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. 2016 Dec 27;10:659. doi: 10.3389/fnhum.2016.00659

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Copyright © 2016 Wang and Wang.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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(A) Generating weight matrices by the three sparsifying schemes. It consists of two steps. The first step is to calculate the full weight matrix from the preprocessed fMRI data by correlating two fMRI time series. The second step is to apply one of the three different sparsifying schemes on the full weight matrix. (B) The spatial constraint. (C) By pulling together the non-zero elements in each sparsified weight matrix, we could obtain a new vector. Sparsity is defined by the ratio of zero elements in the vector when different thresholds are set to the vector. The results are averaged across subjects.