Improving reliability of subject-level resting-state fMRI parcellation with shrinkage estimators.
A recent interest in resting state functional magnetic resonance imaging (rsfMRI) lies in subdividing the human brain into anatomically and functionally distinct regions of interest. For example, brain parcellation is often a necessary step for defining the network nodes used in connectivity studies. While inference has traditionally been performed on group-level data, there is a growing interest in parcellating single subject data. However, this is difficult due to the inherent low signal-to-noise ratio of rsfMRI data, combined with typically short scan lengths. A large number of brain parcellation approaches employ clustering, which begins with a measure of similarity or distance between voxels. The goal of this work is to improve the reproducibility of single-subject parcellation using shrinkage-based estimators of such measures, allowing the noisy subject-specific estimator to "borrow strength" in a principled manner from a larger population of subjects. We present several empirical Bayes shrinkage estimators and outline methods for shrinkage when multiple scans are not available for each subject. We perform shrinkage on raw inter-voxel correlation estimates and use both raw and shrinkage estimates to produce parcellations by performing clustering on the voxels. While we employ a standard spectral clustering approach, our proposed method is agnostic to the choice of clustering method and can be used as a pre-processing step for any clustering algorithm. Using two datasets - a simulated dataset where the true parcellation is known and is subject-specific and a test-retest dataset consisting of two 7-minute resting-state fMRI scans from 20 subjects - we show that parcellations produced from shrinkage correlation estimates have higher reliability and validity than those produced from raw correlation estimates. Application to test-retest data shows that using shrinkage estimators increases the reproducibility of subject-specific parcellations of the motor cortex by up to 30%.
Department of Biostatistics, Johns Hopkins University, USA.
Amanda F
AF
Mejia
Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, USA.
Mary Beth
MB
Nebel
Department of Biostatistics and Epidemiology, University of Pennsylvania, USA.
Haochang
H
Shou
Department of Biostatistics, Johns Hopkins University, USA.
Ciprian M
CM
Crainiceanu
Department of Radiology, Johns Hopkins School of Medicine, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA.
James J
JJ
Pekar
Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins School of Medicine, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, USA.
Stewart
S
Mostofsky
Department of Biostatistics, Johns Hopkins University, USA.
Brian
B
Caffo
Department of Biostatistics, Johns Hopkins University, USA. Electronic address: mlindqui@jhsph.edu.
Martin A
MA
Lindquist
D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D001931:Brain Mapping; D016000:Cluster Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D009044:Motor Cortex / Q000033:anatomy & histology / Q000502:physiology; D009434:Neural Pathways / Q000502:physiology; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology*; D059629:Signal-To-Noise Ratio