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. 2019 Mar 7;9:3879. doi: 10.1038/s41598-019-40345-8

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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A flow chart for extracting population information map and conducting out-of-sample prediction for cognitive measurement using information flow in the human brain. (a) Obtaining information flow metrics (F-values) between every pair of regions. For time courses from brain regions 1 and 2, we obtained two directed values F_1→2 (information flow from region 1 to 2) and F_2→1 (information flow from region 2 to 1). (b) Arranging F-values as an information flow matrix. Specifically, F-values obtained from (a) were arranged corresponding to brain regions in an asymmetrical matrix. (c) Obtaining individual information flow matrix. For every subject, we computed the F-values for every pair of time courses. This yielded a 268 × 268 subject-specific asymmetrical F-value matrix and a 268 × 268 asymmetrical p-value matrix (corresponding to the F-value matrix) for each subject. (d) Obtaining average information flow in a group. We first computed the average p-values for edge across subject. We recorded edges with corresponding F-values that had an average p-value less than a threshold (e.g. 0.05). (e) Edge (F-value) selection. We used a two-step feature selection procedure. During the first step, we selected an F-value if its average p-values across subjects were below a threshold (see Methods for details). We then used a leave-one-subject-out cross-validation (LOOCV) analysis to conduct a further feature selection on F-values, and build a predictive model. The training and testing in LOOCV were performed iteratively for n times. (f₁) Due to the large variability of LOOCV, we took the union of the selected features from each LOOCV iteration. (f₂) Using selected F-values, we built a feature weight map of information flow. (g) Out-of-sample prediction. We multiplied the information weight map with F-values from previously unseen subjects, without further model fitting, to predict their cognitive scores. The efficacy and predictive power of information flow in predicting cognitive flexibility was evaluated by correlating the predicted and observed cognitive measurements.