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Abstract #1780

Application of Probabilistic Modeling to Motion Correction of Neonatal Brain Resting-State BOLD Data

Jenna M Schabdach1, Rafael Ceschin1,2, Vince Lee2, Vincent Schmithorst2, and Ashok Panigrahy1,2

1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States, 2Department of Radiology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, United States

Functional connectivity studies commonly use resting-state BOLD MR images to study the neurodevelopment of healthy and at-risk neonates. BOLD images are highly sensitive to motion; post-acquisition motion correction techniques can be applied to BOLD data to compensate for motion. We compare the corrective performance of two motion correction techniques on a cohort of 17 healthy neonates: the traditional correction to the first volume technique and a novel, HMM-based motion correction technique. We evaluate the corrected images in terms of the Power et al. thresholds and show the HMM-based technique can be used to recover neonatal BOLD data corrupted by motion.

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