapping, London, ON, Canada

Synopsis

Keywords: fMRI Analysis, fMRI (resting state)

Motivation: Resting-state network quality is a trade-off involving the amount of data used (data quantity) to form the network and confounds such as motion, yet these trade-offs remain largely unexplored.

Goal(s): Establish the relationship between motion and group network spatial specificity across varying data quantities.

Approach: We analyzed group resting-state networks with differing levels of motion contamination, modelled the relationship and identified data quantities required for specific spatial specificity levels.

Results: The relationship between quantity of data and motion level changes based on the desired spatial specificity. We present a methodology to produce calibration curves that reveal this relationship.

Impact: This contribution informs researchers who want to perform power calculations or group-level analyses following dual regression of network components with a methodology to approximate the quantity of data necessary to achieve quality results given an estimate of the motion levels.

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