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Published on: November 8, 2012
The effect of preprocessing in dynamic functional network connectivity used to classify mild traumatic brain injury
Victor M Vergara1, Andrew R Mayer1,2, Eswar Damaraju1
1The Mind Research Network Albuquerque NM USA.
Introduction:
Dynamic functional network connectivity (dFNC), derived from magnetic resonance imaging (fMRI), is an important technique in the search for biomarkers of brain diseases such as mild traumatic brain injury (mTBI). At the individual level, mTBI can affect cognitive functions and change personality traits. Previous research aimed at detecting significant changes in the dFNC of mTBI subjects. However, one of the main concerns in dFNC analysis is the appropriateness of methods used to correct for subject movement. In this work, we focus on the effect that rearranging movement correction at different points of the processing pipeline has in dFNC analysis utilizing mTBI data.
Methods:
The sample cohort consists of 50 mTBI patients and matched healthy controls. A 5-min resting-state run was completed by each participant. Data were preprocessed using different pipeline alternatives varying with the place where motion-related variance was removed. In all pipelines, group-independent component analysis (gICA) followed by dFNC analysis was performed. Additional tests were performed varying the detection of temporal spikes, the number of gICA components, and the sliding-window size. A linear support vector machine was used to test how each pipeline affects classification accuracy.
Results:
Results suggest that correction for motion variance before spatial smoothing, but leaving correction for spiky time courses after gICA produced the best mean classification performance. The number of gICA components and the sliding-window size were also important in determining classification performance. Variance in spikes correction affected some pipelines more than others with fewer significant differences than the other parameters.
Conclusion:
The sequence of preprocessing steps motion regression, smoothing, gICA, and despiking produced data most suitable for differentiating mTBI from healthy subjects. However, the selection of optimal preprocessing parameters strongly affected the final results.
