Background: :
The brain networks can provide us an effective way to analyze brain
function and brain disease detection. In brain networks, there exist some import neural unit modules,
which contain meaningful biological insights.
Objective::
Therefore, we need to find the optimal neural unit modules effectively and efficiently.
Method::
In this study, we propose a novel algorithm to find community modules of brain networks
by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic
crossover, abbreviated as NIDPSO. The differences between this study and the existing
ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose
not need to predefine and preestimate the number of communities in advance.
Results: :
We generate a neighbor index table to alleviate and eliminate ineffective searches and
design a novel coding by which we can determine the community without computing the distances
amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are
designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO.
Conclusion:
The numerical results performing on several resting-state functional MRI brain networks
demonstrate that NIDPSO outperforms or is comparable with other competing methods in
terms of modularity, coverage and conductance metrics.
Grid Partition Variable Step Alpha Shapes Algorithm
