Analytical framework overview. (A) Data preprocessing: The VBM preprocessing pipeline is used to generate MNI-space modulated GMV maps for the training dataset, testing dataset, and clinical dataset. The preprocessed GMV maps of the training dataset with chronological age served as inputs for constructing the brain age estimator. (B) Feature extraction: The large-scale SCNs of the entire training dataset are estimated using spatial ICA approach. The spatial regression analyses with different ICA orders are then applied to estimate network integrity indices of the corresponded SCNs for each individual of the training dataset. (C) Model construction, validation, and evaluation: LASSO regression with nested 10-fold cross-validation scheme is used to construct the proposed brain age estimator from the training dataset with different ICA orders. The MAE and mean coefficient of determination (R²) were used to determine the optimal ICA order. (D) Feature extraction: For the testing and clinical dataset, spatial regression analyses with predifined ICA orders were applied to estimate network integrity indices of the corresponding SCNs. (E) Model generalization and its clinical application: The final large-scale SCN-based brain age estimator (established from training dataset) was used to assess generalizability (to testing dataset) and feasibility of clincal application (to clinical dataset). Abbreviations: AD, Alzheimer’s disease; GMV, gray matter volume; ICA, independent component analysis; LASSO, least absolute shrinkage and selection operator; MDD, Major Depressive Disorder; MNI, Montreal Neurological Institute; SCN, structural covariance network; SCZ, schizophrenia; T1, T1-weighted magnetic resonance imaging; and VBM, voxel-based morphometry.