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A recent interest in resting state functional magnetic resonance imaging(rsfMRI) lies in subdividing the human brain into anatomically and functionallydistinct regions of interest. For example, brain parcellation is often used fordefining the network nodes in connectivity studies. While inference hastraditionally been performed on group-level data, there is a growing interestin parcellating single subject data. However, this is difficult due to the lowsignal-to-noise ratio of rsfMRI data, combined with typically short scanlengths. A large number of brain parcellation approaches employ clustering,which begins with a measure of similarity or distance between voxels. The goalof this work is to improve the reproducibility of single-subject parcellationusing shrinkage estimators of such measures, allowing the noisysubject-specific estimator to "borrow strength" in a principled manner from alarger population of subjects. We present several empirical Bayes shrinkageestimators and outline methods for shrinkage when multiple scans are notavailable for each subject. We perform shrinkage on raw intervoxel correlationestimates and use both raw and shrinkage estimates to produce parcellations byperforming clustering on the voxels. Our proposed method is agnostic to thechoice of clustering method and can be used as a pre-processing step for anyclustering algorithm. Using two datasets---a simulated dataset where the trueparcellation is known and is subject-specific and a test-retest datasetconsisting of two 7-minute rsfMRI scans from 20 subjects---we show thatparcellations produced from shrinkage correlation estimates have higherreliability and validity than those produced from raw estimates. Application totest-retest data shows that using shrinkage estimators increases thereproducibility of subject-specific parcellations of the motor cortex by up to30%.

Improving Reliability of Subject-Level Resting-State fMRI Parcellation with Shrinkage Estimators