Connectome-based predictive modeling analysis with CONN toolbox outputs
Setup (before using this code):
- Preprocess your dataset in Conn, including extraction of ROIs from an atlas file (see utils) selected as an "atlas file" within Conn
- Extract head motion as frame-wise displacement in Conn (Setup > Covariates 1st level > Covariate tools > Compute new/derived first-level covariates > Compute 'FD_jenkinson')
- In startup.m file, specify the parent directory of your dataset folders. Example (change to your specific directory): global globalDataDir; globalDataDir='/work/swglab/Aaron/data';
- Create a .mat cell array file with a list of subject names included in your Conn project
- Create a .mat file with a vector of behavioral scores for each subject
Functions:
extract_CONN_atlas_FC.m: extracts functional connectivity matrices (from atlas) and mean FD, then merges across subjects (for input to CPM_internal.m)
CPM_internal.m: runs CPM within a dataset (kfold, leave one out, or use entire dataset to define and save model parameters)
CPM_internal_permute.m: runs permutation test to assess significance
CPM_external.m: test CPM (defined by CPM_internal.m output) in external data
CPM_view_networks.m: view intra- and inter-network contributions to positive and negative edges of a pre-computed CPM
univariate_SchaeferYeo.m: correlate behavior vs. connectivity between all intra- and inter-network pairs in the Schaefer atlas; apply FDR correction