“…Conventional analytic techniques, such as multiple regression, are not suitable because of marked collinearity in the degree of exposure to specific types of maltreatment at adjacent ages. Instead, we identified the most important cross-validated "predictors" associated with global network measures using random forest regression with conditional inference trees (RFR-CIT) (cforest in R package party), an artificial intelligence machine learning strategy that is highly resistant to collinearity, which we have used in an expanding series of sensitive period studies (Khan et al, 2015;Pechtel, Lyons-Ruth, Anderson, & Teicher, 2014;Schalinski, Breinlinger, Hirt, Teicher, Odenwald, & Rockstroh, 2017;Schalinski, Teicher, Carolus, & Rockstroh, 2018;Schalinski, Teicher, Nischk, Hinderer, Muller, & Rockstroh, 2016;Teicher, et al, 2018;Tomoda, Navalta, Polcari, Sadato, & Teicher, 2009a;Tomoda et al, 2012). Random forest regression predicts outcome by creating a forest of different decision trees with each tree generated from a different subset of the data and constrained in the number of predictors that can be considered at each branch point (Breiman, 2001).…”