a simple and objective method for reproducible resting state network (rsn) detection in fmri一个简单的和客观的方法可再生的静息状态的网络(工匠们)检测在功能磁共振成像.pdfVIP

A Simple and Objective Method for Reproducible Resting State Network (RSN) Detection in fMRI 1 1,2 1,2 Gautam V. Pendse *, David Borsook , Lino Becerra 1 P.A.I.N Group, Imaging and Analysis Group (IMAG), McLean Hospital, Harvard Medical School, Belmont, Massachusetts, United States of America, 2 A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States of America Abstract Spatial Independent Component Analysis (ICA) decomposes the time by space functional MRI (fMRI) matrix into a set of 1-D basis time courses and their associated 3-D spatial maps that are optimized for mutual independence. When applied to resting state fMRI (rsfMRI), ICA produces several spatial independent components (ICs) that seem to have biological relevance - the so-called resting state networks (RSNs). The ICA problem is well posed when the true data generating process follows a linear mixture of ICs model in terms of the identifiability of the mixing matrix. However, the contrast function used for promoting mutual independence in ICA is dependent on the finite amount of observed data and is potentially non-convex with multiple local minima. Hence, each run of ICA could produce potentially different IC estimates even for the same data. One technique to deal with this run-to-run variability of ICA was proposed by [1] in their algorithm RAICAR which allows for the selection of only those ICs that have a high run-to-run reproducibility. We propose an enhancement to the original RAICAR algorithm that enables us to assign reproducibility p -values to each IC and allows for an objective assessment of both within subject and across subjects reproducibility. We call the resu