Convolutional neural networks (CNN) have exhibited great success in image classification. The application of CNN to classification of patients with brain disorders and healthy controls is also promising using functional magnetic resonance imaging (fMRI) data. However, the shortage of the number of subjects is a challenge for training CNN. Spatial maps separated from the fMRI data by independent component analysis (ICA) can provide a solution to this problem within an ICA-CNN framework. As such, we propose three strategies for both prior to and post ICA sample augmentation in the ICA-CNN framework. More precisely, we propose to increase the number of samples by performing spatial smoothing and band-pass filtering on the observed fMRI data before ICA, and spatial smoothing on the spatial maps after ICA. We evaluate the proposed methods using 82 resting-state fMRI datasets including 42 Schizophrenia patients and 40 healthy controls. The spatial map of the default mode network is used for classification, and each data augmentation is constrained to have the same numbers of samples for a fair comparison. The results show a 2%15% increase in an average accuracy compared to the existing multiple-model-order method when adopting each of the proposed sample augmentation strategies. The spatial smoothing on the spatial maps is the most accurate among the three proposed methods. When using a combination of the proposed spatial smoothing on the spatial maps with the multiple-model-order method, the average accuracy increases above 90%.