Complexity of resting cortical activity predicts neurophysiological responses to theta-burst stimulation but fails to generalize: A rigorous machine-learning approach
Fig 3
Plot of the final decision tree from LMFP Ratios Cross-Cohort Experiment.
If the condition in the top row of the white box is true, the decision path takes the left node, or else, takes the right node. “Training Sample” in the white boxes represents the sample size of the training set before the split. “Class split” represents the class ratio of the training set as follows: [decrease in cortical excitability, increase in cortical excitability]. Yellow box represents a decrease in cortical excitability as assessed by the ratio of the AUC of the 100-131 ms window of the left motor region of LMFP. Similarly, blue box represents an increase in cortical excitability. RQS stands for regression quality score.