Previous studies have shown that functional brain connectivity in the Attention-Deficit/Hyperactivity Disorder (ADHD) shows signs of atypical or delayed development. Here, we investigate the use of a nonlinear brain connectivity estimator, namely Mutual Connectivity Analysis with Local Models (MCA-LM), which estimates nonlinear interdependence of time-series pairs in terms of local cross-predictability. As a reference method, we compare MCA-LM performance with cross-correlation, which has been widely used in the functional MRI (fMRI) literature. Pairwise measures like MCA-LM and cross-correlation provide a high-dimensional representation of brain connectivity profiles and are used as features for disease identification from fMRI data. Therefore, a feature selection step is implemented by using Kendall’s Tau rank correlation coefficient for dimensionality reduction. Finally, a Support Vector Machine (SVM) is used for classifying between subjects with ADHD and healthy controls in a Multi-Voxel Pattern Analysis (MVPA) approach on a subset of 176 subjects from the ADHD- 200 data repository. Using 100 different training/test separations and evaluating a wide range of numbers of selected features, we obtain a mean Area Under receiver operating Curve (AUC) range of [0.65,0.70] and a mean accuracy range of [0.6,0.67] for MCA-LM, which outperforms cross-correlation, which yields a mean AUC range of [0.6,0.64] and a mean accuracy range of [0.57,0.59]. Our results suggest that MCA-LM as a nonlinear measure is better suited at extracting relevant information from fMRI time-series data than the current clinical standard of cross-correlation, and may thus provide valuable contributions to the development of novel imaging biomarkers for ADHD.