Improving reliability of subject-level resting-state fMRI parcellation with shrinkage estimators

• Published in: NeuroImage (Orlando, Fla.) Vol. 112; pp. 14 - 29
• Main Authors: Mejia, Amanda F., Nebel, Mary Beth, Shou, Haochang, Crainiceanu, Ciprian M., Pekar, James J., Mostofsky, Stewart, Caffo, Brian, Lindquist, Martin A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.05.2015; Elsevier Limited
• Subjects: Algorithms; Bayes Theorem; Brain - anatomy & histology; Brain - physiology; Brain Mapping; Cluster Analysis; Clustering; Expected values; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - statistics & numerical data; Magnetic Resonance Imaging - methods; Methods; Models, Statistical; Motor Cortex - anatomy & histology; Motor Cortex - physiology; Neural Pathways - physiology; Noise; Population; Reproducibility of Results; Rest - physiology; Scholarships & fellowships; Signal-To-Noise Ratio; Time series
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2015.02.042

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%. [Display omitted] •We test the value of shrinkage methods to improve resting-state fMRI parcellations.•We use simulated data where the true parcellations are known and scan–rescan data.•We perform shrinkage on connectivity measures and then perform clustering.•Shrinkage results in 30% more reliable subject-level parcellations of rsfMRI data.•Simulations show increased accuracy even where subject-level parcellations differ.