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

作     者：Mejia, Amanda F. Nebel, Mary Beth Shou, Haochang Crainiceanu, Ciprian M. Pekar, James J. Mostofsky, Stewart Caffo, Brian Lindquist, Martin A. 

作者机构：Johns Hopkins Univ Dept Biostat Baltimore MD 21205 USA Kennedy Krieger Inst Ctr Neurodev & Imaging Res Baltimore MD 21205 USA Johns Hopkins Sch Med Dept Radiol Baltimore MD USA Kennedy Krieger Inst FM Kirby Res Ctr Funct Brain Imaging Baltimore MD USA Johns Hopkins Sch Med Dept Neurol Baltimore MD USA Johns Hopkins Sch Med Dept Psychiat & Behav Sci Baltimore MD USA Univ Penn Dept Biostat & Epidemiol Philadelphia PA 19104 USA 

出 版 物：《NEUROIMAGE》 (神经图像)

年 卷 期：2015年第112卷

页      面：14-29页

核心收录：

学科分类：1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 1010[医学-医学技术（可授医学、理学学位）] 1009[医学-特种医学] 10[医学] 

基　　金：National Science Foundation Graduate Research Fellowship Program [DGE-1232825] NIH from the National Institute of Biomedical Imaging and Bioengineering [R01 EB016061, P41 EB015909] 

主　　题：Algorithms Bayes Theorem Brain/anatomy & histology Brain/physiology Brain Mapping Brain Mapping Cluster Analysis Humans Image Processing, Computer-Assisted/methods Image Processing, Computer-Assisted/statistics & numerical data Magnetic Resonance Imaging/methods Models, Statistical Motor Cortex/anatomy & histology Motor Cortex/physiology Neural Pathways/physiology Reproducibility of Results Rest/physiology Signal-To-Noise Ratio 

摘      要：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