Algorithm 1:

The Training and the validation strategy for connectome-based predictive modeling

Require: global T-scores, functional and structural connectivity, and biological measures
1:	for subject i = 1 : N do
2:	for threshold= 0 : 0.001 : 0.05 do
3:	N-1 subject as training set and the ith as testing set;
4:	All edges that were positively or negatively correlated with global T-scores and p < threshold constitute the positive and negative network;
5:	The positive and negative network strength was calculated by summing the correlation coefficients of each edge in the positive and negative network;
6:	Make predictions using simple or multiple linear regression models;
7:	end for
8:	Use models to predict global T-scores of ith subject;
9:	end for
10:	Apply predictive models trained in the entire N subjects using consensus network to the external validation set.