Machine learning can be used to train a model that maps MRI features to clinical phenotype covariates. We present the application of such a framework in the context of MRI case studies. While the presented framework is general in its applicability for individual level analysis, it has particular appeal in the context of case studies where the data can be extraordinarily rare or precious. Specifically, the framework was applied to study the case of an extraordinary long term meditator whose MRI data was acquired over four different time points over a period of fifteen years. Thanks to standardization of image processing and sparsity enhancing regularization methods in machine learning, the case study was performed by including the existing prior data in training the model.