Dimensionality estimation for optimal detection of functional networks in BOLD fMRI data

► We test multiple PCA-based dimensionality estimators for fMRI discriminant analysis. ► Spatial map reproducibility optimally detects covariance-based network signals. ► Only reproducibility detects dimensionality changes with stronger network signals. ► Other dimension estimates are unstable (eg, classification) or too big (eg, MDL). ► All results are demonstrated in simulations and multiple real fMRI data sets.