Pattern recognition methods for classification are leveraged in the field of computational anatomy and neuroimaging showing high reliability and applicability. Body-brain human functions related to the motor-strength features can be discovered by data integration and analysis of 3D brain images, phenotype and behavioural information. This work is focused on the study of feature-based interplay of 3D brain structures with motor-strength information. In particular, this research introduces an ensemble of supervised machine learning approaches for a binary motor-strength classification (strong vs weak) based on 3D brain anatomical features. The proposed approach has been evaluated on 1113 case studies by obtaining well-defined features and reaching the average accuracy of 72% on the test set.