An important task in unsupervised learning is the detection of communities in networks. Although many community detection techniques have been proposed, there are still some challenge problems, such as unbalanced community detection and the low efficiency. In this paper, we propose a community detection technique combining the sequential signal propagation of the Particle Competition model and the parallel propagation inspired by Self-Orgnizing Map (SOM). As a result, the model presents two salient features: 1) It can detect unbalanced communities. 2) It is much more efficient than the original particle competition model due to the introduction of parallel propagation. Still in this work, we analyze functional brain network by identifying the modules (communities) using the proposed technique. Our results show that there is a strong correlation between brain functions and brain regions and a big decrease of intra-strength measure among communities from the Control Network to the Schizophrenia Network, indicating that the functional correlation of brain regions is weakened in the disease network.