For neuroimaging-based brain disease diagnosis, sparse regression models have proved their effectiveness in handling highdimensional data but with a small number of samples. In this paper, we propose a novel framework that utilizes sparse regression models as target-level representation learner and builds a deep convolutional neural network for clinical decision making. Specifically, we first train multiple sparse regression models, each of which has different values of a regularization control parameter, and use the outputs of the trained regression models as target-level representations. Note that sparse regression models trained with different values of a regularization control parameter potentially select different sets of features from the original ones, thereby they have different powers to predict the response values, i.e., a clinical label and clinical scores in our work. We then construct a deep convolutional neural network by taking the target-level representations as input. Our deep network learns to optimally fuse the predicted response variables, i.e., target-level representations, from the same sparse response model(s) and also those from the neighboring sparse response models. To our best knowledge, this is the first work that systematically integrates sparse regression models with deep neural network. In our experiments with ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest classification accuracies in three different tasks of Alzheimer’s disease and mild cognitive impairment identification.