empirical_edges_corr_weight_loss.zip

In this study we estimated whole-brain functional connectivity characteristics that can predict subsequent weight loss, using the Network-Based Statistic (NBS; Zalesky, Fornito, &amp; Bullmore, 2010) procedure. Here attached the un-threshold edge-wise correlation of functional connectivity and future weight loss. Specifically, the strength of each edge was correlated with the percent weight loss measurement, followed by a Fisher r-to-z-transformation. In order to identify each node an adequate table is additionally attached.<br><br>

本研究采用基于网络的统计量（Network-Based Statistic, NBS; Zalesky, Fornito, & Bullmore, 2010）方法，对可预测后续体重减轻的全脑功能连接特征进行了估算。

本研究附带了功能连接与未来体重减轻的未阈值化逐边相关性数据。具体而言，先将每条连接边的强度与体重下降百分比测量值进行相关性分析，随后执行Fisher r-to-z转换（Fisher r-to-z transformation）。为识别各脑节点，本研究额外附带了一份完备的配套表格。