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Shrinkit mold2/2/2024 If b=5, the odd time series will consist of the time points 1-5, 11-15, \'85, and the even time series will consist of the time points 6-10, 16-20, \'85. Since time series data is autocorrelated, keeping the data in blocks reduces the effect on the correlation structure of the data. Block length is the length of each "block" for block splitting of the time series (similar to block bootstrap). Takes as input time series data matrix Y (time x variables), block length b and function name(s) fun. X1 and X2 are computed from the first and second halves of each subject's time series, respectively, and Xodd and Xeven are computed from the odd and even blocks of each subject's time series, respectively. Should be applied to each subject's data to create X1, X2, Xodd and Xeven. Returns a shrinkage estimate matrix X_shrink and the shrinkage parameter vector lambda.\ For information on X1, X2, Xodd and Xeven, see description of split_ts.m. For example, X( :, :, i ) could be the VxV correlation matrix or a Vx1 seed connectivity map for subject i. The parameter(s) being estimated can be any quantities of interest. The last dimension of each array is n (the number of subjects). Takes as input X1_grp, X2_grp, Xodd_grp and Xeven_grp, which are four arrays of equal dimension containing subject-level parameter estimates. Apply shrinkIt.m to group arrays to obtain shrinkage estimates of parameters of interest.\ Combine all subjects into group arrays with the last dimension = n (number of subjects).\ģ. Apply split_ts.m to each subject's data to obtain several estimates of the parameters of interest for each subject.\Ģ. The optimal level is determined using the relationship between the within-subject (noise) variance and the between-subject (signal) variance, which are estimated from the data.\ġ. The degree of shrinkage ranges from 0 (no shrinkage) to 1 (complete shrinkage), and is set to the optimal level for each voxel-pair. This code performs empirical Bayes shrinkage of subject-level connectivity matrices towards the group mean. \cf2 Makenzie Miller, \cf0 Laboratory for Neurodevelopmental Research and Imaging, Kennedy Krieger Institute\ Mary Beth Nebel, Laboratory for Neurodevelopmental Research and Imaging, Kennedy Krieger Institute\ NeuroImage.\cf0 \Īmanda Mejia, Department of Biostatistics, Johns Hopkins School of Public Health\ Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI. \cf2 H Shou, A Eloyan, MB Nebel, A Mejia, JJ Pekar, S Mostofsky, B Caffo, MA Lindquist and CM Crainiceanu (2014). Improving reliability of subject-level resting-state fMRI parcellation with shrinkage estimators. \cf0 AF Mejia, MB Nebel, H Shou, CM Crainiceanu, JJ Pekar, S Mostofsky, B Caffo and MA Lindquist.
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