Artificial Jellyfish Search Optimization Algorithm for Human Brain Functional Parcellation

Human brain function parcellation is an important way to reveal the separation of brain functions. However, most of the existing parcellation methods can not deal with the high dimension and low signal-to-noise ratio of functional magnetic resonance imaging (fMRI) data, so they show the problem of weaker search ability and poorer parcellation results. To alleviate this problem, a human brain functional parcellation method based on artificial jellyfish search optimization (AJSO) algorithm is proposed. Firstly, a functional correlation matrix is calculated based on the preprocessed fMRI data and mapped to a low-dimensional space. Then, a food is encoded as a cluster solution composed of multiple functional cluster centers and the improved AJSO is used to search for better food. The time control mechanism integrated with iterative stagnation is used to control the artificial jellyfish to perform active or passive motion, so as to improve the global search ability. Step size determination strategy guided by fitness is designed for active movement to enhance scientific and targeted search of artificial jellyfish. Finally, according to the principle of minimum distance, the cluster label of each row data in the correlation matrix is obtained and mapped to the corresponding voxels. Experiments on real fMRI data show that compared with other partitioning methods, the new method not only has higher searching ability, but also can obtain better spatial structures and stronger functional consistency. In this study, artificial jellyfish search optimization algorithm is applied to brain functional parcellation, which provides a more effective method of brain functional parcellation.