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What Simulation tells us about Head Movement Nuisances in Resting State Functional Network…

"What Simulation tells us about Head Movement Nuisances in Resting State Functional Network Connectivity using group ICA"

Victor Vergara, Ph.D. - Postdoctoral Fellow, the Mind Research Network

Presented: May 26, 2016

ABSTRACT: Nuisances can appreciatively affect functional connectivity analysis. Recently it has been pointed out that methods used to preprocess head motion variance might not fully remove all unwanted effects in the data. Furthermore, the presence of head movement nuisances might decrease our ability to identify significant group differences or significant relationships with continuous variables. Testing these hypotheses has been difficult because most studies of nuisance effects rely on real data where the ground truth is unknown. However, a novel simulation approach that intertwines real data nuisances and artificially generated functional connectivity provides a technique to overcome the lack of a fully controlled simulation of functional connectivity derived from fMRI. The technique has been applied to group independent component analysis (gICA) and the study of resting state functional network connectivity of the brain. The evaluation of simulated results utilizes patient/control classification performance based on linear support vector machines and leave-one-out cross validation. Simulation results helped us understanding a bit better how head nuisances affects group differences and classification performances.

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