to estimate the prediction accuracy of the RF model. While classification trees are grown for each bootstrap sample (which is approximately two-thirds of the training data), the OOB error rate is calculated for each classification tree being built. The aggregate of OOB scores on all ‘ntree’ trees (which is the maximum number of trees preset in the model calculation) will provide the ensemble OOB error rate. Thus, the OOB score provides a validation for the RF model. In the model used in the current study, the maximum number of trees ‘ntree’ was set at 500 (default). The optimal number of features analyzed at each node (‘Mtry’) was estimated to be 10 (using the “tuneRF” function) and was used in the classifier algorithm. The final list of variables that significantly contributed for the classification was tabulated, and 3-dimensional connectivity maps of top significant DMN connections within a brain anatomical template were created using custom Matlab scripts.
